The Importance of Data Hygiene in Your Analytics Strategy

I began learning to use a computer at age 8-9 whilst using a language called BASIC. Over the next few years, I learnt various languages and suites like Cobol, Fortran, Pascal, Foxpro, C, C++, Java… But one thing I learnt very early in life with respect to computing, which is still relevant in today’s world is ‘GIGO’ – Garbage In Garbage Out.

In today's data-driven world, businesses are increasingly relying on #analytics to make informed decisions. However, the #quality of the data that is used for analytics is critical. If the data is dirty or inaccurate, the insights and recommendations that are generated will be unreliable. This can lead to poor decision-making and missed opportunities.

Imagine, inaccurate responses or poorly sampled data from your Market research activity, inaccurate or incomplete data in your CRM or sales order entries. These are stark examples of data hygiene issues.

This is something I have kept repeating to my clients regularly over the years and is a major perspective I try to get clients to have when it comes to their analytics strategy. If you want to derive the best out of your data and put it to good use, you must focus on #datahygiene.

So what is data hygiene?

Data hygiene is the process of ensuring that data is clean, accurate, and consistent. This includes identifying and removing errors, duplicates, and inconsistencies in the data. It also includes ensuring that the data is organised in a way that makes it easy to find and use.

Why is data hygiene important for analytics?

There are several reasons why data hygiene is important for analytics.

First, dirty data can lead to inaccurate insights.

For example, if a dataset contains a large number of errors, the results of any analysis that is performed on that data will also be inaccurate. This can lead to poor decision-making, such as making investments in products or markets that are not actually profitable.

Second, dirty data can make it difficult to find and use the data that you need.

If your data is not organised in a way that makes sense, it can be time-consuming and difficult to find the information that you are looking for. This can lead to delays in decision-making and missed opportunities.

Third, dirty data can damage your reputation.

If your customers or partners see that your data is inaccurate or inconsistent, they may lose confidence in your business. This can lead to lost sales and damaged relationships.

How can you improve data hygiene in your analytics strategy?

There are several things that you can do to improve data hygiene in your analytics strategy. Here are a few tips:

• Start by identifying the sources of your data.?Once you know where your data is coming from, you can start to assess its quality.
• Use data validation tools to identify errors and inconsistencies in your data.?There are a number of different data validation tools available, so you can choose one that meets your specific needs.
• Clean your data regularly.?Once you have identified errors and inconsistencies in your data, you need to clean it on a regular basis. This will help to ensure that your data remains accurate and consistent.
• Organise your data in a way that makes sense.?Once your data is clean, you need to organise it in a way that makes sense. This will make it easier to find and use the data that you need.
• Educate your team on the importance of data hygiene.?Your team needs to understand the importance of data hygiene and how it can impact your analytics strategy. Make sure that they are trained on how to identify and clean data errors.

By ensuring this, you can improve data hygiene and ensure that the insights and recommendations that you generate are reliable.

Here are some additional inputs you could follow for improving your organisation's data hygiene:

• Use a #datadictionary to document the meaning of your data fields.?This will help to ensure that everyone on your team is using the data in the same way
• Establish #dataquality standards and monitor your data against those standards on a regular basis.?This will help you to identify and address data quality issues early on
• Automate as much of your #datacleaning process as possible.?This will save you time and resources and help to ensure that your data is cleaned consistently
• Use a #datagovernance framework to ensure that your data is protected and managed in a secure and compliant way.?This will help to protect your data from unauthorised access, use, or disclosure

By following these tips, you can improve data hygiene in your analytics strategy and ensure that your data is clean, accurate, and consistent. This will help you to make better decisions, improve your business performance, and protect your data.

Maintaining data hygiene is the first step towards making the right informed decisions.?