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syntellisolutionsinc · 5 years ago

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Digital Transformation: Not A Choice But A Necessity

In the last decade, the term ‘digital’ has been thrown around more often than ‘google it’. Perhaps that’s the reason why the concept of digital transformation, when introduced to companies, was mildly misunderstood by them.

Digital transformation as a process it’s not only about moving all your company’s data to a cloud – it’s about modifying the way your company functions by incorporating new digital technologies in every area of the business. By doing digital transformation, your company will endure changes on an organizational and cultural level. That includes modification in internal communication and implementation of business models to improve current customer relationships and employee satisfaction.

It’s important to mention that there’s no universal definition of digital transformation. Ultimately, it’s about your company, employees, and customers and finding the most suitable technology for your business. As shown in one survey by TechRepublic, more and more companies are trying to incorporate digital transformation. By the end of 2018, 70% of the survey respondents were already doing digital transformation or planning to do. Plus, in comparison to the data from 2016 and 2017, there’s a 53% increase in digital transformation budgets overall.

It’s no surprise that companies are going digital, but what are the common benefits of it, and why digital transformation has become a determining factor for success?

Here are some of the most important benefits of digital transformation for businesses:

1. Better Customer Experience And Engagement

Increasing user satisfaction it’s in the heart of any digital transformation. Almost 70% of world leaders have noticed a significant increase in user engagement and satisfaction from digital transformation. It helps companies in many areas regarding user engagement, such as:

More personalized customer journeys

Faster and more comprehensive user support service

A more agile approach to solving user’s problems

Maintain real-time communication by using an omnichannel strategy

Maintaining a positive brand image positive is any company’s primary goal and with digital transformation, that goal it’s easily achievable.

2. Creates Digital Culture

Digital transformation is all about how the company will apply and lead the employees through the process. Creating a culture of learning about digital tools will improve employees’ productivity, performance, and enjoyment and will increase their trust in the company’s vision. Your goal as a business is to have digital-savvy employees and although that may take some time, the end results are worth it.

3. Brings Different Departments Together

Contrary to popular belief, digital transformation can unite different departments more, not divide them. All it takes is finding the right communication tool and enough encouragement for everyone to try it – not to be averse about it! Also, to create a digital intelligent working atmosphere, any company needs strong leadership.

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4. Data-Driven Decisions

Digital transformation will open the door to an extensive amount of data. Use that data to come up with better business decisions that will increase customer retention. There are two types of data: structured or personal customer information and unstructured data, the social media data, all of which you can use to create better customer journeys for your users.

5. Builds The ‘Always-Learning’ Mindset

Life in the fast-paced society taught us that if we can’t adapt we’ll fail. That’s what digital transformation does too – it slowly builds up the mindset of a learner. Plus, businesses should always aim to be better than their competition and constantly to look for new ways to make use of digital tools.

To sum things up, there’s no if when it comes to digital transformation – right now, it’s a matter of when. If you can’t do it alone, Syntelli Solutions can always help you. With our comprehensive services and agile approach, we can help you start the digital transformation today. Our team of professional data scientists is always available to you, so feel free to contact us.

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8 Performance Optimization Techniques Using Spark

Due to its fast, easy-to-use capabilities, Apache Spark helps to Enterprises process data faster, solving complex data problems quickly.

We all know that during the development of any program, taking care of the performance is equally important. A Spark job can be optimized by many techniques so let’s dig deeper into those techniques one by one. Apache Spark optimization helps with in-memory data computations. The bottleneck for these spark optimization computations can be CPU, memory or any resource in the cluster.

1. Serialization

Serialization plays an important role in the performance for any distributed application. By default, Spark uses Java serializer.

Spark can also use another serializer called ‘Kryo’ serializer for better performance.

Kryo serializer is in compact binary format and offers processing 10x faster than Java serializer.

To set the serializer properties:

conf.set(“spark.serializer”, “org.apache.spark.serializer.KryoSerializer”)

Code:

val conf = new SparkConf().setMaster(…).setAppName(…)

conf.registerKryoClasses(Array(classOf[MyClass1], classOf[MyClass2]))

val sc = new SparkContext(conf)

Serialization plays an important role in the performance of any distributed application and we know that by default Spark uses the Java serializer on the JVM platform. Instead of Java serializer, Spark can also use another serializer called Kryo. The Kryo serializer gives better performance as compared to the Java serializer.

Kryo serializer is in a compact binary format and offers approximately 10 times faster speed as compared to the Java Serializer. To set the Kryo serializer as part of a Spark job, we need to set a configuration property, which is org.apache.spark.serializer.KryoSerializer.

2. API selection

Spark introduced three types of API to work upon – RDD, DataFrame, DataSet

RDD is used for low level operation with less optimization

DataFrame is best choice in most cases due to its catalyst optimizer and low garbage collection (GC) overhead.

Dataset is highly type safe and use encoders. It uses Tungsten for serialization in binary format.

We know that Spark comes with 3 types of API to work upon -RDD, DataFrame and DataSet.

RDD is used for low-level operations and has less optimization techniques.

DataFrame is the best choice in most cases because DataFrame uses the catalyst optimizer which creates a query plan resulting in better performance. DataFrame also generates low labor garbage collection overhead.

DataSets are highly type safe and use the encoder as part of their serialization. It also uses Tungsten for the serializer in binary format.

Code:

val df = spark.read.json(“examples/src/main/resources/people.json”)

case class Person(name: String, age: Long)

// Encoders are created for case classes

val caseClassDS = Seq(Person(“Andy”, 32)).toDS()

// Encoders for most common types are automatically provided by importing spark.implicits._

val primitiveDS = Seq(1, 2, 3).toDS()

primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)

// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name

val path = “examples/src/main/resources/people.json”

val peopleDS = spark.read.json(path).as[Person]

3. Advance Variable

Broadcasting plays an important role while tuning Spark jobs.

Broadcast variable will make small datasets available on nodes locally.

When you have one dataset which is smaller than other dataset, Broadcast join is highly recommended.

To use the Broadcast join: (df1. join(broadcast(df2)))

Spark comes with 2 types of advanced variables – Broadcast and Accumulator.

Broadcasting plays an important role while tuning your spark job. Broadcast variable will make your small data set available on each node, and that node and data will be treated locally for the process.

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Suppose you have a situation where one data set is very small and another data set is quite large, and you want to perform the join operation between these two. In that case, we should go for the broadcast join so that the small data set can fit into your broadcast variable. The syntax to use the broadcast variable is df1.join(broadcast(df2)). Here we have a second dataframe that is very small and we are keeping this data frame as a broadcast variable.

Code:

val broadcastVar = sc.broadcast(Array(1, 2, 3))

broadcastVar.value

res0: Array[Int] = Array(1, 2, 3)

val accum = sc.longAccumulator(“My Accumulator”)

sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum.add(x))

accum.value

res2: Long = 10

4. Cache and Persist

Spark provides its own caching mechanisms like persist() and cache().

cache() and persist() will store the dataset in memory.

When you have a small dataset which needs be used multiple times in your program, we cache that dataset.

Cache() – Always in Memory

Persist() – Memory and disks

Spark provides its own caching mechanism like Persist and Caching. Persist and Cache mechanisms will store the data set into the memory whenever there is requirement, where you have a small data set and that data set is being used multiple times in your program. If we apply RDD.Cache() it will always store the data in memory, and if we apply RDD.Persist() then some part of data can be stored into the memory some can be stored on the disk.

5. ByKey Operation

Shuffles are heavy operation which consume a lot of memory.

While coding in Spark, the user should always try to avoid shuffle operation.

High shuffling may give rise to an OutOfMemory Error; To avoid such an error, the user can increase the level of parallelism.

Use reduceByKey instead of groupByKey.

Partition the data correctly.

As we know during our transformation of Spark we have many ByKey operations. ByKey operations generate lot of shuffle. Shuffles are heavy operation because they consume a lot of memory. While coding in Spark, a user should always try to avoid any shuffle operation because the shuffle operation will degrade the performance. If there is high shuffling then a user can get the error out of memory. Inthis case, to avoid that error, a user should increase the level of parallelism. Instead of groupBy, a user should go for the reduceByKey because groupByKey creates a lot of shuffling which hampers the performance, while reduceByKey does not shuffle the data as much. Therefore, reduceByKey is faster as compared to groupByKey. Whenever any ByKey operation is used, the user should partition the data correctly.

6. File Format selection

Spark supports many formats, such as CSV, JSON, XML, PARQUET, ORC, AVRO, etc.

Spark jobs can be optimized by choosing the parquet file with snappy compression which gives the high performance and best analysis.

Parquet file is native to Spark which carries the metadata along with its footer.

Spark comes with many file formats like CSV, JSON, XML, PARQUET, ORC, AVRO and more. A Spark job can be optimized by choosing the parquet file with snappy compression. Parquet file is native to Spark which carry the metadata along with its footer as we know parquet file is native to spark which is into the binary format and along with the data it also carry the footer it’s also carries the metadata and its footer so whenever you create any parquet file, you will see .metadata file on the same directory along with the data file.

Code:

val peopleDF = spark.read.json(“examples/src/main/resources/people.json”)

peopleDF.write.parquet(“people.parquet”)

val parquetFileDF = spark.read.parquet(“people.parquet”)

val usersDF = spark.read.format(“avro”).load(“examples/src/main/resources/users.avro”)

usersDF.select(“name”, “favorite_color”).write.format(“avro”).save(“namesAndFavColors.avro”)

7. Garbage Collection Tuning

JVM garbage collection can be a problem when you have large collection of unused objects.

The first step in GC tuning is to collect statistics by choosing – verbose while submitting spark jobs.

In an ideal situation we try to keep GC overheads < 10% of heap memory.

As we know underneath our Spark job is running on the JVM platform so JVM garbage collection can be a problematic when you have a large collection of an unused object so the first step in tuning of garbage collection is to collect statics by choosing the option in your Spark submit verbose. Generally, in an ideal situation we should keep our garbage collection memory less than 10% of heap memory.

