Countless business owners use several tools, such as data management tools, to improve their operations. While it is easy to use these tools, not all can tell how well and effective the tools they use are in enhancing their business workflow. This is where [metrics and KPIs](https://www.forbes.com/advisor/business/what-is-a-kpi-definition-examples/) come in handy.

Setting up Metrics and key performance indicators (KPIs) in your business can enable you to measure progress and make necessary adjustments by providing an accurate [data governance overview](https://www.delphix.com/glossary/what-is-data-governance). Once established, these tools can optimize your results and significantly enhance your business operations.

However, while this appears as a turning point for many businesses, most are new to these standard measurement strategies and struggle to identify the criteria for monitoring the success of data management.

This article will serve as a comprehensive guide on how metrics and KPIs can be used to measure data management success and progress. Let's check it out!

What is Data Management?

Data management involves collecting, organizing, assessing, and evaluating data to enhance decision-making, productivity, and business operations. For optimal results, establishing data governance is essential.

What is Data Governance?

Data governance is the procedure used to ensure that data sets comply with strict rules and regulations.

It is no news that data is a corporate asset of great importance to any business in this globalized world. Data can be used to make informed and strategic decisions. Several businesses and Individual operations deal with collecting and storing bulky data in their daily work activities.

In addition, data can improve marketing campaigns, optimize business operations and maximize profit if appropriately handled.

However, managing this large amount of data while making strategic decisions can be daunting.

But thankfully, recent IT system upgrades provide tools and software that aim to ease the monitoring and measure the effectiveness of data collected in corporate systems. They also ensure that these data are accurate and readily accessible. They are Data management and data governance metrics and KPIs
What Are Data Management Metrics And KPIs?
Data management KPIs and metrics are indicators established to measure the overall efficiency of an organization's data operations.

These indicators provide several functional and measurable criteria to assess the effectiveness of data management operations.

You can also monitor these criteria to ensure the progress and success of your data management strategy. Some of them include:
Essential Criteria To Measure For Progress And Success In Data Management
Data Consistency:

This metric enables you to gauge the level of organization within a specific database. You can eliminate any inconsistency in your data by providing you with this.

Note that minor inconsistencies within your collected data can lead to misanalysis and unnecessary expenses.

Ensure that each piece of data entered in the input stage is specific to the group to avoid errors. Employing digital forms and automatic data collection can achieve this, as these methods save time and prevent errors.

Data Uniqueness:
This measures duplicate and unique records within a source system. To measure success, strive for uniqueness by ensuring your data is distinct.

Though having duplicate data might seem appealing, it's essential to understand that it can significantly increase your storage requirements. It is critical to identify duplicates to maintain and enhance the effectiveness of your data management processes.

Frequent testing of records and removal of duplicates facilitate data hygiene, making optimizing business operations easier without duplicates' influence.

Many businesses and companies have embraced automation to continuously check and eliminate data duplicates before they can lead to miscalculations and disrupt operations.
Data Completeness:

This measures accounts that have missing or incomplete data. For example, a database containing the names of employees' email addresses and phone numbers has missing columns or segments.

This error can create significant difficulty in processing payments, which may result in delays.

While this can be automated, many times than one, these errors are human-generated, resulting from uncoordinated inputs in spreadsheets or online forms.

However, setting concise and clear rules with compulsory fields can lead to a reasonable reduction of incomplete data, save time, reduce confusion, and also enhance business workflow.

Database Availability:
Database Availability refers to the time required to keep a database operational and accessible. It is important to note that databases can crash or break down at any point for several reasons, such as storage failure or power outage.

This can result in a very severe and catastrophic [data loss](https://www.techtarget.com/searchdatabackup/definition/Data-loss), which can harm your business operations.

Fortunately, you can enhance your database availability by implementing failover and redundancy measures and establishing a routine data backup protocol.

With KPIs, you can determine the time required to restore your system after a crash.
Why Measure Success And Progress of Data Management?

You should measure the progress of your data management strategy for several reasons. Not only because it keeps you organized but because it can either facilitate or slow down your business operations. Additionally, they:

Ensure that the data collected are accurate, of good quality, and readily accessible. It also makes identifying and addressing redundancy, inaccuracy, and possible errors easy. Thereby facilitating decision-making based on reliable data.

Enhances a sense of accountability within your business. It establishes clear objectives and responsibilities for improved data procedures among teams and Individuals.

It also makes room for improvement, especially in sectors with a lack or repeated occurrence of errors. Measuring data management enables optimizing data management processes and improves business outcomes.
Measure Data Management For Improved Outcome

Measuring data management strategy makes room for necessary and strategic decision-making. This is based on how well or poorly your data management strategy functions.

Constant effort in scanning and identifying faults and errors in your data keeps you at an advantage of improved business operations, procedures, and organization among teams. It also increases business profit.