Data Quality Management: Growing Role in Big Data Initiatives

What Is Data Quality Management?

The term, data quality management, refers to implementing a systematic framework to produce more accurate, precise, valid, complete, and reliable data through continuous profiling of data sources, verification of information quality, and execution of multiple processes to eliminate data quality errors. Data quality management varies from company to company because each business has a unique set of needs and expectations for its data.

Factors like firm size, sources involved, dataset size, etc., all have a role in determining the types of people you'll need to manage data quality, the metrics you'll need to measure it, and the data quality processes you'll need to employ. In this article, we'll go through quality data implementation and management fundamentals to help you figure out how to adopt these practices at your own business to meet your unique needs.

Features of Data Quality Management

The reliability of your data can be increased with the help of a system that employs several aspects that successful data quality programmes use.

First, data cleansing aids in eliminating duplicates, inconsistencies, and obscurities in the data. To gain insights from your data sets, you need to apply the norms of data standardization, which is what cleansing does. With this method, you can tailor data to your specific requirements by establishing data hierarchies and defining reference data.

Monitoring and cleaning data (data profiling) is used to check numbers against norms, find connections, and confirm records against descriptions. Inconsistencies in your data can be found, understood, and possibly exposed with the help of patterns established by data profiling procedures.

A business can take preventative measures against the damage caused by low-quality data by validating business rules and building a business lexicon and lineage. Specifically, this calls for the documentation and specification of translations of business terms from one system to another. Additionally, data can be checked for accuracy using predefined statistical criteria or user-defined guidelines.

A consolidated picture of enterprise activity via a data management interface is another essential element that can help simplify the process.

Need of Data Quality Management

Managing the quality of your data is a crucial step in deriving actionable insights that can boost your business's bottom line.

In the first place, all other company endeavors can benefit significantly from solid data quality management. The use of inaccurate or outdated information might result in disastrous outcomes. A data quality management programme should be implemented to ensure consistently high quality across the entire organization.

Secondly, having accurate and up-to-date data quality management tools allows you to have faith in the applications that use that data further upstream and downstream. Managing data quality helps save money by preventing waste. Costly blunders and oversights, including losing track of orders or spending, can result from poor quality. By establishing a solid grip on your data, which is made possible by data quality management, you can gain insight into your business and its costs.

Building a Data Quality Management Framework

Ensuring data quality doesn’t have to be the ad-hoc activity it is for many companies. The right framework can help any company wrap their arms around the complexity of the data problem and create a manageable process to safeguard the integrity of this most precious asset. The following overview gives a simple and workable Data Quality Management framework.

• Create a Role-Based Organizational Structure

The first step for good DQM is to define the critical roles within the IT group. They include DQM program managers (focused on establishing data quality requirements, managing day-to-day data measurement tasks, team scheduling, and budget management); change managers (charged with managing shifting needs of data when it’s used across infrastructure and processes); data analysts (who interpret and report on the data); and data stewards (focused on turning data into a corporate asset). 

•  Build a Data Quality Definition

Defining data quality rules is essential to squeeze the most value out of data. High-quality data should include details about data integrity (how it maps to quality standards), completeness (how much of the data is being acquired), validity (how it conforms to data set values), uniqueness (how often it appears in a data set), accuracy (for each need), and consistency (data holds the same value in different sets). 

•  Profile and Audit Data

Data auditors look to validate data against metadata and other existing metrics, then report on data quality accordingly. Data profiling technology helps uncover data quality issues such as duplication and lack of consistency, accuracy, and completeness. 

•  Data Monitoring, Reporting, and Error Correction

Finally, companies must monitor and report on data exceptions, which are usually captured in business intelligence software to ensure bad data is identified before it’s used. Once bad data is uncovered, it can be corrected or de-duplicated as needed. 

The Importance of Data Quality Management for Compliance

One of the most important uses of data quality management is for business compliance to government mandates. Many companies gather and process sensitive personal and customer data, as well as private third-party and IoT data. Data privacy regulations such as GDPR require that businesses correct inaccurate or incomplete personal data, making validation a critical process. 

Data quality management ensures that a company can identify, classify, and document internal and external personal information to meet GDPR compliance, and on a broader scale to measure the completeness, accuracy, and timeliness of data. Not meeting regulatory compliance can be costly for companies as well. According to a recent report from DLA Piper, GDPR fines increased by nearly 40 percent in 2020, showing the willingness that regulators have to use their powers of compliance enforcement.

Common DQM Tools and Techniques

Once data quality rules and targets have been established, there are many tools available on the market that can help data architects and IT managers ensure a smooth DQM process. Some of the most important technology tools include: 

• Data Scrubbing.

Used to fix data errors and enhance data sets by augmenting the data with missing values, more current information, or additional records. Results can then be measured against performance targets, and any shortcomings can provide a vital starting point for the next round of data quality improvements. 

• Data Profiling.

As mentioned above, profiling tools help analyze data sources and collect metadata to identify the origin of data errors. They create data handling rules, data discovery relationships, and automated data maintenance and transformation. 

• Collaboration Tools.

Provide a shared environment and workflow where data repositories can be analyzed by data quality managers, data stewards, and change managers. 

