Businesses need accurate, quality data to make the right data-driven decisions. Where does this data come from? It comes from good data management. Your data will be no good and even dangerous if it fails to meet the quality criteria.  This makes it crucial to manage your data effectively. How do you do that? With the data management best practices. Before we jump to the best practices, let’s understand data quality and how it is enabled by good data management.

Understanding Data Quality and How Good Data Management Ensures It

One can define data quality in several ways. However, data quality generally means that data is suitable for the use case or task at hand. Since data quality is dependent on the context in which it is utilized, there is no absolute data quality benchmark. However, the following are the widely accepted rules for evaluating data quality:

• Completeness: No values are missing
• Validity: Data matches the rules
• Uniqueness: No data duplication
• Consistency: Consistent data across various data stores
• Accuracy: The degree of reality the data represents
• Timeliness: Reality is represented by data from the required point in time

By bringing together and managing data from disparate applications or data silos, good or effective data management helps provide data that fulfills the above criteria for data quality. Next, we look at the 3 best practices that make up good data management.

The 3 Data Management Best Practices to Try

You won’t get desired outcomes from your data analytics until you adopt the best practices for managing data. Following are the 3-data management best practices that you need to try:

1.      Ensure Easy Access to Data

The more data your data scientists and analysts have access to, the better it will be for your business. With access to more data, the data analysts and scientists will be able to determine more quickly which data will best predict an outcome. So, the first best practice to enable good data management is providing data users easy access to data and allowing them to easily work with data from a variety of sources, formats, and structures.

2.      Employ Advanced Analytics Techniques

This will help to strengthen the arsenal of your data analysts and scientists. With sophisticated analytical capabilities, your data users will be able to better understand data distribution and variance. Based on predictive capability strength, these users will be able to determine which variables or combination of variables will be most useful.

3.      Cleanse Data to Build Data Quality into Your Data Integration Flow

Poor data is the reason for the failure of most strategic processes. You can build data cleansing right into existing processes by using a data quality platform designed according to the data management best practices. This improves performance by pushing processing down to the database and based on the analytic method being used, removes invalid data to enrich data and ensure data quality.

In addition to the above, there are few other things you can do to manage data effectively. This includes creating a good data management plan. Following are some tips to do this.

Tips for Creating a Good Data Management Plan

Planning is required to establish a system that allows you to access files, avoid duplication, and back up your data. A good place to start is creating a data management plan. Part of business planning, a data management plan allows everyone to know what’s needed from them and what they’re supposed to do.

Why is a data management plan needed? Data and files can quickly become disorganized after you create, gather or start manipulating them. A good data management plan will help your business to save time and prevent errors that may occur otherwise.

More importantly, the plan will allow you to add context to your data, making it easier for you and others in the organization to understand the data in the short, medium and long-term. How can you develop such a plan? By following the tips for creating a good data management plan given below.

1.      Define Roles

Generally, many people in an organization are responsible for managing data. Even if there are only two to three people in your organization managing data, you need to define roles so everyone knows what they’re expected to do. By doing this, you will ensure that when something needs to be done, the right person will assume the task. This will save your business a lot of time in the long run.

2.      Outline the Data Being Collected

Be clear about the data you’re collecting and why you’re collecting it. Create a list of every type of data that to be included in your data plan, and then provide justification for doing so. This will provide everyone with answers to questions regarding data that they might have later on.

3.      Determine How You Will Collect Data

This is one of the most important things that you’ll need to do to create a good data management plan. Create a separate plan for collecting data. You may use several different methods to collect data, make sure to include all of them. Also, you will need at least one person to look over this data as it’s being collected so mention the person or data management service in the plan as well.

4.      Determine How You Will Protect Data

Securing data is required by law so once you have data, you will need to protect it. Determining how you will protect data and then detailing this in the plan is extremely important so make sure you don’t skip it. This is important for protecting your business and complying with the law.

5.      Test Your Plan

Once you have a data management plan in place, test it routinely to evaluate its effectiveness over time. If the plan yields desired outcomes, then continue using it. However, if it stops producing the results you expect, then consider modifying it.

Data management is no easy feat. However, good data management plan can ensure effective data management in the long run. Another thing that can help you to manage data effectively is an enterprise data management solution. Keeping following us to find out more!

 

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