Data is the lifeline of our world today. Corporations, companies both large and small, government and educational institutions now rely on big data to derive valuable insights and create personalized experiences. 

But data is only useful if it is clean and can be used for its intended purpose. Unfortunately, raw data is inherently flawed data. The data we collect at the source is dirty data caused by human errors, system limitations and many other varying factors. According to IBM, dirty data costs companies $3.1 trillion per year and those costs come from the expenses companies must incur to manage the consequences of ad data – including hiring new talents, upgrading systems, reputation and brand building costs and mistakes that affect customer retention. 

Clearly, to thrive in today’s world, you must improve data quality and for that you need a robust data quality management plan. 

And here’s everything you need to know about implementing a data quality framework. 

What Is Data Quality?

Data quality refers to data that is accurate, complete, relevant, valid, timely and consistent. These are the qualities of good or usable data. For a business to rely on its data, it needs data to follow this quality benchmark. 

But what does all this mean? Here’s a quick example of each of these features for you to understand better.  

Accurate: A business user wants to know how many customers are male in their 30s. If the data recorded is accurate, the business user will get the right records. If the data is inaccurate, meaning if some fields of the “gender” or “age” column is blank, has incomplete information (such as missing year in age), then the user will not be able to generate accurate data. 

Complete: Phone number fields with incomplete city or country codes. Addresses with missing ZIP codes are all examples of incomplete data that wreaks havoc for users who want to use this data to make informed decisions. 

Relevant: The data you collect should be relevant to your fundamental purpose. For example, you want to collect data to study a working woman’s choice of a laptop brand. For this, you’ll need to collect data such as their job titles, their income range, their age etc. Irrelevant data would be information on their spouse, their children or their insurance provider. 

Validity: Data formats are important. If data is not in the right format, it is going to be a nightmare for any user to organize and analyze it. Take for example time formats – if you don’t have a valid standard to follow through, some of your data may have a 12-hour or some may have a 24-hour format. Data that doesn’t follow a defined format would be invalid. 

Timeliness: Data should be up to date and should reflect recent happenings. Outdated data leads to inaccurate results. 

Consistent: For a data set to be consistent, it must have the same versions throughout the same and different data source. Take for example buyers’ data – it must have the same information throughout. Your buyers’ names must remain the same. You cannot have nicknames or abbreviations in one data set while only first names in other data sets. Consistency is important.

While this may sound easy, it is not. For your data to achieve this quality criteria, you will need to implement a strong data quality management plan that includes optimizing your data collection process, investing in data preparation tools and having a team dedicated to managing your data quality project. 

The benefits to good data quality is quite obvious – you have insights you can rely on, you can make data-driven, informed decisions, you have better audience targeting and you can create strategic business campaigns. 

But how do you go about implementing this? Here’s what you need to do. 

Implementing A Data Quality Management Plan 

Don’t wait for a data disaster to make you take implementing a data quality management plan seriously. Until then it would be too late. Get your department heads to understand the importance of data quality and the things you will need to get this plan rolling. 

  • Evaluate the Health of Your Data: Before implementing a plan, you need to know the current health status of your data. What are the issues plaguing your data the most? Who is responsible for those issues? What can you do to improve or minimize the chances of errors in the data collection process. You can do this by using a data quality solution that allows you to discover the flaws with your data. Only when you know the problems affecting your data can you figure out the next steps to take in fixing in. 
  • Creating a Data Collection Process: Once you know the kind of issues affecting your data, you will be at a better position to make an effective plan. Say for example, you now know after a discovery exercise that your data has serious formatting and consistency issues (no capital letters in names, incorrect time formats etc), you can create a plan to address this problem by either defining data entry protocols or establishing new processes. 
  • Invest in a Data Quality Solution: Fact is, you cannot implement a data quality plan by yourself. Hiring a team to do the mundane task of preparing or cleaning is way too costly. It’s always a better idea to invest in an automated solution rather than to spend hundreds of thousands of dollars in hiring data analysts only to fix data quality instead of analyzing data. 
  • Implement Data Quality Rules: This can also be part of your digital transformation initiative where you can include new data quality plans to be followed by people interacting with your data. When implementing these rules, you will also be defining responsible personnel, the methods of data collection and the protocols they need to follow. 
  • Merging Disparate Data Sources: In an age when customers want a personalized experience, you will need to have a cohesive source of truth to achieve that. You cannot afford to have valuable customer data locked away in multiple data sources. It doesn’t make sense for marketing, sales, support, and billing to each have their own customer data set repeating the same information over and over again. Part of data quality management, therefore, focuses on combining these multiple data channels and turning it into a single source of truth, thereby, making it easier for you and your organization to benefit from your data. 

When it comes to data quality, there are no shortcuts. As the world seeks to use data to combat diseases, develop businesses and derive key opportunities, the need for quality data is more important than ever. If you’re planning for digital transformation, it starts by implementing a data quality plan. Without reliable data, you cannot achieve grander goals.