#data warehouse vs data mart

[albertyevans]

Uncover the differences between Data Warehouse and Data Mart through our in-depth comparison. Explore into their setups, data sources, focuses, and decision-making capabilities to optimize your data management strategy.

[brigitaindia]

What Makes Snowflake Cloud Data Warehouse Different?

The Cloud Data Platform (CDP) offered by Snowflake is one of the most popular tools for businesses transitioning to modern data architecture. One of the most common questions we get from clients is, “What makes Snowflake stand out from other cloud data warehouses like Amazon Redshift, Azure Synapse?” In this article, we’ll look at six unique and important features that set Snowflake apart from the rest. 

What Is a Data Warehouse?

A data warehouse is like a big box store of data that you can use to make better decisions. Data comes in from different sources like transactional systems, databases, and more, usually on a regular basis. Analysts, data engineers, and decision makers can access the data through BI, SQL, and other analytics tools. Analytics and reports are essential for businesses to stay ahead of the competition. Business users use reports and dashboards, as well as analytics tools, to get the most out of their data, track business performance, and make decisions. Data warehouses help these tools by storing data in a way that minimizes I/O and quickly delivers query results to hundreds or thousands of users at once. 

Data Warehouse vs. Database

·     A database is a set of related data that shows what's going on in the real world, while a   Data warehouse is a system that stores data from different sources.

·     Databases are designed to store data, while Data Warehouses are used to analyze it.

·     Databases are used for application-oriented data, while Warehouses are used for subject-oriented data.

·     Databases use OTP, while Warehouses use OAP.

·     Database tables and joins are complex because they're normalized, while Warehouse tables and joins are simple because they're denormalized.

·      ER modeling is used for Databases, while data modeling is used for Warehouses.

What is Data Warehousing Used for?

Data Warehouse is a great way to get the most out of your business.

1. It helps you access important data from multiple sources in one place, so you can easily access it from the cloud.

2.    It also helps you integrate multiple sources of data, so you can reduce stress on your production system.

3.    It helps you save time on analysis and reporting by reducing the total turnaround time.

4.    It also stores a lot of historical data, which can help you make predictions about the future.

5.    It also adds value to your business applications and CRM systems, as it separates analytics processing from the transactional databases.

6. Lastly, it helps you make sure that stakeholders and users aren't overstating the quality of the data in your source systems, so you can get more accurate reports.

Components of a Data Warehouse

A data warehouse is composed of five distinct elements:

a)  ETL: Extract, Transform, Load (ETL) is a process used by Database Administrators (DAs) to transfer data from a source to their data warehouse.

b)  Metadata: Metadata is a collection of data about data. It describes all of the information that is stored in a system for the purpose of making it searchable. Examples of metadata include articles' authors, dates or locations, create date of file, file size, etc.

c)  SQL Query Processing: SQL is the primary language used to query your data. It is the language used by analysts to extract insights from the data stored in a data warehouse. Generally, data warehouses employ specialized SQL query processing technology that is closely integrated with the compute, enabling high-performance analytics.

d) Data layer: The data layer is where users actually access the data. That's usually where you'd see a data mart. The data layer separates out different parts of your data depending on who you're trying to give access to. That way, you can get really granular across your company.

e)  Governance/Security: This is linked to the data layer, meaning you must be able to offer fine-grained access and security controls across all of your organization’s information. Most data warehouses have excellent governance and security built-in, so there’s not much custom engineering required to include this. Governance and security needs to be planned for as your warehouse grows and you add more data.

A well-crafted data warehouse is essential for the successful implementation of an analytics program. The main features of a data warehouse are dashboards, analytics tools, and reports, which are essential for any organization. Brigita software development services provide the necessary data-driven decisions, helping you to make the correct choice when developing a new product or inventory. A properly designed component of a data warehouse facilitates the rapid execution of queries, providing higher benefits than on-premises or traditional versions.

[jcmarchi]

A Beginner’s Guide to Data Warehousing

New Post has been published on https://thedigitalinsider.com/a-beginners-guide-to-data-warehousing/

A Beginner’s Guide to Data Warehousing

In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions.

However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Businesses must employ certain techniques to organize, manage, and analyze this data.

Enter data warehousing! 

Data warehousing is a critical component in the data ecosystem of a modern enterprise. It can streamline an organization’s data flow and enhance its decision-making capabilities. This is also evident in the global data warehousing market growth, which is expected to reach $51.18 billion by 2028, compared to $21.18 billion in 2019.

This article will explore data warehousing, its architecture types, key components, benefits, and challenges.

What is Data Warehousing?

Data warehousing is a data management system to support Business Intelligence (BI) operations. It is a process of collecting, cleaning, and transforming data from diverse sources and storing it in a centralized repository. It can handle vast amounts of data and facilitate complex queries.

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making.

Moreover, modern data warehousing pipelines are suitable for growth forecasting and predictive analysis using artificial intelligence (AI) and machine learning (ML) techniques. Cloud data warehousing further amplifies these capabilities offering greater scalability and accessibility, making the entire data management process even more flexible.