8. Level of Parallelism

Parallelism plays a very important role while tuning spark jobs.

Every partition ~ task requires a single core for processing.

There are two ways to maintain the parallelism:

Repartition: Gives equal number of partitions with high shuffling

Coalesce: Generally reduces the number of partitions with less shuffling.

In any distributed environment parallelism plays very important role while tuning your Spark job. Whenever a Spark job is submitted, it creates the desk that will contain stages, and the tasks depend upon partition so every partition or task requires a single core of the system for processing. There are two ways to maintain the parallelism – Repartition and Coalesce. Whenever you apply the Repartition method it gives you equal number of partitions but it will shuffle a lot so it is not advisable to go for Repartition when you want to lash all the data. Coalesce will generally reduce the number of partitions and creates less shuffling of data.

These factors for spark optimization, if properly used, can –

Eliminate the long-running job process

Correction execution engine

Improve performance time by managing resources

For more information and if you have any additional questions, please feel free to reach out to our Spark experts at Syntelli.

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How Predictive Analytics in Finance Can Accelerate Data-Driven Enterprise Transformation

Small and large organizations will have to learn to operate in new ways, even if the economy rebounds quickly. Consumer confidence will likely be low after COVID-19 and financial services companies must learn to react in real-time to rebuild relationships and increase investments.

Predictive analytics in financial services is a growing area of interest with constantly emerging technologies. It can make a huge difference in customer experience and your organization’s digital transformation, thanks to its ability to help you make smarter decisions and plan for the future. Even during unprecedented times, predictive analytics’ ability to deal with ever-changing circumstances and new data can be the key to success for organizations across industries.

What Is Predictive Analytics?

As its name implies, predictive analytics is the science of predicting future events using existing data. It uses big data, machine learning, and other data analytics tools to forecast industry characteristics based on current trends and historical data.

Predictive analytics is related to prescriptive analytics, which uses artificial intelligence and big data to tell businesses what profit-maximizing choices to make. In contrast, predictive analytics does not make normative claims or tell the financial services and insurance industry what to do; instead, it makes descriptive claims about industry outlooks.

The Importance of Data Management and Analytics

Finding valuable insights from the data your company gathers takes time and effort. Data governance tools, including artificial intelligence and data lakes, can make your massive amounts of data more manageable.

For example, data lakes allow customer satisfaction surveys to be stored and analyzed in their raw form, reducing the need to manually process or simplify their content. Cloud storage eliminates the need for expensive, on-site servers while still processing your data securely and allowing you to access it quickly. Data processing is faster than ever thanks to technological improvements in computer processing power and artificial intelligence.

Once a general data analytics system is set up, data scientists can continue to improve upon its accuracy, add new features, and update data. Data scientists are taught to recognize how ‘noisy’ data can be misinterpreted and take steps to avoid false conclusions about potential future events.

Putting Customers First

Big data analytics for financial services can benefit you by providing a better understanding of your current customer base. Even if your organization values customer service, there simply aren’t enough hours in the day to reach out to and interview customers about their needs and wants. Predictive analytics in financial services can provide surprising answers to unasked questions and help you consider the whole customer, regardless of which services they’re currently using.

As customers mature and their families grow, their needs change as well. This is especially true in the financial services industry, where customer needs are shaped by family size, income, education levels, and existing assets. A young professional couple preparing to have children will likely develop an interest in college savings accounts, life insurance, and a mortgage.

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In addition to influencing the types of financial services offered, predictive analytics can improve your ability to serve individual customers. When a customer fills out an application for a loan or other service, predictive analytics can help assess the likelihood your customer will repay the loan. A high-quality predictive analytics system can guide your business to offer different services, like secured loans or lower loan amounts, to customers who don’t qualify for the service they originally applied for.

Better Online Banking

Predictive analytics can show areas where consumer interest is likely to spike, giving managers enough advance notice to shore up online infrastructure in those areas. If internal metrics and external market factors indicate that many people are likely to become interested in buying homes, marketing teams can update the website to promote mortgage loans to existing customers and IT staff can invest in making online mortgage applications easier.

Data-driven analytics can also show gaps in the system that allow fraud and abuse. Although fraud usually is analyzed as a past pattern and is not fully covered by predictive analytics, predictive analytics can play a role in advising IT staff about which online services should be secured against potential scammers.

5 Ways Prescriptive Analytics Helps Deliver Better Financial Services

Although predictive analytics in banking is helpful and essential, prescriptive analytics takes the data a step further. Predictive analytics shows companies the raw results of their potential actions, while prescriptive analytics shows companies which option is the best. Read More

Predicting Market Changes

The ability to predict future revenue is another growing use of data analytics in finance. With a combination of both internal and external data, your organization can predict revenue growth from specific sectors of the market.

The ability to predict market changes is especially important for growing companies. Even profitable ventures should be examined with predictive analytics to create demand projections, especially with the uncertainties caused by COVID-19. Minor changes to growth plans can increase or decrease your return on investment, with serious implications for investor confidence in the future.

Predictive analytics can also help determine which marketing campaigns and strategies are likely to be effective. If there’s an up-and-coming neighborhood in your service area, intel from predictive analytics could inform a smart marketing strategy targeting this new market.

Rising to Meet Future Challenges

Predictive analytics in financial services are constantly improving thanks to new technologies and abundant interest in science. Your organization can use customized data solutions to minimize the guesswork involved in meeting the needs of your existing customers and reach new ones effectively.

Syntelli Solutions is a leader in providing cutting-edge data science services, including predictive analytics, prescriptive analytics, and data management services. We provide services to a range of industries, but we have special knowledge of the financial and insurance industries.

No matter how large or small your customer base or service area is, custom-tailored data solutions can help you serve customers better and make smarter decisions. Contact us today to learn more about how we can unlock your data’s potential.

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syntellisolutionsinc · 5 years ago

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7 Reasons to Start Using Customer Intelligence in Your Healthcare Organization

When it comes to data, more is not necessarily better, and organizations must carefully craft their data analytics strategies to make the most of the available information. Data on patient backgrounds are just as important as information about their current health and experiences inside the hospital or clinic.

Most professionals agree that major decisions can be better made with a full array of customer intelligence information, but even minor, day-to-day judgments can be improved with the right data. Here are seven reasons why organizations should take steps to start using more customer intelligence and data analytics tools.

1. Understanding Customer Profiles

Not all healthcare industry organizations serve the same demographic. Although healthcare providers can reasonably procure an imprecise judgment on the demographics they serve based on the location and services of the practice, factors like family size, occupation, and education level are difficult to predict.

All of a patient’s demographic factors influence how, when, and why they seek out services. A patient who faces financial obstacles to getting care will generally seek preventive care less often than they should, and someone who dropped out of high school will usually know less about health and nutrition than someone who completed an advanced degree.

Understanding customer profiles is the first step in leveraging customer intelligence for broader proactive purposes. Creating better services for patients is impossible without first knowing their needs, attitudes, and obstacles to care, factors often not discussed honestly over the course of an appointment. Data can reveal trends that affect appointments for both healthy and ill patients.

2. Training Staff Better

Customer intelligence analytics can reveal a lot about patient needs and wants, both medically and holistically. Data collected from patient visits can be analyzed for general trends in patient engagement and experiences.

For example, patient data may reveal that physicians do not always prescribe the appropriate follow-up tests or that certain medical conditions are being diagnosed at an unusually high rate. Organizational leadership can look at any unusual trends and remedy common mistakes by providing updated training and reminders for all staff.

3. Creating Better Marketing Campaigns

Customer intelligence helps you understand patient preferences and satisfaction, which in turn can help you shape marketing campaigns. If you’ve found that your staff’s professionalism and high standard of care have struck a chord with senior citizens, it may be helpful to launch a targeted campaign highlighting your patient-centric services.

Knowing the details of your existing customer demographic can also help you discover where you might recruit new patients. Knowing where your patients live and don’t live can inform appropriate marketing strategies.

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A detailed customer intelligence platform can give your marketing team access to the information it needs to craft more effective and relevant campaigns. With real-time updates to customer intelligence and marketing campaign results, you can make adjustments on the fly to maximize the return on investment for your campaign.

4. Growing Responsibly

Customer intelligence can reveal how and where to expand, creating more opportunities to serve your existing client base. A clinic that wants to add services or specialists would want to analyze their existing customer base for services that patients appreciate and avoid oversaturating the market or creating positions that are nearly impossible to fill.

This is especially important for organizations seeking to invest significant money in an additional building or wing. Without a clear picture of patient needs, concerns, and satisfaction, there’s no way of knowing whether you’ll make good on your investment.

Growing also requires consideration of potential customers that you haven’t yet accounted for. Subfields of future-oriented business intelligence, including predictive analytics and prescriptive analytics, play an even larger role in helping leaders decide the best investments for the future. Customer intelligence completes the picture by showing organizational leaders’ current strengths and weaknesses, allowing for smarter decision-making.

5. Increasing Communication with Customers

It’s hard to rely on patients to stay up on their follow-up and preventive care, even with highly-educated and wealthy patients. Customer intelligence can be used to craft and send personalized emails and text messages designed to remind the patient about necessary appointments.

Healthcare facilities can also send relevant general health information to entire families, even if the stated goal of the message isn’t to bring patients back in for another visit. Staying in contact with past clients helps improve patient intimacy and trust so your practice can reap the benefits of patient loyalty.

6. Fix Retention Problems

Client retention is an ongoing issue, especially for specialized clinics or family practice centers in areas with high market saturation. In order to address and fix client retention, organizations need an accurate picture of what factors are contributing to patient turnover.

For example, an effective customer intelligence system could collect information about patient check-in and appointment times, and discover that patients who have to wait more than 30 minutes to be seen have a much higher chance of not coming back for their follow-up or other future appointments. Knowing what your clients will tolerate can help you increase patient satisfaction.

Why is Digital Transformation Important for Healthcare?

