Data teams need to review important metrics and KPIs regularly to ensure they’re getting the best results and improving their strategies. However, measuring ROI for data teams isn’t always the simplest task in the world. How can data teams overcome these challenges? In today’s article, we’ll look at why data analytics are important, what metrics and KPIs you should look at, and how Secoda can help you manage your data. Read on to learn more.

Why Should You Track Data Analytics?

Tracking data analytics and data quality metrics are essential if data teams want to ensure they have high-quality, accurate data. When you have the best data, you can make better data-driven decisions, your business runs more efficiently, and you can avoid wasting time.

When you don’t have quality data, your business may waste time, money, and make strategies based on inaccurate analytics. This bleeds into your daily operations, making it difficult for your business to run efficiently.

With quality data management and data metric tracking, your business runs better and fewer mistakes are made. Now that we know why tracking your metrics and KPIs is important, let’s look into some of those key metrics.

Data Quality Metrics

Data quality can be difficult to measure. That’s why it’s essential to lay out a data management strategy and highlight key metrics to pay attention to. With these metrics, you can get a better idea of the accuracy and quality of your data.

Accuracy

Data accuracy looks at the rightness and validity of data. In other words, is the information in the data field correct? To measure data accuracy, you need to look at the errors in any given data set. Fewer errors, when compared to the overall data set, means you have higher-quality data.

If you have accurate data, then your team can confidently use it in their daily operations. If your data isn’t accurate, things can get confusing and inefficient. Every business needs accurate data. 

For a simplified example of the importance of data accuracy, imagine you need to bill a customer, but you don’t have the right information in their data field. Now time needs to be spent finding the right information so the bill can be sent. If the data were accurate in the first place, this inefficiency would have been avoided.

Consistency

Consistency is another important data quality metric. With consistency, your data should match across data sources. If you pull information from two different places in your business, this information should agree. When you have inconsistencies, wires can get crossed when teams try and collaborate.

If your company has a lot of different data sources and data silos, it can be difficult to maintain consistency. It’s important to manage your data and make sure that disparate data sources don’t get out of hand.

For a simple example of the importance of data consistency, imagine that an order needs to be shipped to a warehouse. One data source says that a certain number of items need to be shipped, but another source is providing a different number. Now more time and effort have to be expended to determine the accurate number.

Completeness

Completeness is a data quality metric that looks at how comprehensive your data is. Complete data needs to give you the total picture of a data point or data set. Critical fields need to be filled for data to be considered complete.

To explain the importance of complete data, imagine that you’re on the marketing team for a company and you’re needing to send out an email blast to repeat customers. You have the names of the customers, but you don’t have any of the email addresses. This is incomplete data and it essentially makes that set useless.

Reliability

Reliability looks at the time value of data. Your data needs to be accurate and consistent over time to be considered reliable. If your data becomes less and less accurate over time, you don’t have reliable data.

For an example of the importance of data reliability, we can look at the email marketing example again. Imagine you have a list of clients and you need to send out an email to each. Your email list hasn’t been updated in two years, so half the emails are older and the clients likely won’t see them. This is unreliable data that hasn’t stayed consistent or accurate over time.

Usability

Data usability is a metric that measures how easy it is to use and understand data. If your teams don’t use your data, they won’t be able to put it into action. Data shouldn’t be mysterious. It needs to be simple to interpret and usable.

To explain why usability is important, imagine that one of your nontechnical team requests data and gets back hyper-technical metadata. Now that team needs to look to the data team to provide them with an interpretation of it. It needs to be much easier for teams to request and easily understand data, whether they’re a technical user or not.

Freshness

Freshness is a data quality metric that looks at the age of data. Since data points can change over time, it’s best to make sure your data is as current as possible. Data should be updated regularly, whether it has changed or not. You never know when a data point could change, so you want to make sure your data stays as fresh as possible, it helps it maintain accuracy.

For this example of data metric importance, you can look at our data reliability example. If you have a list of customers and you haven’t updated their emails in years, then it’s probably a safe bet that not all of those emails will be accurate. Freshness and timeliness need to be something you measure regularly.

Data Uptime

Data uptime measures how often data is delivered promptly and whether your systems are reliably delivering data. Data uptime is important for fast-paced companies that utilize data to drive decisions and strategies. When you have good data uptime, your team can be more efficient.

Imagine that your team makes a data request and it takes hours for them to get the information they need. This slows down their work and their strategies. Data uptime that is fast and efficient helps teams do more.

Number of Requests

When you’re looking at the number of requests, you want to see your numbers go down. The number of requests refers to the requests that data teams get from other teams. If you have an efficient system for data requests and when data is democratized, understandable, and accessible, your data team gets fewer requests.

This is a win-win for everyone. Imagine your data team is getting a huge number of requests from other teams every day. Not only does this inundate the data team with more work, but it means that the other teams will be waiting longer to get information back as they wait for the data team to work through all the requests. Reducing the number of requests helps everyone in your company.

Try Secoda for free

If you’re looking for a way to improve your data quality metrics, Secoda can help. Secoda is a data catalog platform that allows you to search, document, and manage company data all in one place.

This helps improve every data quality metric we’ve covered today. Consolidating your data all in one platform helps to keep data accurate, consistent, reliable, and fresh. Making the data searchable for all team members means your data is more usable. This also reduces the number of requests for your data team and dramatically improves your data uptime. Interested in learning more about Secoda? Try our platform for free today to see how it can help your team.