Before we discuss different data warehouse architectures, let’s look at the major components that constitute a data warehouse.

Key Components of Data Warehousing

Data warehousing comprises several components working together to manage data efficiently. The following elements serve as a backbone for a functional data warehouse.

Data Sources: Data sources provide information and context to a data warehouse. They can contain structured, unstructured, or semi-structured data. These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc.

ETL (Extract, Transform, Load) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.

Metadata: Metadata is data about the data. It provides structural information and a comprehensive view of the warehouse data. Metadata is essential for governance and effective data management.

Data Access: It refers to the methods data teams use to access the data in the data warehouse, e.g., SQL queries, reporting tools, analytics tools, etc.

Data Destination: These are physical storage spaces for data, such as a data warehouse, data lake, or data mart.

Typically, these components are standard across data warehouse types. Let’s briefly discuss how the architecture of a traditional data warehouse differs from a cloud-based data warehouse.

Architecture: Traditional Data Warehouse vs Active-Cloud Data Warehouse

A Typical Data Warehouse Architecture

Traditional data warehouses focus on storing, processing, and presenting data in structured tiers. They are typically deployed in an on-premise setting where the relevant organization manages the hardware infrastructure like servers, drives, and memory.

On the other hand, active-cloud warehouses emphasize continuous data updates and real-time processing by leveraging cloud platforms like Snowflake, AWS, and Azure. Their architectures also differ based on their applications.

Some key differences are discussed below.

Traditional Data Warehouse Architecture

Bottom Tier (Database Server): This tier is responsible for storing (a process known as data ingestion) and retrieving data. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.

Middle Tier (Application Server): This tier processes user queries and transforms data (a process known as data integration) using Online Analytical Processing (OLAP) tools. Data is typically stored in a data warehouse.

Top Tier (Interface Layer): The top tier serves as the front-end layer for user interaction. It supports actions like querying, reporting, and visualization. Typical tasks include market research, customer analysis, financial reporting, etc.

Active-Cloud Data Warehouse Architecture

Bottom Tier (Database Server): Besides storing data, this tier provides continuous data updates for real-time data processing, meaning that data latency is very low from source to destination. The data ecosystem uses pre-built connectors or integrations to fetch real-time data from numerous sources.

Middle Tier (Application Server): Immediate data transformation occurs in this tier. It is done using OLAP tools. Data is typically stored in an online data mart or data lakehouse.

Top Tier (Interface Layer): This tier enables user interactions, predictive analytics, and real-time reporting. Typical tasks include fraud detection, risk management, supply chain optimization, etc.

Best Practices in Data Warehousing

While designing data warehouses, the data teams must follow these best practices to increase the success of their data pipelines.

Self-Service Analytics: Properly label and structure data elements to keep track of traceability – the ability to track the entire data warehouse lifecycle. It enables self-service analytics that empowers business analysts to generate reports with nominal support from the data team.

Data Governance: Set robust internal policies to govern the use of organizational data across different teams and departments.

Data Security: Monitor the data warehouse security regularly. Apply industry-grade encryption to protect your data pipelines and comply with privacy standards like GDPR, CCPA, and HIPAA.

Scalability and Performance: Streamline processes to improve operational efficiency while saving time and cost. Optimize the warehouse infrastructure and make it robust enough to manage any load.

Agile Development: Follow an agile development methodology to incorporate changes to the data warehouse ecosystem. Start small and expand your warehouse in iterations.

Benefits of Data Warehousing

Some key data warehouse benefits for organizations include:

Improved Data Quality: A data warehouse provides better quality by gathering data from various sources into a centralized storage after cleansing and standardizing.

Cost Reduction: A data warehouse reduces operational costs by integrating data sources into a single repository, thus saving data storage space and separate infrastructure costs.

Improved Decision Making: A data warehouse supports BI functions like data mining, visualization, and reporting. It also supports advanced functions like AI-based predictive analytics for data-driven decisions about marketing campaigns, supply chains, etc.

Challenges of Data Warehousing

Some of the most notable challenges that occur while constructing a data warehouse are as follows:

Data Security: A data warehouse contains sensitive information, making it vulnerable to cyber-attacks.

Large Data Volumes: Managing and processing big data is complex. Achieving low latency throughout the data pipeline is a significant challenge.

Alignment with Business Requirements: Every organization has different data needs. Hence, there is no one-size-fits-all data warehouse solution. Organizations must align their warehouse design with their business needs to reduce the chances of failure.

To read more content related to data, artificial intelligence, and machine learning, visit Unite AI.

[123albert]

Confused between Data Warehouse and Data Mart? This detailed comparison guide provides insights into their objectives, models, sizes, and data handling approaches, helping you choose the right solution.

[garymdm]

Data Lake vs Data Cesspool

Data Lake vs Data Cesspool - The value of #DataGovernance for #BigData

Visual Linage and Context for Hadoop analytics and integration Andrew C. Oliver’s (@acoliver) recent post “How to create a data lake for fun and profit”  is an interesting take on the value of a data lake – an unstructured data warehouse where you pull in all your different sources into one large “pool” of data.  Schema-on-Read In contrast to data marts and warehouses, a data lake doesn’t…

[mytechnoinfo]

Confused between Data Warehouse and Data Mart? This detailed comparison guide provides insights into their objectives, models, sizes, and data handling approaches, helping you choose the right solution.

