Importance of data quality

1. Importance of data quality

In this video we will discuss the value of measuring and improving data quality and learn why it is important in business context.

2. Data quality generates business value

Data quality is a measurement of the degree to which data is fit for purpose. Let's build on this definition by discussing the value and importance of data quality. Everyday businesses are making decisions based on data. The data quality may be high, poor, or unknown. By initiating some basic data quality activities, companies can ensure that data is suitable for their use. Once data quality is known, improved, and continuously monitored, data value begins to increase and more people will want to use data. When this happens, better business decisions are made which drives the business value of data. As shown, increasing data quality increases the value of data because it more closely represents the truth and can be trusted. When data value increases, the business value of data increases because the data can be used for better decisioning and processes.

3. Activities for good data quality

Let's define basic data quality activities that must be in place for driving good data quality. The underlying data which is used to make decisions and in business processes should be monitored for data quality, have timely issue resolution, and be produced and consumed by people who understand the value of data quality. Continuously doing these basic yet high impact activities, will ensure that data value and business value increases.

4. Value on the offense

Companies that have these basic data quality activities in place can reap the benefits of good data quality. Many companies do not have good data quality processes in place and will eventually use poor quality data to make a bad decision. By managing your company's data quality, you can gain a competitive edge. By managing your customer's data with data quality rules, you will be able to serve your customers better. For example, identifying duplicate customer records and cleaning the data so that you only have one customer record will ensure that you only contact a customer once versus multiple times.

5. Value on the defense

You can also glean value by defending the business from risks and inefficiencies. By implementing and monitoring data quality rules you can find issues before they cause major risks. For example, if a manual entry of the balance on a customer's account had an extra 0 at the end, the customer could potentially think they have extra money in their account and try to withdraw it. This is a risk for your company in potentially losing money and the customer if you decide to ask for the money back. By implementing a data quality rule up front, you can prevent issues like this from happening. By ensuring high quality data is used for business processes, you will avoid running into processes broken due to data quality issues. For example, if a customer service process relies on a customer name being populated and the name is missing, then the process will not work as expected.

6. Checking for data quality

Think about the last time you or a business user had to make an important decision using data. Now that you understand the value of good data quality, what questions will you ask about data before using it? You must ensure it is the latest data available, there are no duplicate records, the dataset is complete, there are values when expected and that those values are accurate and valid. If your data meets your data quality criteria, then you can feel confident in trusting and using it for your purposes.

7. Let's practice!

Now that you have a solid foundation of why data quality is important, let's practice.

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