As with the other major transformations, Healthcare organizations have been trailing from other industries so recently we sat down and discussed with Dr. Pillay, Chief Innovation Officer for UAB Health System, who has been at the forefront of Healthcare Innovation for the last 30 years to discover some of the insights as to why this is the case. Read More

7. Improve Patient Outcomes

Hospitals in particular must grapple with patient readmission rates, mortality rates, and other key metrics. When these rates are higher than average, customer intelligence can help you discern what factors are contributing to the problem.

In some cases, factors like income could be contributing to patients’ failure to adhere to follow-up plans, but sometimes there are factors that are within the hospital’s control. Customer intelligence analytics, part of the broader digital transformation in healthcare, can help hospitals connect the dots to improve their relationships with patients.

Harnessing the Best Technology and Data Science Methods

Achieving these goals isn’t possible with just a spreadsheet and some graphs. Unlocking the full power of your customer intelligence requires a comprehensive approach to how your data is collected, stored, and viewed.

Syntelli Solutions provides specialized data analytics and data science services for healthcare facilities employing data-driven approaches. We provide services ranging from data management to complex artificial intelligence, all with an eye toward data privacy and processing speed.

Our team works to understand your organization’s needs and craft the solutions you need to improve patient outcomes, recruitment, and retention. Contact us today to learn more about our multifaceted approaches to customer intelligence.

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The Future of Analytics in Higher Education with Artificial Intelligence

The future is sooner than you would have expected – it is now. Contrary to concerns about Artificial Intelligence (AI) in everyday activities, ethical AI can enhance a balanced, accessible, scalable, and inclusive learning system. With the increasingly limited resources and restrictions but high expectations on student outcomes for higher institutions, any institution looking to thrive in this current age will have these factors to consider.

In light of the recent happenings, the only sure thing is: Things Will Change.

Sometime in March 2020, most states in the USA implemented remote work and school. The concept of working and schooling from home was quite strange and frustrating. The whole work-day dynamics changed so drastically without any warning or form of preparation. Many understandably struggled to balance family and work, and some scrambled to acquire new skills to make them more relevant in the digital world. It is impressive, though, that many companies stepped up to provide free learning resources ranging from Music to Math and virtual Zoo and Museum tours.

A plethora of new changes, involving many parts at a speed we did not envisage or prepare for, re-emphasized the changing mindset. This change ignited a level of adaptation we wouldn’t have adopted willfully, and people began to do things they didn’t envision they could do in light-years.

People are starting to solve problems and demand solutions or services differently.

Businesses with no online presence went online, delivery services and curb pick-ups sprang up, and non-essential procedures or services got canceled.

Age-old policies changed in minutes over video conferences which made the decision-making process very informal.

Yet, a new saying was birthed – “The New Normal”.

[Case Study] How the University of Alabama at Bimingham Amped Up Organization-Wide Performance with Data Analysis

Read how the University of Alabama at Birmingham was able to leverage Tableau, a data visualization & analysis tool, to enhance performance of each department and program. Learn More

The New Normal for Education

1. Content

Just as we know big data for businesses, we also have humongous data for learning with an increasing number of useful resources for students. There has been an increase in the creation of smart content. Learning materials and resources are condensed in a more precise and digitized format. This makes learning much faster and easily accessible.

2. Accessibility

With a more concise and digitized learning format, students will have the flexibility of choosing to work remotely or not. Even though this is already in practice, it will take on a larger scale and extend to many other programs. This will gradually reduce the cost of education, remove global boundaries, and make learning possible from anywhere.

3. Delivery

With more people opting for remote learning, the mode of delivery will gradually change. The medium to teach will shift from the sole traditional class delivery or operations to creating a hybrid of integrated AI with augmented reality and the conventional classroom.

Learning will take on a more adaptive approach and become even more personalized. It will then become even more challenging to sustain a manual method to measure engagement, pace, and effectiveness of the teacher. Different measures to measure this for early intervention will become paramount, which will further require more use of technology.

Online learning platforms provide instant learning work emphasizing on weak points for improvement and recommending other topics to learn. Educators can see specific struggle areas and the number of attempts, contrary to the traditional learning format. It will be a lot of work for teachers to provide personalized learning track and almost impossible to trace specific struggle areas for every student at once.

4. Automation

Machines cannot replace the role of an educator because it goes beyond helping students garner academic knowledge or handling administrative tasks like grading or marking attendance. These tasks can be automated to enable educators to gain deeper insights into students’ behavior and provide detailed and individualized feedback. Everything is done in a fraction of time with more accuracy, thus providing more time for mentorship and guidance.

The Role of Institutions

These are critical areas institutions need to look into to enable them to compete in the new era of AI-enhanced higher education.

1. Data Mining/Engineering

Many higher institutions already have processes in place to handle data processing. The difference, however, is there will be more data, mostly unstructured, that will require near real-time processing and accessibility to more users.

Remember, the data will no longer be in a standard format that can be easily generalized. Institutions will require a more creative approach and a tweak to existing data processing pipelines to accommodate these rapid changes.

2. Realtime Monitoring

Monitoring academic, financial, and operational outcomes will even become more critical. The ability to successfully pull this off is dependent on the data mining process. With a gradual blur on education boundary, the competition will be more fierce, so real-time monitoring is essential to early intervention for improved outcomes and to stay ahead of the curve.

“Syntelli helped us deliver Tableau dashboards which had some complex inner workings that were ultimately well-received by our administrators and deans. The Syntelli team were easy to work with and their analysts overcame some challenging problems in a very short period of time. I really appreciated how Syntelli always came prepared with alternative solutions to difficult or seemingly impossible user requirements.”

Eva W. Lewis

Vice Provost, Institutional Effectiveness & Academic Planning, University of Alabama at Birmingham

3. Predictive/Prescriptive Analytics

Using all the metrics monitored and data gathered,

Identify key indicators of student performance.

Measure the effectiveness of teaching resources.

Evaluate the quality of education

Institutions can deploy predictive and prescriptive analytics to understand factors they have control over that can yield desired outcomes. These are also methods to ensure early intervention for remedial actions to steer the students in the right direction. Waiting to see the results of tests and exams will be of no help and could be damaging to the school’s reputation.

The role of Artificial Intelligence is very significant in education advancement, and it does not imply that institutions should rid themselves of real and in-person opportunities either for academics or non-academics.

A well-deployed AI system should serve as a level ground for all students and provide more time for more meaningful engagements.

Syntelli Solutions is continually doing research and development to help institutions make the most of the resources available to them. Contact us to learn more.

Moyosore Lawal, Sr Analytics Associate

Providing solutions that enhance business competitiveness and enable companies achieve their goals leveraging on data is what Moyo stands for. She has worked with data in a number of ways and has a well-grounded understanding of the data lifecycle.

As a Data Scientist/Engineer, she has managed several successful projects building and implementing predictive models. She earned her M.S. in Data Science and Business Analytics degree from the University of North Carolina at Charlotte.

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How Big Data Analytics Helps Healthcare Providers Shift to Digital Value-Based Care

Why a Shift?

Some healthcare providers aim to shift their revenue generation model from a pay-per-service model to a results-oriented mode of operation.

The shift to value-based care positions the healthcare industry as a service industry; healthcare providers will become more concerned with improving communication and care while lowering costs.

Value-Based Care

Value-based care refers to healthcare providers receiving financial rewards for patient outcomes rather than being incentivized to run extra tests or provide unnecessary services.

Value-based care also leads to personalization, as treatments must be prescribed carefully, based on the specifics of the situation. The need for value-based care has cemented the future of big data in healthcare, as data analytics in healthcare can inform a personalized approach to patient care.

Big Data and Value-Based Care

Healthcare data analytics makes use of the patient data available in electronic health records (EHR), patient billing statements, and clinical notes. The aggregation of these data sources makes it possible for physicians to effectively diagnose and treat illnesses they had never personally encountered before.

A well-informed healthcare analytics strategy is important in finding valuable insights within large piles of data. Data should be properly managed and analyzed in order to realize the potential of the benefits of big data in healthcare. Used properly, AI transforms healthcare for the better.

Big Data Analytics

Big data analytics uses machine learning and AI to catch patterns that physicians can’t. Descriptive data analytics make statements about the current correlations and patterns, while prescriptive analytics in healthcare can extrapolate from that to offer insights for treating specific ailments.

What Healthcare Providers Do with Big Data

Healthcare analytics trends show that healthcare providers are beginning to jump on the big data bandwagon that many other industries are already riding. Instead of using big data to run personalized advertisements, healthcare applications of big data can help physicians provide better patient care.

Data in use can provide diagnostic assistance, clinical decision support, patient monitoring and strategic intervention, and enhance patient to physician communication.

Diagnostic Assistance

The healthcare analytics advantages are even present when physicians are working exclusively with their clinical notes. With the advent of natural language processing (NLP) capabilities, machine learning can tease out relevant patterns from the EHR that result in a quicker and more reliable diagnosis.

An evidence-based healthcare system using big data can make use of improved data mining algorithms to help with diagnosing and dosing patients.

Clinical Decision Support (CDS)

Predictive algorithms provide data-based insights for treatments. This makes it easier for physicians to factor in all the variables relevant to a patient’s health to provide a personalized treatment plan.

Clinical decision support systems like clinical guidelines can provide a roadmap to help physicians decide what treatment steps to take in everyday situations by pointing out the most important data points to focus on.

Monitoring Conditions for Prevention and Intervention

Wearable devices make it possible for healthcare personnel to receive real-time data about the patient’s condition, which can make outpatient care more successful.

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Wearable devices can be used to gather more data on diseases and conditions or to identify hospital infections and patient health downturns more quickly. They have even been known to improve patient engagement, making treatments more effective.

Communication

Communication is key in the healthcare industry, and big data, with the help of NLP, makes it easier to analyze feedback and harvest insights. Increased communication enhances the feeling of patient intimacy, which can promote patient loyalty and improve the healthcare facility in the long-run.

