Data Management KPIs: 6 Metrics to Measure and How to Improve Them

Data management remains a top challenge for companies across all industries. The vast majority of organizations have a hard time moving data from point to point and putting actionable insights into the hands of teams and business leaders. Companies today struggle with poor visibility into their data sources, insufficient data pipelines, and low data quality. 

In order to improve data management, data managers must track and monitor key performance indicators (KPIs) and optimize them over time. Data management KPIs are critical for any organization that’s serious about improving data usage. 

Read on to learn more about how data management KPIs work and what you can do to improve them over time.

What Are Data Management KPIs?

Data management KPIs measure the overall effectiveness and efficiency of data operations within an organization. All companies, from startups to large enterprises, should track these metrics. 

Data managers rely on KPIs just like sales and marketing leaders use analytics to track daily performance within their departments. In other words, data management KPIs provide critical insights into how data is moving throughout the organization as well as its overall quality and usefulness.

Many data leaders are now using real-time dashboards to track data management KPIs and keep a pulse on what’s happening across the organization. Some choose to build their own monitoring platforms, while others rely on third-party cloud services that automate tracking and monitoring. 

Why Track Data Management KPIs?

Data management KPIs are important because businesses are increasingly relying on analytics to guide daily decisions and enhance workflows. But as companies look to become more data driven, they also require access to efficient and reliable data flows. Unreliable, inconsistent, and inaccurate data can lead to costly production errors that threaten operational stability and harm business operations. 

To illustrate, imagine a sales team that has inaccurate information about previous purchase histories or brand interactions. This could potentially disrupt their strategy and make it harder to close deals on the most favorable terms. 

With this in mind, tracking data management KPIs can ensure that all teams throughout the organization have access to high-quality analytics. Plus, when team members trust their data, it increases the likelihood they will rely on it instead of making decisions based on gut instinct. 

Remember that having trust in your data is one of the fundamental building blocks to becoming a data-driven organization. For this reason, tracking data management KPIs can enhance your data’s value and help the organization embrace automation. 

Top Data Management KPIs to Monitor

There are many different metrics to consider, and narrowing them down can be difficult. With that in mind, here are a few general KPIs that data leaders should track over time.

1. Data consistency 

Data consistency lets you see how uniform data is within a specific database. 

Ideally, you should strive to eliminate inconsistent data from your repositories. Having even a few inconsistencies can open the door to major complications. For example, imagine using a data set that mixes imperial and metric measurements. This type of error could lead to incorrect calculations and costly repercussions.

Strong data starts at the input stage. Setting up digital forms and automating data collection can help you eliminate input errors and prevent rework down the line.

2. Data uniqueness 

The uniqueness KPI measures duplicate and unique records within a source system. 

Again, your goal should be complete uniqueness in order to ensure there are no duplicate data points. Duplicate data is harmful because it leads to wasteful practices and higher storage costs. 

Unfortunately, duplication is common in sales and marketing environments, where teams often receive databases with multiple instances of the same lead or customer. Finding and eliminating duplicates is critical for maintaining a lean and efficient data management strategy.

To improve data uniqueness, test for duplicate records frequently and remove unnecessary items from your databases. Practicing consistent data hygiene makes it easier to stay on top of duplication and prevents it from impacting operations. 

A growing number of companies are choosing to automate this process and consistently scan for data duplication errors to solve the problem before it impacts operations. 

3. Data completeness 

Data completeness measures how many accounts have missing or incomplete data. 

For example, a customer database may contain missing names, email addresses, and phone numbers. These types of errors can make it difficult or impossible to process transactions, leading to billing and processing delays. 

In most cases, data completion is a human error that stems from faulty input in spreadsheets and online forms. Setting clear rules with mandatory fields can help reduce incomplete data, saving time and reducing headaches. 

4. Average database availability 

The average database availability KPI refers to the amount of time a database remains up and running. 

Databases can crash for various reasons, like server failure, storage failure, or a power outage. In some cases, crashes can lead to catastrophic data loss and negatively impact customers and operations. 

The good news is that you can improve database availability by automating failover and establishing redundancy. In addition, it’s critical to routinely back up your data. 

One similar KPI to consider is mean time to repair, which you can use to analyze the amount of time it takes to restore a system after an unexpected crisis. 

5. Total number of accounts serviced 

This metric has to do with the number of internal or external accounts with inaccuracies from issues like insufficient, duplicate, and incorrect data. Most data leaders track this KPI over a monthly period. 

In a perfect world, your business shouldn’t have to service accounts due to data errors. However, data management is messy, and mistakes happen. But by embracing automation and establishing strong data governance and management policies, you can greatly reduce data management errors and lower the total number of accounts that require service. This can also free engineers to focus on higher-level priorities—a win-win.  

6. Report production cycle time 

Report production cycle time refers to the average amount of time it takes to fulfill a management request for a new report. This is typically a multistep process that requires receiving the request, gathering information, and visualizing the insights. 

Business leaders today expect speedy access to reports and insights. After all, modern data dashboards and real-time reporting engines make it possible to access data immediately instead of waiting hours or days.

Despite this, businesses often struggle with reporting delays because they can’t connect their reporting engines to disparate data sources. It’s not that they lack the information. It’s that they don’t have the ability to access it. If your business is struggling with reporting delays, you may want to focus on improving data pipelining

How Panoply Helps with Data Management 

Panoply is a one-stop shop that makes it easy for data analysts to sync, store, and access data from any location. Using our platform, you can connect a wide range of data sources without having to write any complex code. 

With Panoply, you can unlock data from databases, files, and tools. As a result, your team can leverage faster reporting with more consistent metrics and fewer errors.

Check out Panoply in action by requesting a free demo today. While you’re at it, jump right in and try Panoply for free.

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.

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