Data saturates the modern world. Data is information, information is knowledge, and knowledge is power, so data has become a form of contemporary currency, a valued commodity exchanged between participating parties.

Data helps people and organizations make more informed decisions, significantly increasing the likelihood of success. By all accounts, that seems to indicate that large amounts of data are a good thing. However, that’s not always the case. Sometimes data is incomplete, incorrect, redundant, or not applicable to the user’s needs. 

But fortunately, we have the concept of data quality to help make the job easier. So let’s explore what is data quality, including what are its characteristics and best practices, and how we can use it to make data better. 

What’s the Definition of Data Quality?

In simple terms, data quality tells us how reliable a particular set of data is and whether or not it will be good enough for a user to employ in decision-making. This quality is often measured by degrees. 

But What Is Data Quality, in Practical Terms?

Data quality measures the condition of data, relying on factors such as how useful it is to the specific purpose, completeness, accuracy, timeliness (e.g., is it up to date?), consistency, validity, and uniqueness. 

Data quality analysts are responsible for conducting data quality assessments, which involve assessing and interpreting every quality data metric. Then, the analyst creates an aggregate score reflecting the data’s overall quality and gives the organization a percentage rating that shows how accurate the data is. 

To put the definition in more direct terms, data quality indicates how good the data is and how useful it is for the task at hand. But the term also refers to planning, implementing, and controlling the activities that apply the needed quality management practices and techniques required to ensure the data is actionable and valuable to the data consumers. 

Now, let us look at data quality dimensions after you better understand what is data quality.

Data Quality Dimensions

There are six primary, or core, dimensions to data quality. These are the metrics analysts use to determine the data’s viability and its usefulness to the people who need it. 

  • Accuracy

The data must conform to actual, real-world scenarios and reflect real-world objects and events. Analysts should use verifiable sources to confirm the measure of accuracy, determined by how close the values jibe with the verified correct information sources.

  • Completeness

Completeness measures the data's ability to deliver all the mandatory values that are available successfully.

  • Consistency

Data consistency describes the data’s uniformity as it moves across applications and networks and when it comes from multiple sources. Consistency also means that the same datasets stored in different locations should be the same and not conflict. Note that consistent data can still be wrong.

  • Timeliness

Timely data is information that is readily available whenever it’s needed. This dimension also covers keeping the data current; data should undergo real-time updates to ensure that it is always available and accessible.

  • Uniqueness

Uniqueness means that no duplications or redundant information are overlapping across all the datasets. No record in the dataset exists multiple times. Analysts use data cleansing and deduplication to help address a low uniqueness score.

  • Validity

Data must be collected according to the organization’s defined business rules and parameters. The information should also conform to the correct, accepted formats, and all dataset values should fall within the proper range.   

How Do You Improve Data Quality?

People looking for ideas on how to improve data quality turn to data quality management for answers. Data quality management aims to leverage a balanced set of solutions to prevent future data quality issues and clean (and ideally eventually remove) data that fails to meet data quality KPIs (Key Performance Indicators). These actions help businesses meet their current and future objectives.

There is more to data quality than just data cleaning. With that in mind, here are the eight mandatory disciplines used to prevent data quality problems and improve data quality by cleansing the information of all bad data: 

  • Data Governance

Data governance spells out the data policies and standards that determine the required data quality KPIs and which data elements should be focused on. These standards also include what business rules must be followed to ensure data quality.

  • Data Profiling

Data profiling is a methodology employed to understand all data assets that are part of data quality management. Data profiling is crucial because many of the assets in question have been populated by many different people over the years, adhering to different standards.

  • Data Matching

Data matching technology is based on match codes used to determine if two or more bits of data describe the same real-world thing. For instance, say there’s a man named Michael Jones. A customer dataset may have separate entries for Mike Jones, Mickey Jones, Jonesy, Big Mike Jones, and Michael Jones, but they’re all describing one individual.

  • Data Quality Reporting

Information gathered from data profiling, and data matching can be used to measure data quality KPIs. Reporting also involves operating a quality issue log, which documents known data issues and any follow-up data cleansing and prevention efforts.

  • Master Data Management (MDM)

Master Data Management frameworks are great resources for preventing data quality issues. MDM frameworks deal with product master data, location master data, and party master data.

  • Customer Data Integration (CDI)

CDI involves compiling customer master data gathered via CRM applications, self-service registration sites. This information must be compiled into one source of truth.

  • Product Information Management (PIM)

Manufacturers and sellers of goods need to align their data quality KPIs with each other so that when customers order a product, it will be the same item at all stages of the supply chain. Thus, much of PIM involves creating a standardized way to receive and present product data.

  • Digital Asset Management (DAM)

Digital assets cover items like videos, text documents, images, and similar files, used alongside product data. This discipline involves ensuring that all tags are relevant and the quality of the digital assets.

Data Quality Best Practices

Data analysts who strive to improve data quality need to follow best practices to meet their objectives. Here are ten critical best practices to follow:

  • Make sure that top-level management is involved. Data analysts can resolve many data quality issues through cross-departmental participation.
  • Include data quality activity management as part of your data governance framework. The framework sets data policies and data standards, the required roles and offers a business glossary.
  • Each data quality issue raised must begin with a root cause analysis. If you don’t address the root cause of a data issue, the problem will inevitably appear again. Don’t just address the symptoms of the disease; you need to cure the disease itself.
  • Maintain a data quality issue log. Each issue needs an entry, complete with information regarding the assigned data owner, the involved data steward, the issue’s impact, the final resolution, and the timing of any necessary proceedings.
  • Fill data owner and data steward roles from your company’s business side and fill data custodian roles from either business or IT whenever possible and makes the most sense.
  • Use examples of data quality disasters to raise awareness about the importance of data quality. However, while anecdotes are great for illustrative purposes, you should rely on fact-based impact and risk analysis to justify your solutions and their required funding.
  • Your organization’s business glossary must serve as the foundation for metadata management.
  • Avoid typing in data where possible. Instead, explore cost-effective solutions for data onboarding that employ third-party data sources that provide publicly available data. This data includes items such as names, locations in general, company addresses and IDs, and in some cases, individual people. When dealing with product data, use second-party data from trading partners whenever you can.
  • When resolving data issues, make every effort to implement relevant processes and technology that stops the problems from arising as close as possible to the data onboarding point instead of depending on downstream data cleansing.
  • Establish data quality KPIs that work in tandem with the general KPIs for business performance. Data quality KPIs, sometimes called Data Quality Indicators (DQIs), can often be associated with data quality dimensions like uniqueness, completeness, and consistency.

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