5 Best Practices to Create a Successful Data Management Plan

Category

Blog

Author

Wissen Team

Date

May 28, 2024

It is a well-known fact that adopting a data-driven roadmap for growth will yield significant results for businesses. In fact, studies have shown that companies that leverage big data solutions experience an average of 8% more profits and 10% lower operating costs when compared to those that ignore their data.

But achieving these feats requires more than just the initiative to implement complex big data software in your business. To achieve success and extract maximum ROI from data initiatives, enterprises must have a solid data management plan ready for use before they select tools and data solutions.

What Is a Data Management Plan?

Data management encompasses the guidelines every team within the business must follow while capturing, validating, archiving, and processing data from different business systems. It outlays how data must be handled in each of these areas to help businesses convince end-users to entrust them with the data and trust the services delivered with the data.

In simple terms, your business has a lot of data spread around a lot of areas. Some of it is useful and very powerful in the context of your business operations. You need to know how to capture useful data and how to organize it in the right way to derive the best results in the shortest time frame. Data management is the art and science of getting this job done.

5 Best Practices to Create a Successful Data Management Plan

Now that you have a clear understanding of what data management does and why it is important, let us explore five best practices to ensure that your data management plan is successful:

Make Sources Trustworthy

The first step for ensuring trusted results from processing the data you have is to make sure that the data source is trustworthy. Only then can insights help different business stakeholders set in motion the right action plan for their sustained growth.

Establishing the trustworthiness of data sources is possible once you have effective data governance in place that creates transparency and eliminates biased handling of data. Governance models must be established across the entire spectrum of data operations for each data source.

Prioritize Security

When moving to the realm of big data, there will be a large flow of data within the organization between multiple systems simultaneously. These systems may be a mix of old and new technologies which have diverse security and vulnerability credentials. It is important to set up a uniform security guardrail across your data assets to prevent any link in the enterprise network from a leak or breach owing to their outdated or vulnerable technology stack.

Leveraging steps like encryption, zero-trust approach, etc., are ways to combat security threats before they make dents in the enterprise technology ecosystem.

Ensure Insights Are Accessible

The true potential of data-driven growth can be leveraged only if all stakeholders can benefit from it. For this, accessibility of insights is critical. Enterprises must plan on creating workflows, privilege mechanisms, and storage management processes that enable all teams to make use of the data that helps their growth ambitions.

At the same time, checks must be in place to eliminate any data integrity issues due to supporting wide-scale accessibility. Automating data validations and verification of key parties in the system is an effective way to filter out any fraudulent access.

Map Data Management Objectives to Key Business Results

Having a foot in the data analytics space doesn't really give a competitive advantage today as it used to a decade ago. This is because today nearly all businesses can achieve such capabilities with data management. So, what does it take to differentiate your business's approach to data management? How does it help you to be more competitive? The answer lies in aligning data management to actual business objectives.

It is important to visualize data as a product and then evaluate various features or aspects of how the product will be used to deliver different experiences. For example, if a data stream is to be used in predicting weather trends, the key business objective is to keep end-users informed about the latest happenings surrounding the weather in their location. From a data management perspective, this outcome translates into a management objective of keeping the data fresh and continuously updated.

Working backward, the data management plan can be constructed to ensure that traits like real-time monitoring, continuous reporting, etc., are considered while framing governance policies and other controls for data management.

Leverage the Right Tools

Tools in the data management space undoubtedly have a strategic role to play in empowering a business to grow successfully. But, studies show that the global enterprise data management market will be worth over USD 122.9 billion by 2025, and this will be courtesy of hundreds or thousands of tools and platforms available in the market.

Enterprises must evaluate credentials like compatibility with their intended use cases, domain specialization or expertise, scalability and performance attributes, and security measures leveraged by different tools before deciding to onboard any. It is important to find the right fit tool so that results and actions can be deployed faster.

Wrapping Up

Building the right data management strategy is crucial for enterprises that wish to dive deep into the world of digital to get better results for their business. Following the above best practices should keep you on track to engineer the best data experiences for your business.

However, it's noteworthy that building data experiences that your customers love will require a holistic implementation of all these five best practices, and that is exactly what we at Wissen do happily for our customers. Get in touch with us to know more.