[rajaniesh]

What is Databricks Lakehouse and why you should care

What is Databricks Lakehouse and why you should care

In recent times, Databricks has created lots of buzz in the industry. Databricks lays out the strong foundation of Data engineering, AI & ML, and streaming capabilities under one umbrella. Databricks Lakehouse is essential for a large enterprise that wants to simplify the data estate without vendor lock-in. In this blog, we will learn what Databricks lakehouse is and why it is important to…

[analyticssteps]

A data lake is a consolidated repository for accumulating all the structured and unstructured data at a large scale or small scale. Talking about buzzwords today regarding data management, and listing here is Data Lakes, and Data Warehouse, what are they, why and where to deploy them. So, in this blog, we will unpack their definition, key differences, and what we see in the near future.  

[quietwitnessofhisgrace]

How Data virtualization Helps Organizations Succeed

Every business organization wants to opt for well-governed and consistent data that is easy to use and access. It provides the prerequisite opportunity to the business enterprise to explore the data for insights successfully and easily. It offers data as a service, and reports in real-time, control the different digital operations. You should remember that business enterprises adopt myriad different strategies for putting the data house in order. It is inclusive of the data marts, data warehouses, ETL, big data, and different cloud data lakes.

However, older techniques are not sufficient to confer the accessibility and agility, as required by the digital businesses. Data virtualization solutions is worth mentioning as it helps in resolving the challenge. As you try to use data virtualization, you will generate the modern data integration layer that allows you to deliver the data within the business-relevant option.

Hence, you can make the best use of the latest data of different business users from various distributed sources of data. Besides this, you can free the users of the business to apply the data from a variety of applications and analytics. As you go through this article, you will be capable of understanding how Data virtualization will be useful to a business organization in getting success:

Data virtualization helps in saving money.

The data virtualization platforms provide ample opportunity. It is not going to resolve the issue of data harvesting. It is another issue which helps in saving an ample amount of money. Such types of information are useful to the business organization in keeping extra money within an exclusive budget. But, you should think of different ways in which you will hemorrhage the money, owing to the fragmented ways in which you will store the data.

There are a plethora of employees who might give up before the execution of different comprehensive searches. On the other hand, a few of them might make use of different substandard workarounds. In addition to this, there are people capable of patching different kinds of protocol, who will release the complete search. It is going to waste an ample amount of time and money at the same time. Data virtualization is worth mentioning in this regard as it helps in resolving the unnecessary and frustrating issues of the past.

Using better analytics

It is not the best option to think of the different types of searches. It is not possible for such kind of software to pull it off. In place of this, you should consider the analytics, which are made available to you. Better analytics is derived from the compilation of the data at the starting of the process. Every time information gets created in the company; you should ensure that it is marked immediately for the business enterprise. It is accomplished by such kind of software as you require performing the analytics for specific reasons. 

Organization of the latest data

Do you think, what you should do about the kind of data so that the analytics will get its hold? As you integrate it virtually, you are going to have it in a single place. Previously, it was challenging for business enterprises to do it. Business organizations make the right use of the Extract, Transform and Load or ETL technique to accomplish this. As you are doing this, you are sure to know about the shortcomings. The primary one is the transformation of the extracted data and loading the same.

Choosing the correct platform

In case you are selling on virtualization, the next step involves investing in the platform. While there are a plethora of options involved, you can be ensured that it is easier said. All you need to do is look at different kinds of consumers and providers, as far as the data is concerned. You should be capable of integrating different DB2 applications and confer real-time performance.

According to Gartner, the majority of the big projects encounter failure, whereas 70 percent of the projects related to Big Data are not that profitable. So, it can be said that big data projects can be challenging. No single reason is responsible for such kind of situation. Also, it is complicated to use different processing technologies and big data storage. The technology seems to be new for the majority of IT specialists. Big data projects are beneficial for the wrong use cases. In spite of such disappointing results, business enterprises are known to initiate big data projects to reap the business's potential benefits.  

Data virtualization is useful in the simplification of big data projects. Though it will not resolve different issues, you can be ensured that it is deployed for the different right use cases. So, it enhances the chances for the success of big data projects. A huge data amount gets stored within the plain files. Owing to this, it becomes challenging for different non-tech-savvy users of the business to get access to it.

You should remember that different data virtualization servers are capable of hiding such complexity. So, the audience of a larger business can ensure data availability to different BI tools. They make the right use of the virtual table for the encapsulation of big data. In business enterprises, it is possible that big data is generated across the world remotely.

Data is produced in different manufacturing plants, factories, and stores. The total amount of data, which is generated by every remote site, is excessive so that it is copied to the central location for different analytical and reporting objectives.

