#DataQualityManagement

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Elevate Enterprise Data Strategy with PiLog's Governance Tool

Enhance your enterprise data strategy with PiLog Lean Data Governance—an advanced solution built to improve data accuracy, streamline compliance, and ensure high-quality data management across systems. Perfect for businesses aiming for digital excellence.

[arnav-malhotra-blogs]

What are the benefits of availing data management services?

In today’s fast-paced business world, data is one of the most valuable assets a company can have. By availing data management services, businesses can unlock the full potential of their data, driving growth, enhancing decision-making, and ensuring security. For companies seeking top-tier data management solutions, EnFuse Solutions stands out as one of the best providers in the industry. Contact today!

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How do you select data management services?

Choosing a data management service that fits your business needs is essential for maximizing data value, improving efficiency, and gaining insights, considering complexities in storage, integration, security, and analysis. EnFuse Solutions offers comprehensive, scalable, and secure data management services, helping businesses maximize efficiency and leverage data effectively. Get in touch today!

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What is data management? Does it help your business to improve?

In today’s data-driven world, effective data management is not just a nice-to-have; it’s a necessity for any business that wants to thrive. If you want to implement or improve data management in your organization, consider partnering with a trusted expert in the field. EnFuse Solutions is one of the best data management companies in India, offering comprehensive services to help businesses harness the power of their data.

[shailesh-shetty]

How Data Management Services Are Revolutionizing Businesses In India

Data is the key to unlocking business success in today’s digital world. As businesses in India embark on their digital transformation journey, the role of data management services has become more crucial than ever. These services are revolutionizing companies in India by helping them harness the power of their data for competitive advantage and sustainable growth. With a team of experts and a track record of success, EnFuse Solutions is the partner of choice for businesses looking to revolutionize their data management practices.

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Data Quality Management is the key to turning raw data into actionable insights. Our services ensure your data is clean, complete, and up-to-date, so you can make confident decisions that drive success.

Read More: https://www.dataentryindia.biz/data-quality-services/data-quality-services-india.html

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[ltslean]

How to effectively frame the Data strategy to enhance your maintenance management?

Data contributes much to maintenance management, streamlining maintenance management operations and maximising returns. Capturing the maintenance data and using it effectively can identify and eliminate the problems caused by unnecessary steps or incorrect instructions in one go.

Read More: https://cmmssoftware.leantransitionsolutions.com/software-blogs-details/Data-strategy-enhance-your-maintenance-management

[dqlabsinc]

How AI and ML are transforming data quality management?

Introduction

In recent years the technology has become prominent, both at work and at home. Machine learning (ML) and Artificial Intelligence (AI) are evolving quickly today. Almost everyone will have some interaction with a form of AI daily. Some common examples include Siri, Google Maps, Netflix, and Social media (Facebook/Snapchat).AI and ML have popularly used buzzwords right now, often used interchangeably. Most experimentation has been geared to finding specific solutions to specific problems. Artificial Intelligence (AI) is an application in which a machine can perform human-like tasks. At the same time, Machine Learning (ML) is a system that can automatically learn and improve from experience without being directly programmed.

Data quality refers to how relevant information is for use. If information isn’t suitable, you won’t be able to make the right decisions. Data quality is determined by several factors, including; accuracy, completeness, reliability, relevance, and timeliness. If there’s a missing factor or is lower than other factors, your data quality won’t be very high. Read more about what is data quality and why is it important.

Increased data volumes have put companies under pressure to manage and control their data assets systematically. Also, standard data management practices lack sufficient scalability and cannot manage ever-increasing data volumes. Companies, therefore, need to rethink their data management. The good news is that substantial progress in artificial intelligence (AI) and machine learning (ML) through entities such as DQLabs.ai – AI/ML augmented data quality management platform, can support you in your data management activities.

How has AI and ML transformed quality management?

Automatic data capture

Besides data predictions, AI helps improve data quality by automating data entry through executing intelligent capture. This ensures all the valuable information is captured, and there are no gaps in the system.

Recognize duplicate records

Twofold entries of data can lead to outdated records that result in bad data quality. AI helps eliminate duplicate records in an organization’s database and keeps precise gold keys in the database. It is hard to identify and remove recurring entries in a big company’s repository without implementing sophisticated mechanisms. An organization can combat this by having intelligent systems that can detect and remove duplicate keys.

Detect anomalies

A small human mistake can drastically affect the utility and the quality of data in a CRM. An AI-enabled system removes defects in a system. Data quality can also be enhanced through the implementation of machine learning-based anomalies.

Third-party data inclusion

Apart from correcting and maintaining data integrity, AI can improve data quality by adding to it. Third-party organizations and governmental units can significantly add value to the quality of a management system and MDM platforms by presenting better and more complete data, contributing to precise decision making. AI makes suggestions on what to fetch from a particular set of data and the building connections in the data. When a company has detailed and clean data in one place, it has a higher chance of making informed decisions.

Fill data gaps

While many automation systems can cleanse data based on explicit programming rules, it’s almost impossible for them to fill in missing data gaps without manual intervention or plugging in additional data source feeds. However, machine learning can make calculated assessments on missing data based on its reading of the situation.