Apps have made communication between patients and their healthcare providers more convenient. Syntelli Solutions can even analyze patient voices to turn call centers into points of valuable data generation.

Challenges of Big Data in Healthcare

The benefits of big data in healthcare are obvious, but applying data best practices to the healthcare industry is not without challenges. Most hospitals use EHR systems, but these systems often can’t communicate between hospitals, making it difficult to use big data to help diagnose very rare conditions.

Even within hospitals, it can be difficult to extract relevant information from clinical notes, as healthcare professionals don’t always use consistent language to describe their patients. This makes NLP less effective and harder to use.

Data Storage

Accumulating massive amounts of healthcare data can help your facility make the switch to digital value-based care, but it can also become unwieldy to store, organize, and manage. Healthcare providers must be particularly careful about data security to remain HIPAA compliant.

Healthcare providers can expect more data regulations in the future; working with Syntelli Solutions can help providers stay on top of regulations and properly manage their data.

Data storage in the Cloud risks compliance issues while attempting to store data in your own facility can be very expensive to scale. Master Data Management (MDM) by Syntelli Solutions creates a single location for all of a company’s data, making it more affordable and accurate to leverage healthcare data.

Our MDM solution provides patient intelligence by matching patient records, improves clinical information management, and enforces information privacy so you can focus on patient care.

How to Use Big Data in Your Healthcare Facility

Syntelli Solutions can help your healthcare facility get started leveraging healthcare data to its full potential. From marketing to fraud analytics, our solutions can help your business reduce waste, grow market share, and lower the costs to your patients.

Whether you’re searching for a better way to store, manage, and analyze your data or you’re new to collecting medical data, Syntelli Solutions can be a great partner to help you reach the next level of patient care.

Contact us today to learn more about how Syntelli Solutions will be able to help your healthcare facility make the shift to digital value-based care.

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Utilizing Big Data to Improve Patient Outcomes in Home Healthcare

The healthcare industry can benefit from using big data to economize, target marketing, and better understand the needs of home healthcare patients.

Home Healthcare

Home healthcare is characterized by care providers tending to patient health at a home rather than a hospital or skilled nursing facility. The individualized patient care approaches illness or injury in an often less expensive, more comfortable, and longer-term way.

Although home healthcare usually refers to a single patient per location, standardized data collection should still be practiced to make data analysis and the consequent better-informed, value-based care possible.

Big Data in Healthcare

Large data sets can be leveraged with big data analytics to reveal trends, patterns, and correlations.

Big data is broadly used by businesses to target advertising dollars and inform marketing strategies, and medical data is used by many in the healthcare industry to improve health outcomes.

Clinical systems in healthcare are responsible for data management; health data is most often documented in the Electronic Health Record (EHR). When healthcare data is correctly standardized and stored, researchers can come to valuable insights and physicians can inform daily actions.

Using Data to Determine Medical Best Practices

Its current uses among healthcare providers clearly demonstrate how AI transforms healthcare. Patient data can be standardized and aggregate to inform evidence-based treatment plans and preventative care using predictive analytics.

Specifically, clinical data is being collected from inhalers so machine learning can use patient experience to help physicians and patients better track and manage asthma.

Big Data in Home Healthcare

Algorithms can be created to mine clinical notes and patient health information for patterns in symptoms that might predict patient outcomes or developing ailments.

Already, big data helps tackle accidental death related to opioid use, an application especially important for a field that aims to allow more independence in injury and illness treatment.

Nursing analytics can also be used to create a clinical decision support tool for the caregivers of patients turning to home care. Predictive analytics could be used to identify high-risk patients who require immediate care based on clinical notes that home care nurses take.

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Finally, machine learning may be able to find cures by leveraging existing data; by regressing patient outcomes with symptoms and treatments, physicians may be able to discover or pinpoint efficacious treatments.

Data Challenges in Healthcare

The healthcare industry has been slow to adapt to changing technology regarding data analytics. This is in part due to the inherent challenges associated with collecting and mining healthcare data.

Even when patient data is digitized, which hasn’t been widespread in hospitals until recently, the lack of standardization makes data mining a difficult process. This also contributes to the difficulty of aggregating data sets, as hospitals often have trouble sharing data sets that aren’t quite compatible.

However, there is reason to be optimistic about the near-future of nursing informatics, as EHR use caught on rapidly in hospitals once the benefits of big data in healthcare were made more readily apparent.

People commonly cite privacy and security concerns when discussing the downfall of big data in healthcare and they are often surprised to learn that big data can play an important role in reducing healthcare fraud and waste. Identifying outliers in the data can catch mistakes and fraud and end up saving insurers and patients money.

Existing and potential regulations surrounding healthcare data are often reason to pause when collecting and aggregating patient information. Syntelli Solutions provides data management support and security to keep your company on top of the regulations.

What Makes Good Data

Not all data can have equally effective applications. Health data is notoriously hard to collect, as many clinical notes rely on unmeasurable observations and hospital-specific scales. Promoting standardization of patient experience documentation, both within facilities and between hospitals, will likely improve the utility of the available data.

Terminology is another aspect of clinical notes that must be standardized to create useful datasets. Going forward, nurses should be taught to document standard terms that fit each observation.

Standardization provides an additional challenge with home healthcare professionals, as these physicians and nurses tend to be relatively independent of the rest of the healthcare industry. However, this is an obstacle that can be surmounted to allow for better leveraging of AI in healthcare.

Keep in mind that data is only as good as the data analytics. Big data can be misleading if improperly interpreted, so it’s important to seek professional help when analyzing your data. Ideally, look for an expert trained in data analytics for the healthcare field; an informatics nurse or data company that works with healthcare data, like Syntelli Solutions, can help you make the most of your data.

About Syntelli Solutions

Syntelli Solutions can help your home healthcare business extract knowledge and insights from data to improve patient outcomes and economize your practice. We employ predictive analytics using machine learning to help clients predict patient behavior and ideal treatments and engagement strategies.

Syntelli Solutions can help you every step of the way; our data engineering services can help you organize and protect your data, and then our reporting and visualization services can help you come to data-driven decisions.

The Takeaway

Big data is not only accessible to big companies. With the support of Syntelli Solutions, you can leverage big data to improve patient outcomes and your bottom line in your home healthcare business.

The healthcare industry has begun to follow the footsteps of the commercial sector, but home healthcare services are largely missing out on the opportunities that data-driven approaches to healthcare can provide.

Take the next step toward bringing your home healthcare practice into the future: contact us to learn more. Ask us how we can help you unlock the value in your data.

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Natural Language Processing – Part 1: Building a Pre-Processing Pipeline

Now, you can imagine how much text data is generated every day. This brings up an important question– how do we process it and convert it into structured data?

Let’s consider that a large corporation is receiving 1000 reviews per day, and we want to analyze all these reviews because there a crucial business decision is dependent on that. Computers only understand structured data like spreadsheets. We want that computer to understand the language as we humans do, so the reviews can be analyzed very quickly. In this kind of situation, NLP comes into the picture. If we already have a trained model, then we can get an answer in minutes.

What is Natural Language processing?

“Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning from human language in a smart and useful way.”

Here are some common examples of NLP used in our day-to-day lives:

Smart Assistants

Most of us use smart assistants like Alexa and Siri that recognize our speech and give us answers based on context. NLP is used to understand the context. As New York Times article, “Why We May Soon Be Living in Alexa’s World,” explained: “Something bigger is afoot. Alexa has the best shot of becoming the third great consumer computing platform of this decade.”

Email Categorization

This is a very basic yet very useful feature of Gmail which displays our e-mails categorized into Primary, Social, and Promotions. This helps us review and respond to the important mails first. The Spam folder is also an application of NLP. Can you imagine your inbox with all emails combined to include spam and promotions without categorization?! 😂

Google Translate

When your first language is not English, this can be the most useful application of NLP. It takes text from one language as input and transforms it into the desired language using the right grammar. Google Translate supports almost 109 languages, allowing us to travel anywhere in the world without a language barrier!

How does NLP work?

As NLP gets more popular day by day, you’re probably wondering – How does NLP really work? How does Gmail categorize my emails?

“What is the matter here?”, asked the first lawyer, whose name was Speed.

In the above sentence, “The” has the highest frequency. As we understand it, “the” is just an extra word, but the computer might understand it as the most important word and conclude that the sentence is talking about ”The”. Therefore, we must teach the computer basic concepts of the English language.

This requires building a pre-processing pipeline, which is demonstrated below. (There is no specific order to follow for the pre-processing task, it is completely dependent on the Data.)

Demonstration of a Pre-Processing Pipeline

Building a Pre-Processing Pipeline

Sample Data for Coding

“COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2. It was first identified in December 2019 in Wuhan, China, and has resulted in an ongoing pandemic. The first case may be traced back to 17 November 2019. As of 14 June 2020, more than 7.83 million cases have been reported across 188 countries and territories, resulting in more than 431,000 deaths. More than 3.73 million people have recovered.” (Source: Wikipedia COVID-19)

NLTK Library provides all the features for pre-processing the data so this blog references the NLTK Library. However, other Libraries can be used too.

Step 1: Sentence Segmentation

First, we need to break down the sample data into separate sentences. In the sample data, we can see that every sentence has a different context. Separating each sentence makes it easier for a computer to understand rather than having it in the form of one whole paragraph. There are many ways to separate the sentences, but the most basic method is by setting it to separate whenever it finds the punctuation mark (.).

Example:

Here, we see separate sentences as an output.

Step 2: Word Segmentation

The data has been divided into separate sentences. Now let us divide it to into words. This process is called as Word Tokenization. Word_tokenize will divide sentences into words whenever there is a space.

Example:

You will notice that the code is considering ‘.’ as one word. We will remove that in the next steps.

Now, we have sentences divided into words.

Step 3: Text Lemmatization

In English, one word can be used in many different forms like,

Look at that apple.

Look at those apples.