Summary

Data virtualization has become an integral part of business enterprises as it is useful in streamlining different challenges, which affect the management of organizational data for several years. Data virtualization helps in establishing the guidelines, which state that the data virtualization platform is useful to seek access to different data vs. traditional techniques.

[techcrunchappcom]

New Post has been published on https://techcrunchapp.com/tech-mn-faq-friday-data-lakes/

tech.mn – FAQ Friday — Data Lakes

Welcome to our latest FAQ Friday — data lakes FAQ — where industry experts answer your burning technology and startup questions. We’ve gathered top Minnesota authorities on topics from software development to accounting to talent acquisition and everything in between. Check in each week, and submit your questions here.

This week’s FAQ Friday is sponsored by Coherent Solutions. Coherent Solutions is a software product development and consulting company that solves customer business problems by bringing together global expertise, innovation, and creativity. The business helps companies tap into the technology expertise and operational efficiencies made possible by their global delivery model.

Meet Our FAQ Expert

Max Belov, CTO of Coherent Solutions

Max Belov

Max Belov has been with Coherent Solutions since 1998 and became CTO in 2001. He is an accomplished architect and an expert in distributed systems design and implementation. He’s responsible for guiding the strategic direction of the company’s technology services, which include custom software development, data services, DevOps & cloud, quality assurance, and Salesforce.

Max also heads innovation initiatives within Coherent’s R&D lab to develop emerging technology solutions. These initiatives provide customers with top notch technology solutions IoT, blockchain, and AI, among others. Find out more about these solutions and view client videos on the Coherent Solutions YouTube channel.

Max holds a master’s degree in Theoretical Computer Science from Moscow State University. When he isn’t working, he enjoys spending time with his family, on a racetrack, and playing competitive team handball.

Let’s start simple — What are data lakes? What is data warehouse?

Data lakes are centralized data repositories that are capable of securely storing large amounts of data in a variety of its native formats. It allows to consumers to search for relevant data in the repository and query it by defining the structure that makes sense at the time of use. In simple terms, we don’t really care what format data has when we capture and store it. Format only becomes relevant when we start to analyze the data and can therefore use the same source data for new types of analysis as the need arises. There are a variety of tools and techniques one can use to implement efficient data lakes.

A data warehouse is similar to data lakes in that it is also capable of storing and making available for analysis large volumes of data. However, this is where similarities end. A data warehouse typically has stricter data architecture and design that needs to be defined before you start populating it with data. A data warehouse uses relational representation of your data and the data in the repository needs to be structured according to how you are planning to use it in the future. While feeding data from your data warehouse into purpose-built data marts may add flexibility to the solution, a significant re-architecture effort may be required to add additional data types to a data warehouse or to support new types of data analysis.

A data warehouse can be used to complement a data lake. You would land data in a data lake, perform initial analysis, and then send the data to a data warehouse designed for a certain business or data domain.

Here is an easy comparison between data lake and data warehouse.

  Data Warehouse Data Lake Data Regional Data Structure, Semi-structured, Unstructured data Data Quality Highly curated data, source of truth Raw data Schema Most often designed prior to implementation (Schema-on-write) Defined at the time of analysis (schema-on-read) Users Business users, data developers Data scientists, data developers, data engineers, data architects Usage Reporting, Business Intelligence, Visualization Exploratory Analysis, Discovery, Machine Learning, Profiling

How do you know if your business is right for data lake or data warehouse, and how can it be a benefit?

A data lake architecture enables organizations to handle ever increasing data volumes, varieties, and velocities, while continuing to provide security, ability to consistently process and govern the data. A single repository can then service many different types of analytics workloads such as visualizations, dashboards, and machine learning.

A data lake enables the business to introduce additional use cases for the data without impacting existing ones. It also provides separation between storage and compute thus ensuring that different applications that consume the data will have minimal impact on each other.

So, what does a successful implementation of a data lake looks like?

There are five key pillars of a successful data lake solution:

Data Ingestion/Processing Mechanism: Proper selection allows you to properly support expected volume of the data and its velocity (how much data you have to begin with and how quickly the new data is coming in).

Data Catalog: This is what keeps your data lake a lake, not a swamp. It provides the metadata describing the content of your data lake — the meaning of various data within it.

Data Storage: Your data lake is not a single centralized storage bucket. There’s a logical and physical structure that helps you break data by your processing lifecycle (raw vs cleansed), by the type of a source system it is coming from, by how you intend to use it (since its final data format and representation may vary), and also depending on the analysis you are trying to perform, etc.

Data Lake Governance and Platform Services: This is the glue that holds everything together starting from infrastructure management (provisioning, monitoring, scheduling), data quality (ensuring provided data is a reliable fit for the intended purpose within the enterprise) and data lineage (understanding where the data is coming from to data security, how it changes/evolves as it moves from the source into the data lake and through data lake) to data security (controlling data access and preventing breaches through implementing appropriate network and access control mechanisms, and end-to-end encryption of the data).

Data Exploration and Visualization: You should define which groups within the company are going to be the consumers of data from the data lake and carefully examine their real (not just declared) needs, consumption scenarios, analytical proficiency and currently used toolset. Proper selection of Data Exploration and Visualization component is the key to user adoption and therefore the success of the overall endeavor.

