Data Strategy Steps: The Data Audit


We have made it to day 3 and step three of exploring the steps we will need to execute to build out our data strategy.  It is time to figure out the lay of the land. 

How do we figure out where we are starting from? The best way would be to perform a data audit. This audit is a process of assessing the quality, accuracy, and completeness of your organization's data. It is an essential step in developing a data strategy because it helps you to understand what data you have, where it is located, and how it is being used.

What should you expect to get when your data audit is finished? First, it can help you to identify any gaps in your data. This can be helpful in determining which data sources you need to collect or improve. Knowing this will also help you to know which data source you should start with. Second, a data audit can help you to identify any errors or inconsistencies in your data. This can help you to improve the accuracy of your data and to ensure that it is reliable. Third, a data audit can help you to understand how your data is being used. This can help you to identify any opportunities to improve the way that you use your data.

Another benefit you will receive from your data audit is that it will help you to build a roadmap to show how you will accomplish your business objectives. By understanding your data, you can identify the data that you need to collect, the analytics that you need to run, and the insights that you need to generate. With that information you will be able to develop a data-driven strategy that will help you to achieve your business goals.

Here are 5 different ways to conduct a data audit depending on the organization's makeup:

  • Manual audit: This is the most basic type of data audit and involves manually reviewing your data. This can be time-consuming and labor-intensive, but it can be a good way to get a detailed understanding of your data.  When your team is done here they will be better equipped to answer questions about the data that will arise. 
  • Automated audit: This type of data audit uses software to scan your data for errors and inconsistencies. This can be a more efficient way to audit your data, but it may not be as thorough as a manual audit.  This method will not give the opportunity for your team to become fully exposed to the data and the process that make the data, but in most cases this is the best place to start.  The reality is that there is just to much data do do this work manually.  
  • Hybrid audit: This type of data audit combines manual and automated methods. This can be a good way to get the best of both worlds.  In a more mature organization this would be a more logical place to start because team members would already be familiar with a majority of the data that will be audited.  
  • Continuous audit: This type of data audit is conducted on an ongoing basis. This can help you to identify and address any problems with your data as soon as they arise.  This is actually a best practice moving forward, but we will cover that when we get to data governance. 

The best way to conduct a data audit will depend on the size and complexity of your organization's data. If you have a small amount of data, a manual audit may be sufficient. If you have a large amount of data, an automated audit may be a better option. When you are in a more mature organization that is familiar with using its data the hybrid method is where I would suggest you start.

We have built on our previous steps of understanding the business objective and getting our stakeholders engaged. With that information we fine tuned our data audit. Next we will focus our road map and a full communication plan. Our data-driven strategy is almost ready to be executed on.

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