Let’s Talk Data Quality

Introduction

Let’s kick things off with a statement – “The higher the value of your data quality, the higher the value of your business.” Yikes! That may be hard to believe, but in order to stay competitive, you need to take advantage of your data when making important business decisions. So, if that’s the case, then it only makes sense that your data needs to be as pristine as possible. And that’s why we decided to share some good practices for maintaining a level of quality that will benefit your company the most. We’ll be tapping into our past experience for this one, and we hope it’ll serve as a good reminder to keep data quality in mind when moving forward with your analytics projects. But first, let’s cover …

What is Data Quality?

As the term suggests, this is data that’s fit for its purpose or use in analytics. There is a bit more to that, though, since data quality has a few factors by which it can be measured. You may find these referred to in various ways, but they generally revolve around these five traits – accuracy, completeness, consistency, reliability, and timeliness. So, if your data is correct, comprehensive, and consistent across all systems, then you have yourself some great data to work with. But never forget that for your insights to be as meaningful as possible and for you to derive the most value out of them, your data needs to be always up-to-date.

The benefits of maintaining this level of data quality are quite enticing. For starters, it’ll be easier and faster to get analytics to your company, so you can hastily get those data-driven insights everyone’s talking about. Furthermore, you’ll experience fewer bugs and inaccuracies in your reports, which is great since they generally lead to a lack of trust in the data if left unsolved. Finally, your team will be able to focus on more valuable tasks than cleaning up data sets, which leads to an increase in productivity. If all of that sounds good to you, then read on to find out how you can achieve all of this by …

Bad Data and How to Deal With It

Companies have always struggled to establish a high level of data quality, and the truth is that it’s very challenging to have it perfect. And that’s fine since the most crucial thing for any business should be to at least get their foot in the door. Having bad data is preferable to having no data, of course, since at least something can be done about the dirty data. Let’s show you what we mean with an example.

Let’s say we’re dealing with a data set full of contact information for your employees. Every employee will have an ID associated with them along with other fields like name, address, job position, etc. So, if these IDs match up to only one employee, that’s fine, and how it should be, but if they encapsulate two or more, then you have bad data on your hands. However, that part is fixable by asking the right people and updating the information. Even if the field was something different, like office location, and had a bunch of inconsistent entries, it can still be potentially dealt with by an experienced data analyst with the help of some advanced techniques and algorithms. Yet, if that same field was empty, then there’s nothing that can be done, and if you wish to include that field in some visualizations, you won’t be getting a clear picture at all. Of course, this example doesn’t capture all cases, and if the data is quite bad, the analyst may have no other option but to send it back for revision.

In order to better deal with the bad data initially, it’s possible to utilize the above-mentioned techniques or algorithms and integrate them into your analytics solution. This way, you’ll be constantly aware of your current data quality issues and can double-check and fix them during the project development. Just check out the image below to get an idea of what we mean. If there’s something to take out from all of this, it should be to NOT wait until your data is perfect and to just start with what you have. And with that out of the way, let’s cover some tips on maintaining good data quality within your organization.

data quality

Best Practices for Maintaining Good Data Quality

  1. Set Rules – From our experience, businesses usually lack set rules for their data sets, which leads to inconsistencies within said data. So, merely setting up some guidelines will help immensely with your company’s overall data quality, and it’ll provide leverage to any data analysts that are working on those analytics projects. Just make sure to keep everyone across all departments is informed of these rules, and additionally, it’s good to have them documented.
  2. Data Governance – Once you have those guidelines figured out, you can move on to implementing a data governance strategy within your company processes and appointing a team to maintain it. This way, you’ll be sure that your data is complete, correct, and convenient for usage at all times. Furthermore, you’ll be sure that the data isn’t misused since data governance also serves to manage who has access to which information.
  3. Train your employees – When first discussing a project with a new client, it’s much smoother to gather the requirements when the contacts have a fundamental understanding of the data at hand. This includes stuff like how the data is being gathered, what the established rules are, who the data steward is, etc. Ensure everyone is aware of the importance of data quality, and you’ll see just how smoother everything will be.

Conclusion

Data-driven companies are the ones that will be competing in this new era of digitization. And in this perplexing world of data, you need to be able to rely on your insights. To do that, you have to take action and improve your data quality since that’s the only way you can accurately make key business decisions. To get that improvement, you need to utilize various good practices like setting up rules, training your employees, and implementing data governance. But most importantly, you need to use your data in order to be able to spot any issues with it. In the end, you can be sure that investing in straightening your data quality will pay off amply, and you’ll be up there competing with the top leaders.

Authors:

Alexandar Madjarov
Alexandar Madjarov
Stiliyan Neychev
Stiliyan Neychev

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