From the implementation perspective, there are some key decisions that need to be made.

Where you are going to host your data? This can be within your own data center or within one of the public cloud providers. The actual technical solution you develop may be portable across different providers, but once you deploy it and start accumulating data, you will be locked in since migrating large amounts of data from one platform to another may prove to be a very expensive proposition if you decide to change providers.

What data storage technology will you use? Choosing optimal solutions will help you balance durability and scale data access throughput, security (access audit, encryption at rest and transit), and cost efficiency.

Hungry for more information on Data Services? Visit Coherent Solutions.

Still curious? Ask Max and the Coherent Solutions team questions on Twitter at @CoherentTweets.

Don’t stop learning! Get the scoop on a ton of valuable topics from Max Belov and Coherent Solutions in our FAQ Friday archive.

FAQ Friday — Digital Apps

FAQ Friday — eCommerce

FAQ Friday – Security and Working Remotely

FAQ Friday – Machine Learning

[halfofthetechtk-blog]

DATAWAREHOUSE VS. DATAMART

Meet the primary contrasts amongst Datawarehouse and Datamart, two extremely helpful instruments for organizations in the advancement of Business Intelligence.

An essential inside the Business Intelligence idea is information mining, otherwise called Data Mining. This alludes to the way toward investigating a lot of information searching for examples or patterns that give light on the information conduct in a given setting. To complete this procedure, organizations require specific apparatuses, for example, Data Warehouse and Data Mart. At that point know the attributes of both apparatuses.

Information WAREHOUSE:

This is where every one of the information is put away in an organization. It comprises of a PC framework with an extensive stockpiling limit, fundamental to assemble and sort out data from various offices in the association.

Information MARTS:

This device deals with putting away data of a particular office or gathering work. It fills in as a use of information stockroom or an option for medium size organizations that can not bear the cost of the expenses of executing such an extensive information stockpiling framework. The Data Marts can be reliant or autonomous of the Data Warehouse. In any case, it is vital that have isolate frameworks that are not coordinated with each other can be troublesome administration assignments and support.

[syntellisolutionsinc]

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.

[CASE STUDY] A Single Version of Truth Database

Learn how this global tool manufacturer was able to implement a data governance solution that helped resolve their issue of reporting different numbers for the same account. Read More.

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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[kristinsimmons]

Consider this Speculative Scenario on WMT-HUM

By TORY WOLFF

WMT is in talks with HUM about a relationship enhancement, possibly an acquisition. The two already know how to work together in alliances (narrow pharmacy network, marketing collaborations, points programs). If a new structure is needed, WMT and HUM must be considering a major expansion of scope or a set of operating models where contributions are difficult to attribute and reward (e.g. joint asset builds). What is on their minds? Beyond any interim incremental moves, what could be the endgame?

Catching convergence fever

Horizontal combinations among the top five health plans have arguably reached the regulatory “permissible envelope.” But provider combinations continue apace, enhancing ability to execute on value-based care to be sure, but also increasing negotiation leverage relative to payers. Further, AMZN’s interest in healthcare is gaining momentum but the specific goals are still mysterious, leaving many incumbents to imagine red laser dots are on their foreheads.

Accordingly, health plans are seeking defensible terrain in convergence combinations: CVS-AET, CI-ESRX, Anthem’s PBM insourcing and growing attention to CareMore (UNH has been ahead of the curve as usual: but their recent SCA and DaVita medical group acquisitions have clarified for the market the scope of its ambitions for OptumCare). Of course, each of these moves just contributes to the uncertainty about the new competitive paradigm, driving more land grabs in response. I view the WMT-HUM discussions as part of these developments.

WMT as a “some assembly required�� care delivery platform

WMT has a 4,500 site network covering over 90% of the US population2. Its stores – most with enormous re-deployable space and easy accessibility – and, increasingly, its websites, are fixtures into the weekly routine of many (especially older) Americans.

So far, however, its healthcare experience has been mixed. Clinics have been a disappointment, handicapped by a combination of excessive ambition, poor strategy and erratic commitment (see note at the end). At this point, Wal-Mart owns clinics in just 19 publicized locations and a shrinking number of leased sites.

On the other hand, pharmacy performance has been strong. One key factor behind that success has been a long-term partnership with HUM including a joint narrow network Medicare PDP product with 2.4M members. Multiple collaborations since the alliance launch have built a strong overlap of customers: more than half of HUM’s Medicare Advantage (MA) lives are in counties which have above average per capita WMT store density, a much larger share than other major plans (see exhibit where width of the columns represents the plans’ share of MA lives and vertical bars show how each plan’s lives are allocated based on WMT network density in their county).

In addition, WMT is a major innovator in healthcare procurement for its own employees — especially regarding national Centers of Excellence. Since 2012, WMT employees can go to selected top providers in the country for cardiac, spine, knee and hip surgery and oncology.

  HUM as a “just add clinics” kit for a vertical model

As a largely mono-line insurer with a nationally distributed membership, HUM uses two approaches to mitigate its lack of the scale and local density of competitors.

