5 Enterprise Data Management Challenges and How to Overcome Them

By Scott Chesney - 

In today’s digital world, companies have and produce more data than ever before. This data is used for everything from shaping key business decisions to informing customer behavior and measuring performance.

To ensure that employees and customers have access to the right data when they need it, companies have to think more strategically about their data management solutions and how they secure, manage, and share their information. A robust enterprise data management program can also help with maintaining compliance with privacy and regulatory requirements and boost productivity.

However, putting a strong enterprise data management program in place is often easier said than done—and many organizations end up facing several key challenges in implementing and maintaining one.

If this sounds familiar, here are five common enterprise data management challenges and some proven tips on how your organization can overcome them.

5 Common Enterprise Data Management Challenges 

An incomplete enterprise data management program can lead to a number of key challenges:

1. Data Silos

Whether comprised of business, human resources, marketing, or other department information, data silos among workgroups seem harmless. However, siloed data created barriers that can lead to a number of operational challenges.

These hurdles can inhibit an organization from making more informed, data-driven decisions, inhibit a more comprehensive understanding of employee or customer behavior, or lead to duplication of effort. As data is funneled into different enterprise systems, siloed, duplicated, or incomplete data sources can lead to a lack of trusted data sources.

2. Governance and Compliance

Data often is ingested from customers or other sources once, but is then routed, shared, processed, and stored by many internal groups. This can lead to different data “owners” using and seeing the same data differently.

Failing to have a robust enterprise data management program that reconciles these views can inhibit different business units from having access to the information they need to deliver on their purpose. This leads to process inefficiencies, inadequate data accessor security controls, or the possibility that data loses its integrity over time.

3. Data Quality

Without a reliable data foundation, any insights derived from processed data are prone to error, rendering outputs unreliable. Similarly, business processes may also be affected as employees must work to revalidate and clean any anomalies before they are used for their own purposes, slowing productivity.

4. Lack of Data Processing Standardization

The absence of standardized data formats, naming conventions, and data management processes can also lead to data integration challenges and other negative downstream impacts.

For example, the lack of a unified structure and clear data processing guidelines can lead to duplicate or incomplete data or the inability of different systems to use data across various contexts¹.

5. Rigid Data Architecture

Legacy systems and antiquated technology infrastructure present dual challenges for businesses. Not only do they incur high operational costs, but they also fall short in terms of scalability and performance—particularly when dealing with substantial data volumes or accommodating evolving business requirements.

Proven Strategies to Overcome Enterprise Data Management Challenges

Data management challenges often come in groups; if your organization is struggling with data silos, it is likely that you are also having a hard time overcoming data standardization.

That’s why we recommend taking a holistic approach to evaluating and strengthening your enterprise data management program by considering these tips:

• Assess and modernize the existing technology infrastructure and consider migrating it to scalable and cloud-based solutions to handle increasing data volumes.
• Establish and enforce a robust data governance framework with clearly defined roles, responsibilities, management policies, standards, and guidelines.
• Define clear data ownership and stewardship patterns so that the business people who know the data are responsible for it.
• Implement data integration and data transformation tools to streamline data processes.
• Implement data profiling and data cleansing tools to identify and rectify data issues.
• Use data integration tools or software to connect disparate enterprise systems and facilitate data sharing.
• Build a data architecture and engineering practices to model the data efficiently and present it in ways that make sense to business consumers.

Take the First Step Toward Bolstering Your Enterprise Data Management

Deciding to begin the journey to strengthen your enterprise data management can be a complex undertaking, but, fortunately, it isn’t one that your organization has to take on on its own.

By choosing a reliable and experienced partner like RCG, you can take the crucial first steps with confidence and develop a comprehensive plan to tear down data silos, build trusted data platforms, and streamline your data processing. RCG's proven expertise in data management empowers organizations like yours to navigate challenges with efficient and scalable solutions that are truly future-ready.

Want to learn even more about how your organization can maximize how it is using data to take its operations to the next level? Then make sure to check out our related comprehensive resource, 7 Steps to Data-Driven Success, now!

Works Cited

1. The ECM Consultant (2023, July 4) "11 Data Management Challenges and Solutions" Retrieved from https://theecmconsultant.com/data-management-challenges/