Data quality management – How to get it right?

What is data quality?  

Data quality is a data usability characteristic that means that the data set is accurate, complete, consistent, and reliable to use. A high data quality is effective to make great business decisions, driving insights from it to perform efficiently. Six DAMA data quality dimensions are accuracy, completeness, consistency, uniqueness, timeliness, and validity.   

• Accuracy - data mirrors real-world values or entities  

• Completeness – all data fields are present   

• Consistency – data is consistent across all systems   

• Uniqueness – no duplicate values or redundant data is present.  

• Timeliness – data is frequently up to date.   

• Validity -  data adheres to predefined rules, standards, and constraints  

What is data quality management?  

Data quality management is the process of making sure that data meets all the data quality dimensions and serves its intended purpose. Data quality management includes strategies, practices, and systems needed to maintain data in high quality from creation to consumption to discarding. This is what most IT and data teams strive for – to maintain and increase data quality and make it suitable for its business use cases. A typical example of data quality management is this: an eCommerce company maintains customer data for marketing campaigns stored in a database. They require high-quality, consistent, and cleaned data, readily available whenever required.   

Benefits of data quality management  

Better decision making  

Mid or poor-quality data could lead to wrong decisions and miscalculated moves. Like targeting the wrong group of audiences with offers on products they don’t even need. That's where data quality management plays a role; it ensures your teams' access reliable, relevant, and right information. So, from understanding future demands to automating marketing campaigns to making strategic decisions, your team could have the expected outcomes.  

Reduce costs  

Poor data quality could mean anything – having duplicates, redundant data, or data that’s old and no longer required. Storing them is not necessary and leads to operational and maintenance cost wastage. One doesn’t just lose money this way with poor data. There could be penalties laid for regulatory violations. And let’s not forget the impact and implications a bad decision could have on business operations. In any way, bad data quality leads to missed opportunities, poor results, decision errors, and regulation issues. For example, a financial company, due to poor integration of transactional data, could face losses in millions, which includes fraud money losses, investigation costs, and penalty for not following AML guidelines.  

Increase in productivity  

Whether it’s a data engineer or an end user, clean data and good quality have some impact over productivity. For instance, the data professional spends less time reviewing or correcting errors and reconciling discrepancies. with streamlined processes and data pipelines, they are able to focus more on strategic tasks, and not the ones that consume time and hinder productivity. Hence, data quality management is paramount for an organization that wants to reduce delays and spike up productivity.  

Data governance and compliance  

Every organization handles sensitive data – employee details, customer payment information, and the like. Protecting such data and adhering to geo-based and industrial based norms is a must, if one should avoid reputational damage and hefty fines. Data quality management (DQM) reduces the risk of running into compliance issues with standards like GDPR, HIPAA, CCPA, etc. This is a great way for an organization to show its customers, partners, and stakeholders the care it takes to safeguard sensitive information.   

Components of data quality management  

There are five data quality management components: data profiling, data cleansing, data standardization, data validation and verification, and monitoring & analysis. There are other components too, which fall under DQM – data governance, data enrichment, integration, data quality metrics, and many others.   

Data profiling  

Data profiling is a process that evaluates data quality dimensions, checking if the data is accurate, reliable, and complete. For example, checking a data set for missing values, inconsistent formats, etc. Data profiling has other roles and responsibilities, other than data quality. For example, it’s used to perform data standardization or understanding relationships between entities. Despite the multiple use cases, data profiling helps improve data quality, minimizes data errors, and saves cost and time.  There are data profiling tools, using which you can evaluate data quality, both open-source and SaaS, Open Refine, IBM InfoSphere, Talend, etc.  

Data cleansing  

Data cleaning or data cleansing is a data quality management component. It ensures that your data is free from error values, duplicates, and other inconsistencies. Consider it like filtering out liquid to remove impurities. Within data cleansing itself, there are multiple processes: merging, imputing missing data, standardization, duplicate removals, and enrichment. All of this is to make sure that your data set is clean and ready to generate reliable insights. Depending on the data set volume, the cleaning technique/tool varies. For small datasets, Excel or other statistical tools are enough. For mass data cleansing, tools and programming languages like OpenRefine, Oracle enterprise data quality, Talend, Python, SQL, and R.  