We can see that both sentences use one-word apple but in different ways. We want to show the root words. Text lemmatization is the process of finding most basic word in each sentence.

Example:

The above example explains what Text Lemmatization is.

Step 4: Text Stemming

Text Stemming is the process of removing the pre-fix and post-fix from words. Lemmatization and Stemming can sometimes be confusing because you could get the same output with both algorithms. The below example will help make these clearer.

You will notice that for words like ‘Driving’ and ‘Drive’, Stemming and Lemmatization both result in the same output but for ‘Drove’, it results in a different output. Lemmatization gives us the root of the words while Stemming removes prefix and postfix.

Example:

Step 5: Remove Stop words

The English language has many words that we may want to remove before performing any analysis. In NLP, these are called Stop Words.

In the NLTK library, there is a list of Stop Words, but sometimes you might want to define a list of Stop Words on your own. NLTK considers some words like “again” and “before”, and in some cases, you may not want to remove these. Here, you can define the Stop Words list.

Example:

Now, let’s write code for removing stop words. We also want to remove punctuation marks from the data.

We’ve covered each step in detail and have learned how to build a reusable pipeline. In Part 2 of this blog series, we will discuss feature extraction and building classifiers based on the data.

If you have any further questions on this tutorial or would like to set up a time to discuss your NLP project with us, please feel free to contact us.

Jinal Butani, Data Scientist

Jinal is a Data Scientist at Syntelli, and her main area of focus is Business Intelligence. She graduated from UNCC in 2019 with a Master’s in Computer Science, and also holds a Bachelor’s in Information Technology. She has experience with successfully managing several Business Intelligence and Data Science projects. Jinal’s passion lies in extracting knowledge from data through visualizations, and building predictive models.

When Jinal isn’t working with data, you can find her performing classical dance, cooking and exploring the world.

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Leveraging Healthcare Data to Support Patient Engagement

The Problem of Patient Disengagement

Patient disengagement is associated with a lack of trust in the healthcare industry and the rising costs of health services. It manifests as patients refusing to accept an active role in their health care, which includes a lack of compliance, personal research, and allocating funds to their care.

Patient engagement statistics show that people are increasingly open to using apps or data aggregations to supplement their health care, but the actual use of existing health apps, ratings, and diagnostics is relatively low.

When patient participation is improved, we can hope for increased patient loyalty and outcomes.

How Data Can Help

It’s time for the healthcare industry to follow the lead of most modern businesses by using data to craft customer-centric approaches to business. Health data can help build patient engagement tools that target specific segments of patients.

Aggregated data that considers demographics and medical conditions along with compliance and outcomes can provide insight into what programs may help specific patients and which patients are at risk for non-compliance.

Patient data can also help healthcare providers find personalized solutions by increasing diagnostic abilities. Learning more about patients should ultimately help physicians improve patient relationships and get patients more involved in their care.

Sources of Healthcare Data

Many avenues can be pursued to collect healthcare-relevant data. Common sources include administrative data, medical records, surveys, and standardized clinical data.

Most healthcare organizations employ electronic health record (EHR) systems to collect and organize patient health information. EHR can be used in compliance with HIPAA to identify patterns among patients and expand the database of diagnosable diseases, which can improve patient outcomes.

Healthcare Customer Relationship Management (HCRM) is another tool that integrates information from EHR with demographics for a more holistic picture of the patients. Physician Relationship Management (PRM) works similarly but instead gathers data about physicians to identify and retain excellent providers.

Personal interactions with patients can also be sources of data, from call centers that field questions and complaints to interactions between physicians and patients.

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How to Use Big Data in Healthcare Patient Engagement

Machine learning and big data models are increasingly being used to inform nuanced intervention that might improve patient engagement. Apt text message reminders and targeted content have been made possible by AI and used effectively.

Patient health engagement models can help predict the risk factor of a patient becoming disengaged or non-compliant. This helps move physicians from a reactive mindset to offering proactive care.

Clinical propensity models with demographic, personal, and health-specific variables can help physicians develop treatment options and tools for patient engagement that target groups using correlations in the data.

Patient engagement specialists can benefit from statistics as they look for methods of increasing patient involvement and removing barriers to care.

Other Exciting Uses for Health Data

Models can be built from health and demographic data to help tailor chronic disease management; correlations in aggregated data can be used to design individualized treatment strategies.

Screening tools can also make it easier for physicians to relay the full range of treatment options to patients with rare diseases. Physician inexperience in treating or diagnosing a particular condition can be offset by a database that collects and organizes national or even global health information.

Big data may even have an important role to play in uncovering and preventing healthcare fraud. Artificial Intelligence (AI) systems may be able to find patterns in data that point to medical billing errors and waste, which are prevalent because of the industry’s complexity.

A review of the role of EHR in improving patient care found that a leading reason for medical errors is lacking medical data processing systems. Using standardized systems is effective at minimizing errors.

How to Increase Patient Engagement

Patient outcomes rely heavily on patient action, or inaction, regarding their health. An important part of any patient engagement system is supplementing patient knowledge of their conditions so they know the responsibilities and risks associated with their conditions.

Health literacy is an important aspect of this issue; natural language process (NLP) tools can be employed to help patients understand the wealth of information available to them through patient portals and databases.

Apps and wearable devices have also been found to improve clinical trial participation and patient engagement. Making participation more convenient and easier to follow can improve patient responses.

Finally, personal outreach must be a major part of patient engagement systems. Patient/physician relationships can increase trust, loyalty, and participation in primary care to disease specialist programs.

5 Ways AI Transforms Healthcare

The speed of AI in healthcare is one advantage that demonstrates the impact of AI in all aspects of healthcare, including care delivery, research, early detection and pharmacovigilance, diagnosis, and treatment. Here is a list of examples of the impact of AI in healthcare. Read More

Dangers of Using Healthcare Data

As with any novel tool, big data in the patient care realm is not without dangers. Misinterpretation of the data can easily lead to mistaken treatments. Healthcare facilities should use reliable data analytics to avoid these problems.

With the right support, AI transforms healthcare by improving the efficiency, distribution, and diagnostic ability of the industry.

Healthcare providers should also be wary of changing regulations surrounding the collection and use of health data. Data privacy is an important aspect of any data management plan.

Big Data Analytics

AI in healthcare can pay off in improved outcomes for many patients. Machine learning, an application of AI, uses data to constantly improve algorithms.

Trained data scientists can extract valuable insights from raw data and present the important aspects of visual diagrams or statistics. This can help providers move past the noise of data and target important information.

In Sum

The proper collection and reporting of healthcare data are revolutionizing the industry and helping physicians save lives. Syntelli Solutions is a leader in data managing and engineering and can employ predictive analytics to help you build a targeted system to improve patient engagement.

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Robotic Process Automation – An Approach to Consider in 2020

“Our intelligence is what makes us human, and AI is an extension of that quality.” – Yann LeCun, Professor, New York University

The above quote makes us think that robots, chatbots, artificial intelligence, machine learning are extensions of ourselves. Various industries are leveraging these qualities to solve problems, improve conditions, and save lives.

Robotic Process Automation is one such quality seen and applied in the industry today to automate and improve the efficiency of several activities that emulate human interactions, which are repetitive and time-consuming. With the help of RPA, companies, teams, individuals can focus on more critical tasks, which require our intelligence.

1. What is RPA?

Computer-coded software that automates manual activities by performing repetitive, rule-based tasks. Simple definition, right?

Figure 1.1 Visualizing RPA implementation in an insurance industry

A more detailed or complex definition would be the automation of specific business processes that are repetitive, labor-intensive, and involve at least some sort of elemental rule-based decision making.

Figure 1.1 provides a fair idea of business processes in an insurance company. All of these processes and tasks which are interdependent are candidates for well-orchestrated RPA implementation. Next, let us understand how RPA works.

2. How does RPA work?

Let us consider an example of repetitive workflow, that involves:

Save PDF attachments from an inbox.

The data in each PDF is then copied into an excel document, which gets saved on a machine.

The data from each excel document is then copied over to a website or a GUI to generate invoice reports.

Status report of invoices generated needs to be reported to the operations manager.

Figure 2.1 Converting several repetitive tasks to RPA workflow

The above workflow may take approximately fifteen minutes on an average for an individual to complete the first three tasks, and this does not involve the summary report yet. We also need to account for how many invoices get generated. Not to forget, there may be hundreds of PDF attachments in the mailbox and copy-pasting errors. This list of questions continues to grow.

Referring to the above repetitive tasks seems like a good use-case for implementing RPA workflow.

So how does the RPA work?

It replicates human interaction (keyboard inputs, mouse clicks)

It operates on User Interface Layer (Like GUIs)

It reads applications

It can be implemented on a desktop or a virtual environment

Figure 2.2 RPA implementation provides long term benefits and analytical capabilities too

Applying sound RPA design principles, testing, and deploying the above workflow can significantly reduce the time spent on each step, providing individuals more time for creative and intuitive tasks at hand.

3. Advantages & Limitations

Advantages:

24/7 Operations – Robots or software applications can work uninterrupted to complete a set of rule-based workflows or routines.

Cost Reductions – May generally cost one-tenth of human employee capacity.

Improved Efficiency – Three to fifteen times gain and more efficient workflows.

Improved Quality and valuable work.

Short Payback Period – Depending on the problem and RPA implementation approach, one can reap benefits anywhere within six to eight months after implementation.

Logging – Every RPA step gets logged, leading to future scope for data analytics and data science.

Internal Control and Traceability – Offers traceability and makes it available for analytical purposes.

Figure 3.1. All we need to do is review and approve the RPA implementation.

Limitations:

Not all tasks apply to RPA implementation.

Inadequate processes or tasks that are unpredictable should not be automated.

Any change in the process means updating RPA code.

There may still be a need for human intervention.

It still not at the expected level of intelligence to update a change in process and handle tasks without rules applied – Applying Artificial Intelligence and Machine Learning to RPA is a work in progress.