Assess relevance

On the other end of the scope of missing data, organizations often accumulate a large amount of redundant data over the years that do not have any use in a business context. Using machine learning, the system can self-teach on the data points required and those not needed. Analysis of this kind can help revamp the process and, eventually, make it simpler.

Match and validate data

Coming up with rules to match data collected from various sources can be a time-consuming process. As the number of births increases, this becomes increasingly more challenging. ML models can be trained to learn the rules and predict matches for new data. There is no restriction to the volume of data, and as a matter of fact, more data works favorably in fine-tuning the model.

The cost of bad data

Bad data can prove to be quite expensive for companies. Attempts to quantify the financial impact have resulted in some shocking numbers. It’s also important to remember that decisions based on flawed data can lead to severe consequences in some cases. Machine learning algorithms can flag some of these situations before they get too far. Financial companies use them to identify forged transactions. It’s estimated that ML models can result in a $12 billion savings for card issuers and banks.

Conclusion

Most businesses look for fast analytics with high-quality insights to deliver real-time benefits based on fast decisions. They consider this a high priority and means of competitive advantage. To enable this, there is an opportunity for organizations to fine-tune and enhance the current data quality approach using ML techniques. Many leading data quality tools and solution providers have tried out ML territory in expectation of increasing the effectiveness of their solutions. Thus, it has the chance of being a game-changer for businesses in pursuit of improved data quality. Although the current intake level of the use of ML for data quality assessment and enhancement is low, it has promising prospects to churn large data sets and enhance data quality.

If you want to try an AI and ML based data quality tool to automate all your DQ management, request DQLabs platform demo here.

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Data Quality Analyst at Experian 100% REMOTE (work anywhere!)

The primary responsibility of the Data Quality Analyst at Experian will be to support application development leveraging strong SQL skills, knowledge of relational databases, and the skills to ensure custom coordination of benefit software, containing over 400 million rows of data, only allows accurate data to be loaded, and data integrity is maintained after bug fixes and enhancements.

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[dqlabsinc]

What is data quality management?

Data is the driving force of every organization in the modern world. As  organizations continue to collect more and more data, the need to manage the quality of the data becomes more prominent each day. Data Quality Management can be defined as a set of practices undertaken by a data manager or a data organization to maintain high quality information. These set of practices are undertaken throughout the process of handling data; from acquiring it, implementation, distribution, and analysis.

This article outlines what DQM entails, its importance, and the metrics used to assess data quality measures.

Why you need Data Quality Management for your business

The proliferation of data in the digital age has presented a real challenge – data crisis. The data crisis entails low quality data in its volumes that makes it hard for the businesses to make sense out of it, and in some instances, unusable. DQM has thereby come forth and has become an important process used to make sense out of data. It aims at helping organizations point out errors in their data which need to be resolved. It also aims at assessing if the data in their systems is accurate to serve the intended purpose.

Let us outline four reasons why you need Data Quality Management;

Better functioning business

All the basic operations of a business are managed quickly and efficiently when the data has been managed properly. High quality data enhances decision making at all levels of operations and management.

Efficient use of resources

Low quality data in an organization means resources including finances are used inefficiently. When businesses maintain data quality through DQM practices saves them from wastage of resources leading to bigger and better results.

Competitive advantage

Reputation precedes every business. A business with a good reputation gains a higher competitive advantage over others. High quality data ensures that a business maintains a high reputation. Low quality data has been proven to bring about distrust from customers, leading to their dissatisfaction in a business’ products and services.

Good business leads

Creating a marketing campaign from erroneous data where the targeted customers do not exist, makes no sense. When the leads are from poor quality data, then there is no point targeting them with campaigns. Accurate customer data brings about better conversion from a better reach. Good data management initiatives therefore, must be practiced.

What are the key features of Data Quality Management?

A good DQM makes use of a system that has various features that will help in improving the trustworthiness of organizational data. Let us outline the various features of a good DQM;

Data cleansing corrects unknown data types, duplicate records, as well as substandard data representations. Data cleansing ensures that data standardization rules that are needed to enable analysis and insights from your data sets are followed. The data cleansing process also establishes hierarchies and makes data customizable to fit an organization’s unique data requirements.

Data profiling is the process of monitoring and cleansing data. Data profiling is used to;

Validate available data against the standard statistical measures,

Create data relationships

Verify the available data against matching descriptions

The data profiling process establishes trends that help in discovering, understanding and exposing inconsistencies in the data, for any corrections and adjustments.

What are the metrics that measure Data Quality?

Data quality metrics are very important in assessing the efforts made to increase the quality of your data. Data quality metrics must be top-notch and must be clearly defined. In the data quality metrics, be sure to look out for; accuracy, consistency, completeness, integrity, and timeliness. Let us discuss different categories of data quality metrics and what they hold in;

Accuracy

Data accuracy refers to the degree to which the said data accurately reflects an event or object that is described.

