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.
You may have often wondered why your team makes poor and late decisions despite the availability of data. If you dig a little deep, you will see inefficient data management is at its center.
Your team may spend hours hunting for the right data, only to find duplicates, outdated records, inconsistent formats, and missing fields. The data may even be buried in siloed systems and scattered across multiple tools, with no context.
These issues can cost your business millions every year. In 2022, Unity Software lost USD 4.2 billion in its market cap and USD 110 million in revenue due to ingesting bad data from a large customer.
Cases like this show why enterprise data management is a pressing challenge for leaders today.
In this blog post, we discuss the 7 biggest EDM challenges businesses face in 2025 and how to overcome them.
Enterprise data management is a comprehensive framework to organizing, managing, securing, accessing, and using an enterprise’s data to achieve its business goals. It makes data a valuable enterprise resource than just information by ensuring data quality, compliance, integration, and metadata management.
An effective EDM process ensures that an enterprise’s data is available for all stakeholders when they need it to help their decision-making process.
There are several ways enterprise data management helps the business:
As data sources grow with the addition of systems like CRMs, ERPs, HRMS and cloud platforms, businesses increasingly need a unified, strategic data management framework.
Efficient enterprise data management is crucial for businesses that want to use data for smart decision making. It helps ensure accuracy, improve decision-making, strengthen compliance, lower costs, and empowers businesses to harness data for growth.
Here are 5 ways an effective enterprise data management strategy can contribute to your business:
Many organizations struggle to manage growing volumes of data, especially as AI tools become a key enabler across different departments.
Here are the top 5 common data management challenges enterprise businesses face in 2025.
In many enterprises, data lives across disconnected systems, such as CRMs, ERPs, analytics tools, and departmental databases.
While it’s great to have systems for each department, when these systems don’t talk to each other, critical information gets trapped. Siloed data also leads to a variety of operational and efficiency challenges:
As a result, these silos block innovation, collaboration, and the ability to respond quickly to market changes. As businesses depend more on real-time analytics and AI-driven workflows in 2025, this is an even more pressing issue than many businesses realize.
Customer and third-party data often flows through multiple teams, such as sales, finance, operations, legal, and product. Each of these departments may route, process, access, and store the data differently.
Without clear, unified governance, data becomes a mere commodity. The same data can be interpreted in conflicting ways.
For example, sales might classify a prospect as ‘active’ after one call, while finance only considers them ‘active’ if a contract is signed.
These misalignments lead to poor quality data and render it unsuitable for decision-making and reporting.
Gradually, this can erode the integrity of your data and make it difficult for leaders to trust the data they rely on for critical decisions.
When no single team is clearly accountable for specific data sets, ownership gaps emerge. And it will compromise all your data management efforts.
Marketing assumes IT is responsible, IT assumes operations, and operations assume compliance.
This leads to a situation where no one fully manages the data lifecycle.
As different teams use data to make decisions, a lack of an efficient enterprise data management system can:
As data is the backbone of AI-driven systems, unclear ownership also creates legal and compliance risks in 2025.
According to McKinsey, over 92% of companies are planning to increase their AI investments in the next three years.
This means businesses need clean, reliable data for the AI tools and systems to work efficiently.
This poses the next enterprise data management challenge for businesses. If the underlying data is incomplete, outdated, unreliable, or inconsistent, the outputs using this data will also be flawed.
For example, a sales automation platform trained on duplicate or mislabeled customer records may send the wrong offers to the wrong people.
As businesses scale their use of AI across operations, even small errors can compound quickly. This can damage your reputation and lead to wasting resources, bad predictions, biased results, or missed opportunities.
People (and governments) are growing concerned about how businesses collect and use data.
To ensure businesses use data responsibly, statutory regulations like GDPR, CCPA, etc., are established. However, the very nature of technology and the type of data collected by businesses make these regulations evolve quickly.
Adding to the issue is the fact that data rarely stays in one place. It is collected, processed, stored, used, and shared across systems and geographies.
And this creates problems for businesses. They need to ensure compliance consistently with regulations that keep evolving.
This means they must be able to:
A lack of efficient enterprise data management systems may lead companies to risk fines and reputational damage.