4. Piloting RPA in your enterprise

“The only limit to our realization of tomorrow will be our doubts of today.” — Franklin D. Roosevelt

Are you ready to implement an RPA solution at your company? If you are wondering where to start, the following guidelines may help you prepare better:

Establish interest and an understanding of RPA within in your organization or company

Manage Expectations – Get to the technology and opportunities of RPA

Involve IT – Requires collaboration and coordinated efforts with the tech gurus

Choose the right vendor – Select a vendor that meets your requirements

Use the following five-step approach for piloting and implementing RPA as shown in Figure 4.1

Figure 4.1 Five Step Basic Plan for Implementing RPA

5. RPA in Data Science and Analytics

From being simple rule engines, which make decisions based on a complex set of interlinked decision trees, RPA implementations are evolving.

Authentic ML or AI capability in RPA implementation is currently developed and tested by leading RPA vendors and may soon be the future.

RPA has paved the way for efficient and robust workflow while capturing a significant amount of data generated by logging during implementation. RPA can create clean, digitize, and structured or semi-structured data. This expanse of data serves as the source for data science and analytics.

Figure 5.1 The future state – RPA and Data Science become complimentary and drive business decisions

6. Conclusion

Robotic Process Automation is the future state of work automation, where remote work may be the new black. Being a low-cost initiative may spark more demand for implementations and evolving innovation in the field of RPA. With AI and ML capabilities currently explored by RPA vendors, we may be void of repetitive work in the future.

Figure 6.1 RPA with AI/ ML capabilities are the future that can benefit the society for the greater good

RPA cannot displace humans or our jobs. It will help us focus and spend our creative energies on transforming teams, business units, companies, organizations, and society. An approach worth considering!

While our consultants are not robots, we learn from previous projects and engagements, which helps us solve today’s problems better. Syntelli will work with you to help you understand and implement a successful RPA solution.

Vishwas Subramanian, Sr Analytics Associate

Vishwas provides solutions to big data problems like real-time streaming data, Traditional SQL vs NoSQL, Hadoop or Spark, Amazon Cloud Services (AWS) vs Personal Cluster. His focus is on analyzing and providing optimum solutions for business use cases.

Vishwas received an M.S. in Electrical Engineering from the University of North Carolina at Charlotte. His research interests are Spark development, Visual Analytics, Android Devices, Machine Learning.

When Vishwas is not providing incredible big data solutions to our clients, you can find him hiking, playing soccer and travelling.

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Using Big Data Analytics to Create Customer Intimacy in Healthcare

This naturally translates into uncertainty among hospital administrators and other management as well. As a healthcare organization strives to improve patient outcomes and satisfaction, customer intimacy dramatically influences the future of the organization.

Attaining customer intimacy requires careful use of customer data analytics to draw a clearer picture of patient needs. Using a wide range of big data is the only way to build rapport with patients in an era when patient and doctor time is limited.

Why Is Customer Intimacy Important?

Customer intimacy plays a huge role in the growth of an organization. While external marketing is important, existing customers may not recommend the provider if they don’t have a strong relationship with the doctor or clinic.

Even more importantly, customer intimacy can increase trust in physicians, which has generally decreased in recent years. This may improve patient compliance with prescribed treatment plans, which is especially critical for individuals living with chronic conditions.

The Complexity of Chronic Conditions

Even if a patient sees the same physician multiple times a month, that physician may struggle to understand their life and risk factors. Physicians tend to interview the patient extensively about their diet, habits, and medical history during the first appointment, then not follow up in-depth about changes.

Patients might not always be honest about aspects of their life that may be negatively impacting their health. They might overestimate how much they exercise or underestimate how frequently they smoke.

These failures to gather information are usually not the physician’s fault, especially since appointment time is so limited. A healthcare provider can improve physicians’ knowledge of individual patients and larger trends by adopting a more cutting-edge approach to predictive analytics in healthcare.

Big data analytics can reveal more about patient exercise and dietary habits than the patient realizes about himself. AI in healthcare can also unveil demographic risk factors and potential comorbidities that may complicate care and increase readmission risks.

Delivering Preventive Care Information

Many patients don’t visit their primary doctors annually and may miss out on opportunities for preventive care. Preventive care appointments are both medically necessary and a great opportunity to build patient trust and intimacy.

Delivering custom-tailored preventive care information to patients may motivate them to visit their doctor again, increasing the chances of them receiving the care they need. This information could be tailored based on the number of children in the household, income, age, and other factors that influence the type of care they need.

If a patient hasn’t visited the provider in a long time, their data on-file may be irrelevant to their current needs. Big data can help supplement this information and show changes in zip code and income.

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Overcoming Financial Obstacles

Healthcare costs are projected to increase at an average of 5.5% per year until 2027. Although health insurance covers most major costs, monthly prescriptions and other expenses can add up quickly for low-income patients.

Many patients hesitate to talk about their financial situation, leaving doctors unaware of the obstacles they face. It’s up to the healthcare industry to proactively inform patients of ways to afford their medications.

Big data can provide a wealth of information about patient income, family size, spending habits, and other demographics that indicate their overall financial health. By combining that information with data about the failure to fill prescriptions, healthcare providers can predict which patients are likely to be non-compliant with prescribed care due to financial issues.

From there, providers can design and custom-tailor emails, text messages, and other outreach efforts to encourage patients to apply for social safety nets, financial aid, and other benefits. In some cases, patients may even be missing out on generic drug options or basic manufacturer coupons.

Deciding Where to Start

Harnessing the power of big data in a way that is relevant to your patients takes time. Many healthcare providers and insurance companies already use AI to tackle healthcare fraud and other problems, but solutions must be customized to meet each organization’s needs.

Although there is a massive amount of data already available and waiting to be analyzed, implementing relevant changes must be done step-by-step. Data must be cleaned before use, and although some data can be stored in unstructured data lake form, even getting to this step requires some time and planning.

Starting with the most serious and urgent problems allows you to boost customer intimacy and trust much faster. Leading causes of death like heart disease can be tackled easily, especially since preventive care and lifestyle changes can do so much good.

Depending on your overall patient demographics, you may decide that financial obstacles and knowledge of financial aid options are an even larger issue. A quick analysis of your patients can reveal which routes should be investigated more in-depth, and which actions should be taken first.

How to Measure Customer Intimacy

One of the ways AI transforms healthcare is by allowing providers to measure consumer intimacy and satisfaction in new, in-depth ways. Being able to quantify customer intimacy values will show you whether your customer intimacy strategy is actually working.

Big data can be helpful in measuring how much the provider is being advertised word of mouth. Social media mentions and reviews can give huge insights into customers’ perceptions of your organization’s intimate customer service. Drawing customer data and analytics from multiple sources can give you the complete picture of how much your patients trust in your care.

Creative Use of Big Data

The impact of big data analytics on healthcare is multifaceted, and as technology improves, we may see even more ways to use big data. Healthcare providers need to start the process of big data analytics as soon as possible to improve patient outcomes and organizational growth.

Syntelli Solutions has the skills and experience you need to link big data and consumer behavior and improve patient outcomes and retention. We provide a huge range of data and analytics services in the healthcare industry and can custom-tailor our services for any size organization. Contact us today to learn more about the new technologies available to you.

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How Data Visualization Made A Breakthrough During COVID-19

We live in a time where it’s not only important to have access to qualified data – the representation of that data is equally important. Data visualization has become a critical element of the current COVID-19 pandemic. It’s been used as the main tool for spreading trustworthy information around the globe.

Data visualization is a presentation of data in a graphical or pictorial format.

It conveys information easier and it simplifies the process of understanding the data for regular users, or in the case of COVID-19, the general population.

One great example of using data visualization in the last months is from Johns Hopkins University’s Center for Systems Science and Engineering. The excellent team from the University created a map, one-of-a-kind so far – it maps the total number of cases, the cases per country, the death, and other data. It gives users an instant ability to visualize the effect of the pandemic and with it, to get a more comprehensive understanding of the data presented.

HealthMap is another accurate representation of the importance of data visualization for this virus. The map shows the timeline of the virus from the starting point in January, per country.

These examples are helping the general public to stay informed with real-life data. The maps are done by using one simple method: Information Visualization Mantra. It’s the way data is presented to the user, by this order:

Overview,

Zoom and filter and

Details on demand.

This method gives users countless options for finding out data for a specific region, state, or even a city – a data that can be used for scientific purposes.

But how did this happen in the first place, and more importantly why?

Data is not something new – we’ve been using it since forever.

The key difference right now is in the world we’re currently living in. The COVID-19 pandemic motivated scientists, developers, university professors to think outside of the box and find out new, understandable ways to present data for the general public.

Dr. Steven Drucker, a University professor and Research Manager at Microsoft Research thinks that not once before the data visualization has been so present in everyday life.

Different mediums have found an original way to present data visualization.

For example, The New York Times has an entire map built on the site, that shows the cases per country with different graphics, updated daily. The Washington Post presented a simulation about the spreading of the virus among people, something that should be an eye-opener for everyone who sees it.

The revolutionized use of data we’re currently witnessing can only expand as time passes. Businesses that won’t be able to keep up with the latest trends will lose this race.

We at Syntelli Solutions can help you with that – we know the impact data can make. With our services, you can leverage your current data and use your data for creating something valuable.

Shreyansh Thanvi, Analytics Associate

Shreyansh works with Syntelli’s Customer Information and Analytics team to determine business requirements, mine data from multiple sources, and perform data visualization and reporting to help our clients achieve their business goals.

With an M.S. in Information Technology from University of North Carolina specializing in Data Analytics, he enjoys working with large amounts of data, facts, and figures and using them to make informed decisions.

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Using Big Data to Attract and Retain the Digital Banking Customer

Now, customers want to have the ability to send money to a variety of accounts, access credit card rewards, and customize their account settings from anywhere. They look for a bank that has the features they need and the customer service they deserve, especially as they travel and spend online in more ways than before.