First, HUM cultivates strong member relations with direct marketing (honed through years of competing against the more widely recognized Blues and AARP brands) and an obsessive, metrics-driven culture of consumer experience excellence. The resulting trusting member relationships support retention as well as high scores on the patient experience portion of Medicare Advantage (MA) Stars ratings.

Second, HUM makes it easy and rewarding to partner on value. This is reflected in wellness, where the Humana Vitality makes heavy use of points, ecosystem integration (e.g. fitness trackers) and brand-borrowing to create engagement (Humana Vitality). More importantly, it is also reflected in its approach to providers: HUM combines rewarding value-based contracting with enablement (e.g. Carehub data warehouse, Transcend analytics), integration (HIEs) and coordinated ancillary care to make it easy and attractive for providers to partner on Stars, risk adjustment and value. HUM ownership of ancillary care where competitive scale is achievable (PBM, Humana@Home now enhanced with the Kindred acquisition) enables tight focus on HUM’s care management strategy, full exploitation of and more touch points with members to reinforce that trusting relationship.

The model of surrounding third party providers with a supporting ecosystem can work well, as long as there are third party providers not distracted by being part of big systems with their own agenda or by super-scaled health plans insisting on more attention to their needs or just buying them up altogether. Convergence raises the specter of this vulnerability. HUM has been investing in provider clinics in FL and now carefully expanding to other markets where it has a critical mass of lives (TX) under the branding Conviva. The pacing has been cautious (not unexpected given the Concentra misstep5, the fear of competing with provider partners and the challenge of competing with other acquirers) and HUM’s current system of 195 Conviva sites6 is a long way from being able to support their plan members

Given these starting points, what might WMT and HUM do together?

Scenario: WMT builds a national clinic; HUM reinvigorates its commercial plans

Suppose WMT and HUM undertook a four step collaboration:

First, bulk up HUM’s commercial book by gradually transitioning WMT lives to HUM administration (pacing the transition to ensure HUM gains from the incremental rate leverage – and that WMT does not lose – and allowing HUM to scale up commercial capabilities as needed). The added heft will increase HUM’s leverage vs. third party providers (having commercial rates – not just Stars and risk adjustment bonuses to attract attention) and a platform for turning around its commercial and TPA business.

Second, expand WMT clinic presence to a national network using HUM’s MA lives, WMT employees and, perhaps, Medicare FFS patients who get their drugs from WMT to provide a critical mass of patients. HUM can continue to grow Conviva hub clinic locations outside of the stores to avoid pigeon-holing in the minds of consumers, but the stores provide foot traffic, overhead sharing and, above all, ready-to-go locations. Building more or less from scratch allows the care delivery system to exploit the latest in teaming models (plenty of physician extenders) and technology (esp. telemedicine).

Third, embed WMT’s Center of Excellence models into HUM health plans. Even if the current impact of these models is not material (something I doubt), they can blunt the pain of narrow networks (with access to nationally recognized brands) and high deductible designs (by offering rich coverage if the Center of Excellence path is chosen). As clinical strategies increasingly shift towards precision medicine, there is an argument that Center of Excellences will become increasingly part of diagnostics and treatment recommendations and a HUM product can be ahead of the curve. Conviva could also structure its clinical model to provide coordinated care before and after the Center of Excellence episode, reducing further the frictions of medical tourism.

Finally, selectively expand ambulatory care capabilities in rural markets to ensure alternatives are available. Rural markets are known to be WMT strongholds but also regions of provider shortage with healthcare economics trends reducing that availability further. At the same time, the art of the possible in ambulatory or low-acuity locations (e.g. micro-hospitals) is growing. WMT could be well positioned to fill the in the gap by selectively expand services (infusions, ASCs, etc.) to either fill gaps or create alternatives if the local provider system lacks competition.

By putting all this in place, HUM would be much better positioned to defend its existing business vs. other emerging convergence models and provider consolidation, reinvigorate its declining commercial business with additional scale (e.g. in pharmacy) and a very differentiated offering, and, finally, obtain enhanced relationships with the leading provider systems in the country. WMT would have a national healthcare delivery business, further enhance the destination value of its stores, and many more touchpoints to build consumer relationships.

That’s a lot of equity value. Hard to see how to accomplish all that in an alliance, easier to see how an acquisition would be best.

Implications

Of course, I am not sure how well this scenario reflects WMT-HUM’s thinking but (to paraphrase the historian Michael Howard) the purpose of scenario planning is not to get the future right, but to prevent strategy from being terribly wrong. At a minimum, WMT-HUM has an option to mitigate CVS-AET integration plays or counter UNH if it starts taking active steps to use OptumCare to preferentially advance its plan business.

If WMT-HUM do proceed along these lines, here are a few implications for incumbents:

The strategy of local consolidation and system building around hospital anchors is already facing the OptumCare threat (hollowing out tertiary inpatient economics). If WMT-HUM pursue the proposed scenario, provider systems will face another ambulatory-based competitor potentially going after some of the same economics.