Different teams in your organization may create, maintain, store, and use data. When different teams handle data using varied formats, rules, and tools, even the same type of data can look very different.
For example, marketing might normalize customer names one way, finance another, and operations yet another.
Without shared standards, data loses consistency and becomes hard to integrate, as:
These issues slow analytics and increase the risk of errors when data moves across systems.
As businesses rely on collaboration between teams and departments, the absence of standardized processes can lead to inefficiency and non-compliance.
Many enterprises still rely on legacy systems that were built years ago. They came a long time before any of today’s data volumes, cloud adoption, and AI-driven needs were even thought of.
This creates a lot of data management and adoption challenges to businesses, as these legacy systems cannot:
In addition, these legacy systems push teams toward manual workarounds, causing errors and increasing costs, and fall short of scalability and performance.
Overcoming the enterprise data management challenges is vital for businesses that are growing and plan to scale. As data is going to be a vital part of your long-term and daily decision-making, creating efficient enterprise data management is important for your success.
Here are 7 actionable strategies you can use in 2025 to address enterprise data management challenges or eliminate them altogether from happening at all.
Assess your current IT infrastructure and its ability to meet your data management needs now and in the future.
Based on the assessment, scale your current technology infrastructure or consider moving your systems entirely to cloud solutions based on growing data volume.
Your organization needs to have a robust data governance framework so that everyone can fall back on when managing data.
This allows different departments, teams, projects, and even management to understand how data must be managed.
Ensure that the framework clearly defines roles, responsibilities, management policies, standards, and guidelines.
One of the biggest problems in enterprise data management is the lack of ownership. No one clearly knows how to take ownership of data and what needs to be done.
You can avoid this challenge by defining the data ownership patterns in your organization. This allows everyone to know who’s responsible for data.
It also helps improve accountability and quality of data.
Implementing data integration and transformation tools can help you streamline your data processes.
These tools help you extract data from diverse sources like websites, social media platforms, accounting systems, etc., clean, and standardize the data making it ready for use.
By doing this, you can save time from manually cleaning it and improve the data accuracy and consistency.
As we have discussed, data in an organization is scattered across diverse sources. From websites to social media platforms, advertisement channels, and business management platforms, the data is decentralized.
You need the data to be centralized to manage and use it well. This is where data integration tools can help you.
These tools integrate with diverse platforms like CRM, ERP, accounting, and data visualization tools. As such, you can not only bring data to your preferred source but also use it well for decision making.
Managing your business data should not be an afterthought. It needs to be a priority for businesses that want to scale and grow. Since data supports intelligent and informed decision-making, investing time and resources to improve its management makes sense. However, going down that path without the right support can lead to more challenges and inefficiencies down the line.
This is where working with a brand like Myridius with years of experience creating, optimizing, and scaling enterprise data management solutions for businesses.
With Myridius, you can:
Our expertise in data management helps you address current challenges with enterprise data management and build systems to avoid them ever happening again in the future.
Take the next step to make your data efficient and work for you instead of wasting your team’s time on data management.
Book a free consultation call with us.
Data silos trap data within departments and make it hard for others to access and use it. This fragmentation often leads to duplication, inefficiencies, and inconsistent reporting across teams. As businesses need cross-departmental data to make informed decisions, data silos work against it. When data is trapped within departments, leaders lack full picture and may make decisions that may not serve the goals of the organization.
Poor data quality can lead to inaccurate analytics and misguided strategies. This can lead to decisions that are not supported by data and missed revenue opportunities for businesses.
Strong data governance ensures accountability, data ownership, standardization, and compliance. It helps align data practices with business rules, reducing risk and improving overall data reliability. You need an efficient data governance process to work with EDM, as it is the primary enabler of efficient EDM.
Overcoming EDM challenges is not a one-time effort or strategy. As business scenarios, goals, and regulations change, it is an ongoing process. You can begin this by building a centralized data architecture. Then invest in integration tools, define clear governance policies, and ensure cross-departmental collaboration. It is also vital to ensure regular audits and employee training to sustain long-term success.
1. The ECM Consultant (2023, July 4) "11 Data Management Challenges and Solutions" Retrieved from https://theecmconsultant.com/data-management-challenges/