As big data in digital banking gets smarter and faster, banks are brainstorming more ways to market their services and help their customers make better financial choices. Banks can increase new sign-ups and customer retention by investing in engaging, relevant features that take advantage of the wealth of data available on consumers.

What Are the Benefits of Digital Banking?

Customers have a wide range of needs depending on the services they’re accessing, their lifestyles, and the technology available to them. Digital banking reduces in-person staffing and customer service costs and gives customers easier access to payment systems.

The added convenience of digital banking makes it easier for customers to pay their bills on-time whenever they remember, instead of dealing with paper forms or phone calls. This can reduce late payments and fees, boosting customer satisfaction and trust.

1. Reducing Fraud

Customers trust that banks will protect their accounts, and banks can improve and advertise their security efforts to attract and retain customers. In recent years, big data has become a key part of modern fraud detection algorithms. By using machine learning to teach AI programs about customer trends scraped from big data, banks can detect and flag transactions that are unusual and likely to be fraudulent.

Customers don’t like dealing with false alarms, so banks have to get the financial services analytics company to keep false positives to a minimum. When customers feel their account information is securely protected, they are less likely to close credit cards or take other actions to reduce their reliance on a bank.

2. Customer Shopping Habits

The link between consumers’ habits and their banking needs is a critical example of the relationship between digital banking and big data. Groceries, gas, bills, and other expenses reveal even more about a customer’s life than their general demographic features.

Many banks already track customer sales data and use that to provide better recommendations for financial services. However, big data can supplement this information and provide more insight into what customers buy using cash or competitors’ credit cards.

With data analytics in financial services, there is huge potential for customized marketing campaigns based on customer shopping habits.

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Individuals with high income and more discretionary spending can be sent emails advertising a prestigious credit card, while individuals who aren’t using their cards often can be sent emails with special offers encouraging them to use their cards more.

3. Solutions for Every Location and Device

Even tech-savvy customers who predominantly use mobile apps and websites will occasionally need in-person services. Currency exchange services, ATMs, and other cash needs must be relatively nearby for a customer to continue using a particular bank as their primary provider.

Big data strategies for financial services can inform company decisions about where to build brick-and-mortar locations. If a majority of customers live in the suburbs but work and shop in the city, then banks may find that small downtown locations are just as valuable as suburban ones.

Big data can also provide insights on the types of devices customers own and which devices they use to access mobile services and websites. By investigating which devices customers are and aren’t using to access services, banks can discover which versions of their apps are worth investing more in.

4. Better Credit Card Rewards

Credit card points and rewards have been a part of banking services for years, but they are still not used as frequently as they could be, even as online shopping and mobile app usage have become more popular. Smarter digital banking with big data must include tangible perks for customers to increase perceived and actual value of services.

Banks can also use big data to tweak their cash back percentages and other reward criteria to meet the needs of their target demographic. For example, young urban professionals without cars won’t benefit from bonus points on gas purchases, so banks need to offer a different perk to attract that demographic.

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5. Predicting Customer Questions

The use of data analytics in banking can provide insights as to what services customers will need next, which can also predict the questions they have. Banks can use cutting-edge data mining to predict questions customers are likely to have and even point them toward relevant written FAQ pages while customers are using the service.

By predicting customers’ questions and addressing them in advance, banks can reduce the amount of resources they spend providing phone- and chat-based customer service. It can also improve customer satisfaction by sharply reducing or eliminating the amount of time customers spend waiting for help.

6. Mortgage Marketing

Young people tend to put off buying their first home until after age 35, but this trend can still vary based on economic factors. Big data can inform financial institutions of which customers are actually searching for homes, regardless of age and other factors.

Since customers don’t want to receive too many marketing emails or in-app notifications for offers, it makes sense for banks to use big data to only show services likely to be of interest to a particular customer. However, existing customers may turn to another lender for their mortgage needs unless they have a trusting relationship with their current bank and have a reason to believe that their current bank provides the best service and value.

This requires proactive and carefully-tailored marketing that advertises the best rates and loan options to customers whose big data suggests they are ready. Marketing can be tailored even further to educate first-time homebuyers, who may have lower financial literacy or knowledge of mortgages simply because they haven’t experienced the process yet.

7. Smarter and Forward-Thinking Digital Banking

Getting the most out of big data requires a multi-faceted approach and a complex data analytics system. All sizes and types of financial institutions have unique needs, and data science experts can provide customized solutions that incorporate data safely and securely.

Syntelli Solutions is a leader in modern big data, AI, and predictive analytics for financial services and other industries. We have a track record of success with small and large clients alike, with projects ranging from basic open source migration to in-depth reworking of CRM systems. Contact us today to learn more about our team and services.

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Benefits of Mobile Business Intelligence For Your Business

There are currently 3.50 billion smartphone users around the world. With it, the possibilities of developing successful mobile BI strategies for your business are endless.

Here are some of the most relevant benefits of Mobile BI for your business:

1. Business On Wheels

More and more people are working remotely. Especially nowadays, during the coronavirus outbreak, as remote work has become essential to keep the economy going. For business purposes, employees are traveling between different locations more often than usual. If you are able to access your working dashboard from anywhere, anytime, your business effectiveness will skyrocket.

Mobile BI creates space for a creative expression of everyone in the team. Brainstorming ideas, sharing valuable insights, or developing new strategies is much easier because everyone has the same access to data.

2. Increased Performance Rates

One of the most important benefits of mobile business intelligence is data accessibility.

Getting a database update every second is the biggest perk because time is money.

Achieving targets and KPI’s through mobile devices has improved employee’s performance significantly. We found out this to be particularly true during this pandemic, as having remote access to business run workstations smoothly.

Access to real-time analytics enables every user to react faster and come up with a solution almost instantly. The entire decision-making process is much faster – instead of searching for relevant data, the data is available on your display.

3. Better Competitive Advantage

An effective mobile BI plan can increase your chances of becoming an industry leader in no time. Access to new data in real-time creates conditions for faster and more flexible adaptation to the market needs. Your sales or marketing team can develop new strategies in a respectable time-manner, ones that can definitely put your business on the spot.

The access of real-time data has given businesses a chance to capitalize, up-sell, manage, and cross-sell and with it, to cope with the dynamic changes in the market.

4. Improved Customer Satisfaction Faster

Mobile BI creates another benefit – a better customer experience. By providing faster, more reliable service, your company is able to increase customer satisfaction. Happy users are willing to share their positive experiences with the community and that’s the best marketing a company can encounter.

Additionally, since employees have access to real-time data, they’ll make valuable decisions faster – which saves a lot of time.

5. Top-Notch Network Security

One risk of having access to data is the chance to steal it. That’s why when it comes to mobile BI, there’s a protective shield around the platform for additional protection.

Secure communications, as a transmission of data, are done through Secure Socket Layer (SSL) or VPN connection. Another security layer is DES or AES along with an encrypted SSL tunnel, sent via 3G/4G or Wi-Fi. If by any chance, a device gets stolen with data on it, the data can be easily removed by the company’s mobile device management system.

These benefits are just the tip-of-the-iceberg – the pros list is much longer.

Here at Syntelli Solutions, we use our real-world data analytics experience to provide you with an effective solution and make sure you achieve a competitive advantage. Our data scientists can leverage your business’s data and your decision-making process will become faster.

Shreyansh Thanvi, Analytics Associate

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5 Ways Prescriptive Analytics Helps Deliver Better Financial Services

Prescriptive analytics is a data sciences field that shows companies the best decision to make in a given scenario. This field uses specialized data analysis programs to consider a range of possible decision parameters, then analyze which one gets the desired results.

Prescriptive analytics is useful in a wide range of applications, from manufacturing investments to self-driving cars. However, it can be particularly helpful for financial services analytics due to its ability to harness long-term economic trends and customer data, including big data.

Here are the top five ways financial services companies can embrace prescriptive analytics to make even better business decisions.

1. Optimize Financial Services

Prescriptive analytics can be trained to calculate what would happen if companies tweaked aspects of their products. For example, prescriptive analytics can tell a company how much to reduce the cost of a product to attract new customers while keeping profits high.

Since most financial services companies have a wide variety of products and services, applying prescriptive analytics to each of those services can maximize profits while minimizing risks. Prescriptive analytics enables leaders to determine the best potential ideas in a simulation, instead of experimenting in real life.

Making financial services more efficient isn’t the only way to improve them. Customer value analysis in financial services requires companies to take a close look at what elements of a service make it valuable and likely to attract customers. Customer perception of services can be surprisingly subject to change, and even a minor shift in service terms and limits can make customers seek out a competitor.

2. Marketing Budgets and Decisions

In order to decide how much money to spend on marketing, financial services companies must take into account how much potential reach they have into a target demographic. The use of data analytics in banking and other services allows companies to analyze the best options for marketing campaigns.

Prescriptive analytics also helps companies decide where to spend their marketing budget, and which demographics will be the most valuable. If a specific demographic is already seeing plenty of ads, the company can pivot and invest more in another mode or targeted online ads seeking a different group of potential customers.

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3. Risk Management

Data analytics in financial services can help companies assess and deal with risks. Insurance plans, mortgages, and new credit card accounts all come with risks for the provider. While some of this risk is unavoidable, some of it can be accounted for and mitigated.

An example of prescriptive analytics in risk management is calculating what would happen if a company tweaked its mortgage qualification criteria. By analyzing a broad amount of data like income and risk of foreclosure and accounting for several economic scenarios, a mortgage company can determine if relaxing their criteria is worth the potential increase in customers.

Prescriptive analytics programs can tell companies exactly how much to relax or tighten their qualification criteria for services. They can even take into account customer satisfaction and long-term economic forecasting.

Risk analytics in financial services cannot account for all variables, especially in the wake of the COVID-19 crisis and economic recession. However, prescriptive analytics can still analyze long-term trends, and current customer needs to help shape their conclusions.

4. Plans for Expansion

Opening new branches and services requires financial services companies to commit a large number of resources. Prescriptive analytics in banking includes processes for determining which expansions are worthwhile, and which are more costly than they are worth.