Besides attacking the tertiary inpatient “flanks”, WMT-HUM could also create a threat “from above” to complex care: national-grade competition. Center of Excellence strategies offer an arbitrage on the wide variability of care quality. Local consolidation can reduce variability in clinical practice but not necessarily to a better average set of outcomes. Transparency and cost sharing will encourage patients to ask more questions. The science is progressing too fast for everyone to keep up and technology is reducing the friction of distance. It may not be WMT-HUM, but someone is going to figure out how to make this work and the right model to get consumers to accept it.

HUM’s moribund commercial business could see a renaissance with better rates (thanks to leverage from incremental WMT employees), a network geared towards store clinics and physician extender teams, and a Center of Excellence differentiation (hard for competitors to replicate because of second-order effects on network relationships).

Finally, this scenario does not necessarily put WMT-HUM on a collision course with AMZN. AMZN’s best long-term play is to create better performing healthcare markets. This WMT-HUM model could plug in nicely to either the healthcare Orbitz (B2C) or healthcare Alibaba (B2B) models for AMZN’s plays. When two potential entrants as savvy and well-resourced as AMZN and WMT can play well together, watch out!

Tory Wolff is managing partner at Recon Strategy.

Consider this Speculative Scenario on WMT-HUM published first on https://wittooth.tumblr.com/

[thebazone]

Data - Tiny Word - BIG Impact

At The BA Zone we like to stress the importance of user-centered design. Today, I'd like to focus on data - a tiny word - that can have significant impact on all aspects of an organization, such as:  architecture, database and software design, reporting, marketing, sales, processing, compliance, and a host of other areas, all of which can lead to success or failure.

From a broader perspective, data is aggregated, analyzed, manipulated, and used for advancement across a wide spectrum of organizations and industries, such as:

Consumer-focused products

eCommerce

Healthcare

Science and Engineering

Law Enforcement

Education

Entertainment

From punch cards to self-evolving, artificial neural networks that are capable of learning based on new data inputs - data has moved from supporting business to driving business. As a BA, you need a clear, concise understanding of data concepts, structures, mapping, and analysis in order to:

Enable you to ask the right questions

Identify business results that are attainable or require process changes

Understand the impact of adding or removing data

Ensure data integrity and security

Create conceptual and logical data models

Migrate applications/databases obtained through business acquisitions

Empower users with the right information at the right time

Data Basics

There are numerous books and articles (in print and online) that cover data and database creation and management. Let's dip a toe in the water and start by reviewing some basic data concepts (just a few for now):

Primary Data vs. Derived Data

Primary Data - is data that is entered into a system manually or via file feeds.

Derived Data - is data that results from taking two or more primary data elements and applying some type of calculation, algorithm, logic, etc., the result of which is called a derived data element.

De-identified Data vs. Identified Data

De-identification of data is the process of protecting the identify of individuals and their personal information by removing or masking identifying data such as name, date of birth, social security number, etc. By using a unique identifier, data can later be re-identified. For example, healthcare organizations de-identify customer information based on the Health Insurance Portability and Accountability Act (HIPAA), designed to protect patient medical records by providing privacy compliance standards. 

Identified data is data where and individual's personal, identifying information is not masked or removed and, therefore, viewable to everyone with access to that information.

Batch Processing vs. Real-Time Processing

Two methods of handling, processing, and ingesting data into a database include: Batch Processing and Real-time/Near-time Processing. Many systems today employ both methods.

Batch Processing - takes datasets, file feeds, etc. and breaks them down into batch jobs that are scheduled for automatic processing and input into the database. These jobs often include operations that perform validation and modification of incoming data, formatting data, and the handling of bad data, just to name a few.

Real-time or Near-time processing - reflects data that is processed immediately in real-time or as near to real-time (e.g. near-time) as possible.

In general, data is collectively stored in structured tables (entities), columns (attributes) and rows (records) within databases and updated depending on multiple variables/operations. Some basic operations that can be performed on data include:  create, update, delete, read-only (sometimes called - reference), and query.

Types of Databases

There are different types of databases based on function. Here are a few of the most commonly used database types:

Operational/Transactional Databases

As its name implies, an operational/transactional database is the real-time, database-of-record that manages and stores data elements from the day-to-day operational processes and/or transactions of an organization. For example, that Mystery Science Theater 3000 (MST3K) video-on-demand you purchased from RiffTrax - what was it again? Oh right, The Crawling Eye, (you have to see it to believe it) would be considered a transaction. In general, these databases reflect current events/data elements along with control data (e.g. flags, counters, etc.) and are designed for fast retrieval/update of data and usually provide minimal reporting.

Data Warehouses and Data Marts

Data Warehouses are, typically, integrated with one or more operational/transactional databases to manage and store multiple versions of events and data elements, creating a historical view and audit trail. For example, detailed, multiple, hospital admissions for a patient over a period of time. Data Warehouses can support high volume, analytical processing, and reporting capabilities. Whereas, data warehouses generally store information at the enterprise level, Data Marts are subsets of data warehouses and provide targeted information. For example, Data Marts can provide client specific dashboard views of information.