Measuring Data Quality

• Accuracy: How faithfully do the numbers represent reality, or how right are they?
• Lineage: How reliable is the data's source?
• Semantic: What is the meaning of the data values?
• Structure: How well-organized are the underlying data values?
• Completeness: Do you have all the information you need?
• Consistency: Is there uniformity in the data across different repositories? Are identical records Currency: Represented with the same values?
• Timeliness: The speed with which the information is provided is an essential factor.
• Reasonableness: Are the data values of the right type and length?
• Identifiability: To what extent can we trust that each record accurately represents a single person and does not contain any erroneous or redundant information?

Quality Management System

By definition, a quality management system(QMS) is a structured approach to documenting and enforcing the policies, procedures, and responsibilities necessary to ensure consistent quality outcomes. A quality management system (QMS) may continuously improve organizational effectiveness and efficiency, which helps coordinate and direct activities to fulfil customer and regulatory requirements.

Quality Management Plan

Organize and Construct

The goals of designing and developing a quality management system are to create the quality management plan for the system, its procedures, and the strategy for putting it into action. Upper management should oversee this system development phase to guarantee that business and customer requirements are being considered.

Deploy

Breaking down the deployment process into smaller, more manageable steps and training employees on documentation, education, training tools, and KPIs will yield the best results. The use of internal networks in businesses to facilitate the introduction of quality management systems is rising.

Manage and Assess

Routine, systematic audits of the QMS are the primary means of creating control and measurement, two fundamental tenets of any quality management system. Depending on factors like size, potential danger, and environmental impact, the particulars will vary widely from one business to the next.

Critiquing and Enhancing

Examine and enhance the processes in place for dealing with audit findings. Finding out how well each method achieves its goals, sharing that information with staff, and refining existing procedures or creating new ones based on audit results are all aims.

Data Quality Management Model

Data quality processes are essential, but a data quality management model is also crucial when developing a data quality plan. These procedures are independent methods for fixing poor data quality in your records. A data quality management model is a systematic approach to preventing a decline in data quality below predetermined limits using regular and frequent monitoring, applying multiple data quality processes (in a predetermined order), and establishing appropriate preventative measures. It expands the procedure chain for managing data quality.

Data Quality Management in Healthcare

Improving Health Care Through Better Data Quality Management. Implementing data quality management in healthcare that regularly monitors data sources, checks the quality of information, and executes multiple processes to reduce data quality issues is vital for ensuring that data is correct, valid, comprehensive, and dependable.

Electronic health records (EHRs), administrative data, claims data, patient registries, health surveys, and information from clinical trials are a few types of health data available. All this information may contain a wide variety of mistakes and other problems due to poor data quality. Data quality management implements procedures to detect and remedy such issues and transform raw data into usable information.

Data Quality Metrics

We've provided five samples of data quality metrics for your perusal below.

The data-to-errors ratio keeps tabs on the percentage of the dataset that contains known errors.

An empty value in a field can be represented by the "number of empty values," which is the total number of occurrences.

The time it takes to derive value from a data set is measured by a metric called "data time-to-value." Although it is affected by several different variables, one of the primary reasons it has the potential to rise is due to improvements in quality.

The "data transformation error rate can measure the frequency with which a data transformation operation fails." Increases in data storage costs without a corresponding increase in data usage may indicate that a sizable portion of the stored data is unusable.

Data Quality Management Best Practices

Determine how poor data quality affects your company's ability to achieve its objectives and how to address this issue.

Choose a set of metrics to help you and your teams agree on what "data quality" means for your company, and use those metrics to monitor and improve data quality over time.

Consequences Of Bad Data Quality

The cost and success of your marketing initiatives The depth of your understanding of your clients If your company has poor data quality management, it could affect every facet of your business.

Consider your business acumen, decision-making prowess, and speed with which you can convert leads into sales.

Recent data published by Gartner indicates that poor data quality costs firms an annual average of $12.9 million. Not only does this mean foregone income, but it also leads to less-than-optimal decision-making, which has many indirect costs to consider.

Use Cases

If you utilize this information to target Facebook ads, you should expect to pay up to 20% more than necessary due to the duplicate and incorrect names on the list. Up to twenty percent of physical mail is lost or misdirected every year. When making phone calls, more time will be spent on unreachable or incorrect numbers. You might not believe it makes a difference if some of your email recipients end up on a "dirty list," but this will affect your open rates and other metrics. There is a $600 billion annual data problem for U.S. businesses, and these costs add up quickly.

But here's the flip side: if your data quality assessment is on point, you'll be able to do the following:

• Get Facebook leads at lower costs than your competition;
• Get more ROI from each direct mail, phone call, or email campaign you execute;
• Show C-suite executives better results, increasing the likelihood that your ad spends will be increased.

In today's digital world, having access to high-quality data determines success and failure.

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Conclusion: Big Data and Data Quality Go Hand in Hand

Big data is a key driver of improving business operations in every facet of the business. Data quality management is becoming a vital tool as big data initiatives crop up around multiple business units and customer-facing activities. Accordingly, Big Data Engineers are vital players in using data to its fullest extent to supercharge their enterprises, and to meet the growing requirements of government regulators.