This is one of the uses that make prescriptive analytics part of big data strategies for financial services. Big data like customer location, financial habits, and mobility can play a large part in deciding whether a new service or location is worthwhile. A bank or credit union shouldn’t open a new branch if the market is already saturated, or if the branch is in an area that isn’t accessible to the target demographic.

It’s also a key area for harnessing data mining in financial services. Data mining allows companies to take large amounts of data, including big data, and sort it into a usable format. Data mining can find hidden and emerging patterns in customer habits and trends, and prescriptive analytics can then apply that information to future scenarios.

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5. Reduce Spreadsheets and Boost Efficiency

In addition to affecting your customer-facing services and income, an excellent prescriptive analytics program can reduce your reliance on spreadsheets and manual data analysis. Most financial services companies use data professionals who clean, maintain, and update data in several formats.

Digital analytics in financial services don’t have to rely only on a team of professionals. By using prescriptive analytics from financial services analytics companies, banks, and other providers can overcome the limits of traditional methods and reduce the strain on data analysts. This, in turn, frees them up to work on other tasks to improve the company further.

Projecting the Future

Since computer processing power is continuously improving, prescriptive analytics technologies also improve. Big data analytics in finance will continue to evolve and provide more accurate calculations and predictions as more companies use them.

Prescriptive analytics enables you to make more informed decisions, decreasing risk and loss. At Syntelli Solutions, we have experience serving a variety of clients in the financial services sector and can work with you to implement customized solutions.

We offer a range of support services for data science in finance, including prescriptive analysis, software training, cloud server support, and more. Contact our team today to learn more about how we can help you maximize your growth.

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Automated Reporting vs. BI: Which Do You Need?

Although automated reporting of current metrics is helpful, businesses can’t thrive without in-depth business intelligence (BI) that digs deeper into the data. The difference between the two is key to unlocking a company’s full potential in a competitive market.

Automated reporting shows current and past accomplishments of a business, while BI answers questions of why and how a situation has occurred. However, the capabilities of each are complex and nuanced, with far-reaching implications for various industries.

Automated Reporting

Automatic reporting provides micro reports of business insights to give employees like fulfillment managers easier and faster access to information they regularly use to help them make short-term decisions.

Automated report generation can be scheduled to run daily, weekly, or whenever a company needs information to be gathered. They can be automatically sent to relevant parties to guide business decisions.

An automated report can contain customer metrics and demographics, internal data on employee speed and task completion, net and gross profits, and other data. The results can help companies immediately adjust spending, orders, and more.

Automatic report software typically has pre-set data visualization programs that can display data consistently every time. However, this software can be customized to meet the needs of managers and analysts. The best software must also include a full suite of BI features to aid in future growth.

Business Intelligence

BI can answer more complex questions about what factors are driving business growth or losses. In order words, automated reporting can report on which areas are working well and which ones are not, while BI can help leaders decide why they are working and how to improve overall performance.

BI analytics can be run regularly, but usually not as frequently as automatic reports. They are more likely to occur quarterly or as leaders consider operational changes and innovations. Automated reporting is intended for management to use while simply monitoring for potential problems and sudden growth, while BI analytics is designed for more in-depth investigations.

With BI, leaders can answer questions like why sales have increased, whether specific marketing campaigns are having an effect on customer demographics, or how employees have become more productive. These questions are critical to leaders who want to maximize profit and growth by replicating past successes.

BI is very similar to predictive analytics, but with the critical difference that predictive analytics is focused on the future and potential events that can influence business growth. Potential areas for predictive analytics include how and where to market services, how long successful services will continue to remain profitable, and which customer demographics are likely to change in the future.

Data Visualization and Reporting Tools

One of the strengths of modern BI software is that it can display data in a variety of ways. Users can create custom reports with little to no programming knowledge, as long as they know where to find the exact data they wish to use.

Common programs like Microsoft Power BI are powerful yet easy to learn with some basic training. Power BI can create customized dashboards that users can access at any time. Live data and data for set periods are just a few clicks away, reducing the amount of time staff spend analyzing data and automating reports.

Programmers can add additional non-native features to Microsoft Power BI and other software with APIs and other customized applications. This added flexibility helps businesses with highly specialized reporting automation needs or complex datasets.

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Applications in Different Industries

Nearly every organization needs data analytics of some kind, and automated reporting systems are the easiest way to accomplish this. The exact needs of each industry will vary, but the ability to analyze different needs can help companies reach their goals.

Healthcare organizations need automated reporting to analyze patient intake, and BI to discover why those patients chose their organization. BI software can securely analyze data and compile reports that help organizations maintain practices that work and search for solutions when plans fall short.

Manufacturers often need automatic reporting to keep up on internal issues, like machine downtime and production speed. Oil and gas professionals have similar needs but are under even more pressure to keep up with constantly changing market demands.

Financial services need automated reporting to stay on top of account openings and closures, insurance payouts, fee intake, and other metrics. To better understand client wants and needs, companies can use BI with more in-depth data analysis to determine what services to offer in the future.

Reducing Stress on IT Departments

Some companies try to use their IT staff to pull data and create reports on key metrics manually. While this can work for a start-up with limited resources, it is not a sustainable practice for a growing business. It also pulls IT staff away from other key tasks like troubleshooting and updating systems.

The solution is to embrace the newest technologies and automation. Reports generated by specialized software will be much faster and eliminate human error, making them a more reliable option for businesses of any size.

Investing in user-friendly BI software also allows individual users to generate and modify reports as often as they want and use auto reporting to keep up with changes. With the right software training, everyone from marketing staff to CEOs can generate and automate reports that answer their questions.

Services from Syntelli

Syntelli Solutions is a leader in data analytics and AI, including report automation tools and business intelligence. We provide services to a broad range of industries, including healthcare, oil & gas, manufacturing, and financial services.

We can help you store and organize data, develop new APIs for existing systems, make data analytics programs run faster and much more. We also have forward-thinking services like predictive analytics and customized machine learning programs. Contact us today to book an appointment and learn more about our data science solutions.

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Data Governance – How to Start with Success

Data governance is a hot topic in every industry.

How do we get consistent reporting each department trusts?

What do each of these values mean to different teams?

Are we are looking at the same information?

How do we audit our reporting properly?

As architectures grow over time, with tools and data marts being added, it can feel like your reporting is further from the source you are working to audit. Master Data Management (MDM) tools are a great way to accelerate a Data Governance initiative. But, tools only get you so far. Data Governance is a people process before a technical process. You will need to get your team structured, your architecture mapped, and budget secured before evaluating tools.

In the past, all data management and reporting fell on IT departments. The values needed are consumed by business. The further the disconnect between the two, usually the further the gap in trust. As Data Governance becomes more of a topic in board rooms, the human disconnects become more apparent.

The first question that needs to be asked if your business is ready for Data Governance is “Do we think about our data process and values as something we throw over the wall to IT?”

If so, is your business ready to change this mindset from the top down?

IT can change process, database structures, reporting flows and calculations. But they cannot fully understand what the full meaning of the data without definition from the end users or business. Most companies begin to realize they have less definition than they originally thought with multiple end users assuming different meanings of the same value.

Change in this thought process is hard and beginning to map and evaluate your data is an arduous process. Every business thinks their data is too unique to be mapped or their calculations are done nowhere else. It takes time to describe the data and process to a level of detail that is easy to understand. Just like any business process, no knowledge imperative to business be scaled if it is not defined and transparent.

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It is counterintuitive to work backwards. Governance meetings usually start to thin in attendance as a project continues. Sitting to define data that has been ingested for years feels like it is less important than meetings to push business forward. Project planning on any Data Governance initiative is the most important piece to producing a success.

Some tips below are some ways to keep your initiative moving forward, in step with business to see positive results.

1. Keep Everyone Accountable

This may feel easier said than done. It is important, as described previously, to get buy in from the top down. RACI charts for each step of the process is one of the most important things to establish first. If everyone knows what they are responsible for early, it helps escalate issues or stalled progress.

2. Document Everything

Every source, system, data value will need to be documented in current state and future state. This is the most important piece that will fulfill any gaps in trust. All business rules and data dictionaries should live in a shared location that are review with everyone to the end users.

3. Protect Your Hub

Master data should live in a separate ‘hub’ where it can be maintained. The business rules and logic should not live in a data warehouse or transactional database. The master data should be in a place that is fed and feeds downstream applications and warehouses.

4. Assign Responsibility Correctly

SME vs. Data Steward – know the strengths of each resource and communicate the roles appropriately. Data Stewards should be reviewing outliers in the data that are falling outside of the rules established and making educated decisions on how to remediate. SMEs should be creating rules, documenting changes and receiving escalated remediation tickets. These resources should be two different people, but with a close working relationship. One cannot work effectively or happily without the other. RACI charts also help teams visualize who will be making final decisions and who needs to be informed. Starting with assigned responsibilities will help accelerate all decisions moving forward.

5. Engage

Work to keep your team engaged in their roles and reporting out to stakeholders in a way that shows quick wins. Pulling the right team members and stakeholders in at the right times to will help keep everyone excited about the progress and produce a deeper understanding of what it takes to have a successful governance program. Trust us, with an engaged team, Data Governance can be fun!

It sometimes takes a team of people internal and external to push a full governance process. If you want to hear more about Syntelli’s approach and favored tools, reach out! We are happy to help.

Kirsten Pruitt, Customer Success Manager

Communication is the key to great delivery. Kirsten joined Syntelli Solutions in 2016 to bring her delivery experience to clients’ projects and enhance our conversations about data. Prior to joining Syntelli, Kirsten spent 4 years as VP of Marketing at Healthcare Education Associates and spent another 4 years managing accounts for an advertising agency. She leverages her previous experience to help our clients in the Healthcare and Manufacturing sectors remain progressive in their thinking about what to do with their data. When Kirsten isn’t delivering awesome projects to our clients, you can find her cheering on our local Carolina Panthers!

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