Distributed Databases

Distributed databases are individual databases or portions of a database stored in multiple physical locations that are synchronized by a centralized, software system called a distributed database management system (DDBMS). This type of database might be used in a company that has multiple branches or offices, etc. Although physically separated, to users this type of database looks like a single database.

Database Management System (DBMS)

A database management system (DBMS) is software designed to enable users to manage data in a database. Typically managed by a database administrator (DBA), using a query language, the DBMS software interacts with the database to: insert, remove, modify, validate, and retrieve data. The software also provides protection of the database through secure access and recovery measures from user/hardware failures.

How organizations structure their databases is dependent on multiple variables. These variables can be revealed using data modeling techniques.

Data Modeling

A data model is a visual representation of an organization's data that allows database designers, developers, and end users to understand and agree on the organization and manipulation/integrity of data, the relationships between data and any constraints on the data. Data models can be grouped into three levels:

Conceptual Model (also known as a Data Model) - is a high-level representation of the type of information an organization needs, primarily focusing on entities (e.g. person, place, thing, process, event, etc., which you can collect data on) and their relationships and constraints.

Logical Model - takes the conceptual/data model a step further by adding as much detail as possible (e.g. attributes/details of entities and detailed relationships) and then layers in some of the technical aspects of implementation without detailing the physical database structure.

Physical Model - specifies database design, structure, tables, columns, rows, constraints, keys (primary/foreign), implementation, and physical storage, etc.

As a Business Analyst, you might be asked to elicit and provide the Conceptual and/or Logical models. As you create these models, focus on capturing a clear, detailed model, free of ambiguity that communicates everyone's understanding of the data.

Types of Database Models 

There are a variety of data models (also called, data structures and data schema) out there.  Here's a high-level look at some (not all) of these models.

Hierarchical Database Model

The Hierarchical database model organizes data into a tree-like, linear structure with a parent-child relationship where each child (record) has a single parent (table). This model states that each child (record) must have only one parent (table), but each parent (table) can have one to many children (records). In order for data to be retrieved from this type of model, it must begin at the parent (or root node).

Relational Database Model

The Relational database model, currently the most widely used database model, logically organizes data as independent tables with each table assigned a key field that connects it to data in one or more other tables. Retrieval of data from this type of model uses these keys - a Primary Key (PK) and a Foreign Key (FK) -  to create a relationship between the tables and quickly access data.

Network Database Model

The Network database model expands the Hierarchical Model from a one parent - many children construct to a model that employs records and sets, where a set consists of one parent record and one or more child records, allowing a record type to be a child of more than one set. This model allows retrieval of data by navigating through these individual sets instances.

Object-Oriented Database Model

The Object-Oriented database model (also called Object Database Management Systems - ODBMS) are databases that store their contents as objects. Objects contain data and executable code in the form of Attributes (data that defines the characteristics of an object) and Methods (the behavior of an object). Classes are used as a template to define the data and methods within the object. This type of model can be used to address complex data/complex data relationships.

Entity-Relationship Database Model

In the Entity-Relationship database model (also referred to as an Entity-Relationship Diagram (ERD), data is defined as Entities (people, places, things, processes) and Attributes (characteristics of an entity), which together make up their domain. The relationships (cardinality) between entities states how may rows in one entity will match rows in another entity (e.g. one-to-one, one-to-many, many-to-many, etc.).

Document Database Model

A Document database model (also referred to as a document store or document-oriented database) is a software program that stores, retrieves and manages document-oriented information (e.g. Microsoft Word, PDF, XML, etc.) that is semi-structured in nature. Instead of employing a table format (rows and columns, etc.), each document can have the same or different structure. To support this design, documents are grouped into Collections, which can then be queried (searched) for documents with certain attributes.

Entity-Attribute-Value Database Model

An Entity-Attribute-Value (EAV) database model (also referred to as an Object-Attribute-Value Model, vertical database model and open schema), is one where the Attribute and Value pair to describe one attribute of a given entity. Often used where the number of attributes could be limitless, but the number that will actually apply to a given entity is relatively modest. For example, a supermarket carries a limitless amount of products (that are being added, changed, updated, etc.). Each product (e.g. entity) might specify, size, weight, unit price, etc. (e.g. attributes) and each attribute may contain a price (e.g. value). From the limitless amount of potential products available, a customer may only purchase a modest bag of groceries.

Star Schema Database Model

In a Star Schema database model, data is organized into two categories:  Facts (events) surrounded by Dimensions (reference information about the Facts) that when diagrammed resemble the shape of a star. The relationships between Fact and Dimension tables are handled using keys. Each Dimension table is assigned a unique primary key. Each Fact table contains the unique primary key of each associated Dimension table and stores it as a foreign key. Considered the simplest database model, Star Schemas are commonly used in data marts and data warehouses.

Data - Byte By Byte

If you were not familiar with the data side of business analysis, you now have a lot to digest! Consider the information covered in this blog your appetizer portion. For the novice, data is best served one byte at time. Bon Appetite!

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How to design Start Schema, Snow Flake Bus Architecture Sample Data Models

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