Data standardization  

Data standardization is to convert data into a uniform format. Data comes from multiple tools and sources, each with its own format. Ensuring all of them stick to the same standards and denomination are essential if you need high quality, interoperable, and consistent data. Data standardization mainly does the following:  

• Standardizing format. Example: sticking to a unified date format dd/mm/yyyy. 

• Measuring units' standardization (using pounds for weight and cm for height and similar things). 

• Naming conventions. Following a similar format for writing names. Example: first name, last name. 

• Address formatting  

• Following the same conventions for categorical values. Write ‘M’ for male, ‘F’ for female. 

Data standardization is very important if you want to ensure compliance adherence and drive accurate analytics through AI/ML use cases. 

Data validation and verification  

Data validation is similar to checking errors, to ensure that data is accurate and of high quality. Though data verification and validation look similar, both serve different purposes. Data validation is usually done to see if the data meets preset rules and standards, which one performs around data entry or migration. Examples of data validation could be checking whether a column’s values fall within a specific range.  

Data verification is rather to confirm the data consistency between source and destination, which is mostly done before or while collecting data. Both data verification and validation are necessary to achieve high data quality, prevent expensive errors, and ensure compliance. 

Monitor and analyze 

While all the above components are essential for data quality management, monitoring is super important to sustain results and improve periodically. Through regular monitoring, you could flag critical issues, and fix them before it affects the business bottom-line. It also helps in other ways - root cause analysis whenever any data quality issue happens, builds trust over data, and helps while scaling. There are real-time data quality dashboards available, which you can set up to keep tabs on data quality concerns, data metrics score, and other metrics.  

Data quality management best practices 

Ready to leverage best practices, tools, and techniques to improve your organization’s data quality? Here’s what you need to follow. 

1. Collaborate with other team members 

Ensuring high-quality data could be an IT or data team’s goal; but it’s a team activity that involves cross-functional collaboration. So, include everyone from end-users and team managers as well, so data quality becomes a part of organizational objectives. When there is a shared perspective and goal, then any data quality issue could be addressed faster.  

2. Set the rules 

Once you gather everyone to be involved, setting and defining rules, standards, and processes is the next step—for everything from data collection to processing to storage. These frameworks will remain constant and be treated as the rulebook by all stakeholders. For example, setting a standardized format for storing phone numbers. 

3. Maintain transparency 

Once you have set every rule and process, it’s time to maintain transparency and keep them all in one place. This is what brings ownership and visibility to whatever you try to establish. You could form a data catalog, which lists down transformation steps, usage, origin, and everything else. 

4. Train employees 

Educating employees becomes the next responsibility. Helping people learn and adopt data quality best practices will have a collective impact on data quality metrics. Not only that, you help your company adopt data-driven culture, where data users are stewards themselves. For starters, you could have a workshop to train employees about data, how to use it, common mistakes to avoid, and all that. 

5. Work on your metadata 

Metadata can help improve data quality in many ways. Meta data is nothing but a guideline with rules and constraints of the specific data—its format, type, accepted value range, and more. When you have a clear context about every data point, it’s easy to structure, organize, use, and share with other teams. Metadata isn’t about the discoverability and usability; the IT teams will also find it much easier to track any issue to its origin, meaning there is great data lineage.  

6. Keep on iterating and improve the process 

Set up data quality metrics and measure how much you are achieving every quarter. Only by monitoring continuously, you could know how far along are you towards ticking off the goals. Some other data quality management best practices include assigning data stewards in every team, automating data quality checks, and integrating sources to form centralized data management systems. 

Final thoughts 

High-quality data is no longer optional. It’s essential, given the amount of use cases we run every day and decisions we make, running through descriptive reports. Every year, organizations are losing an average of $12.9 million annually, according to Gartner. To avoid these obvious financial losses and drive highly effective decisions, one must think about setting up data quality management soon.

While the above data quality best practices can be commonly followed by all, data quality metrics and dimensions might vary from organization to organization. This is because the idea of data quality may depend on the industry, compliance requirements, business goals, etc. Hence, a customized approach to data quality management is what you should start with. Let’s help you with an exclusive data audit to break down what needs to be done and how for the DQM of your organization.