Completeness

Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization. Data completeness indicates if there is enough of it that can draw meaningful conclusions.

Consistency

Data consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other. However, data consistency does not necessarily imply that the data is correct.

Integrity

Also referred to as data validation, data integrity refers to structurally testing data to ensure compliance with an organization’s data procedures. Such data shows that it has no unintended errors, and that it corresponds to its appropriate data types.

Timeliness

When your data isn’t ready when users need it, it fails to fulfill the data quality dimension of timeliness.

Some examples of data metrics that help an organization to measure data quality efforts include;

The ratio of data to errors

This data metric allows tracking of the number of known errors within a data set corresponding to the actual size of the data set.

Number of empty values

This metric counts the number of times there is an empty field within a data set. Empty values usually indicate missing information or information recorded in the wrong field.

Data time-to-value

This metric evaluates how long it takes to gain meaningful insights from a data set.

Data transformation error rate

This metric will track how often a data transformation operation will fail.

Data storage costs

If an organization stores data without using it, this could be an indication that the data is of low quality. Conversely, if the organization’s data storage costs decline while the data operations stay the same or continue to grow, the quality of the data is most likely improving.

Summary

While it may look like it is a real pain to maintain high quality data, some organizations also feel like Data Quality Management is a huge hassle. This means if your organization is the one that takes the lead in making its data sound, it will automatically gain a competitive advantage in its industry.

This article details the information needed to maintain high quality data. Be sure to look out for DQLabs.ai – a leading data quality management platform to help you in keeping your organization competitive in today’s digital marketplace through Data Quality Management.

[dqlabsinc]

Continuous data quality monitoring using AI/ML with DQLabs

In today’s world, we have been doing more of the traditional data management practices. This is a process of connecting people, processes, and technologies by creating governance foundations, going into data stewardship, standardizing and setting policies, execution of master data management, data quality, and with a feedback loop. The problem is that it takes a lot of time and cost and most of the time the value is not generated.

DQLabs, however, takes a paradigm shift from this traditional approach and focuses on,

Self-service automation

Support all types of users

Automate first as much as one could

DQLabs.ai can be described as an augmented data quality platform that manages an entire data quality life cycle. With the use of ML and self-learning capabilities, DQLabs helps organizations measure, monitor, remediate and improve data quality across any type of data.

This article helps you understand how DQLabs performs data quality monitoring continuously, not just once. There are three different metrics that we capture by using a lot of other processing procedures automatically. There are three levels of measurement we do;

Data quality scores are the standard data quality indicators used to record the quality attributes of the data. Usually, most products are validated by these data quality rules or by tying different rules to different sizes and then bringing a score. DQLabs does that. However, the main difference is we don’t expect the users to manage or create any of these rules. DQLabs platform does it all automatically by a semantic classification and discovery of the data within your data sets.     For example, if you have a number data, is it a phone number, social     security number, or license number? Those are the questions we ask, and we use different types of technologies around that to identify that. Once we recognize, we automatically create all these checks we need to do across these dimensions and calculate this. We also do subjective measurements. Subjective dimensions are usually collected by customer satisfaction service or input from different users, functional stakeholders, etc., usually in a traditional world. At DQLabs, however, we have a collaborative portal that users within your organizations use. We track every type of usage that happens within that portal in terms of viewing, adding favorites, a conversation that goes across, or any remediation of data quality issues that occurs within that particular data set. That allows    this subjective way of measuring data quality metrics, so that’s a     measurement at one snapshot, but this is also done continuously.

Impact Score; we not only measure and give how many records are bad based on those checks, but we also take it to the next level of how much we can convert automatically. This is important because we no longer find insufficient data and provide tools to remedy it. We then take it to the next level of how much difference we can make automatically. This is critical in the world of data preparation, data science, engineering, or data engineering because you’re not doing it manually. It is a seamless process and measures how much of an impact we are making. This ensures you understand the bad records using a quality data score,     and you can measure what percentage of those bad records can be turned into good records. For example, if the data quality score depicts the accuracy score to be 60%, the impact score can automatically determine how much of the 40% bad score can be converted into a good score. This will happen for all the data quality checks such as completeness, consistency and accessibility in a continuous way.

The third level of scoring is called a drift level. This is primarily identifying the volatility of the data. An example of a drift level is a stock market price for a stock ticker. The cost can go up and down, and sometimes based on the data collection, it could be a system outage that may be causing a bad record or macro factors such as economic factors, which may be beyond your organization’s control. We have created another set of scores to measure the volatility of the data, and based on the strip level, which can go from none to low to medium to high. All this is done automatically out of DQLabs this is done using the statistical trending benchmarking and then using different AI/ML based algorithms etc.

Watch our on-demand webinar to learn more about the use of advanced algorithms to identify data quality issues not just once but continuously.

In conclusion, the idea of continuous data quality monitoring is to prioritize data quality first, and then move on to the process of discovering all of these metrics right away. This enables greater automation, increased ROI for organizations, and enhanced customer experiences by providing them with trustworthy data and business insights in minutes.

Interested in trying DQLabs free? Request a demo