8 min read

Unlocking Data Potential: Best Practices for Effective Enterprise Data Management

Data is the foundation of any project, especially if we are talking about AI. But the constant data growth and strict security measures make data management more and more difficult. What strategy should enterprises adopt? Discover the key elements of an effective data management strategy and specific tools to help you implement it.
Written by
David Schoch
Published on
February 25, 2024
Read time
8 min read

According to a report, the global enterprise data management (EDM) market size is forecasted to reach $281.9 billion in 2033, a substantial increase from $97.5 billion in 2023. It indicates the shift toward increased investment in EDM technologies by businesses working to optimize their data assets.

At the same time, the growth of global data volume, increasing at a rate of 39% annually, presents significant challenges for businesses striving to effectively manage it. With data security regulations getting stricter daily, companies feel the pressure to safeguard their valuable assets. What’s more, as a result of inadequate or absent data management practices, as many as 73% of organizations are unable to effectively leverage the majority of their data, leading to missed opportunities and revenue loss.

Enterprise data management is crucial for any organization. It’s about establishing policies and procedures for data handling, implementing data quality standards, ensuring compliance with relevant regulations, and leveraging technologies such as databases, data warehouses, and analytics tools to gain insights from data. As a software development company, we at Modeso prioritize the importance of effective data management on our projects. In this article, we want to focus on the most effective EDM practices and showcase real-life case studies to help you understand what works best for your company’s needs.

What is Enterprise Data Management (EDM)?

Enterprise Data Management covers strategies, processes, and technologies that organizations use to effectively manage their data assets throughout their lifecycle.

The primary goal of EDM is to ensure that data is accurate, consistent, secure, and accessible across the organization, enabling informed decision-making, regulatory compliance, operational efficiency, and innovation. Especially in the edge of AI, EDM is essential for companies looking to derive value from their data and gain a competitive edge.

Key elements of an effective EDM strategy

Effective EDM encompasses a wide range of practices, including data governance, data access, data integration, and data security. Let’s discuss them.

Data governance

Data governance is the foundation of effective EDM. It is similar to the work of an architect. Just like an architect designs and oversees the construction of a building to make sure it meets safety, aesthetic, and functional requirements, data governance is required to set up the structure and framework for managing data within an organization, ensuring it’s secure, organized, compliant with regulations, and aligned with the organization’s goals and objectives.

Data integration

Data integration is the process of combining data from disparate sources into a unified and cohesive format. This process involves consolidating data from various databases, applications, and systems, regardless of their formats or structures, and transforming it into a standardized format.

As a software development company, we implement system integration in the majority of our projects. For example, when building a portfolio analytics platform for Albin Kistler, a wealth management company, we implemented secure API-based data integrations between the client’s app and other systems.

With access to SIX apiD we could fetch financial data, including stock quotes, bond prices, and currency exchange rates. By integrating with Expersoft’s PM1 platform, we could provide information about customer ownership, and stock and bond values directly from our platform. Finally, Albin Kistler’s Active Directory was essential for user authentication purposes, eliminating the need for manual user data entry.

Data access

In the context of EDM, data access is the process of giving rights to retrieving, modifying, copying, and moving data from IT systems only to certain individuals. Security protocols, user authentication mechanisms, role-based access controls, and auditing capabilities are required to protect sensitive information, maintain data privacy, and comply with regulatory requirements.

Yet, data access is not about security only. It’s also about enabling collaboration and productivity when your team instantly gets the information they need to drive your business forward.

When implementing software solutions, our team often faces a situation where several user roles with different access levels should be created. Let’s take Rietmann & Partner case as an example. Rietmann & Partner is a company that specializes in auditing and tax consulting. With an idea to streamline and simplify their internal audit processes, they wanted to build a highly customizable solution and reached out to Modeso for help.

After studying the nuances of auditing operations in detail, we created a rule-based system that significantly improved the company’s internal audit operations. Since the audit process involves several roles, such as auditors and lead auditors, we implemented access controls with tailored permissions, ensuring that users could access data pertinent to their roles.

Data security

Every day companies across the world suffer from data breaches, and not only small ones. That’s why safeguarding your data is vital. Data security involves implementing measures like two-factor authentication, encryption, and access controls we’ve covered above, to prevent unauthorized access, breaches, and data leaks.

We talked about data security best practices in our blog post: “6 Essential Data Security Practices for Your Software Development Process." Make sure you check it out.

3 to-dos on your data management list

Now that we’ve covered the main components of EDM, let’s discuss core practices for improving your data management.

Ensure your data is easily accessible

Following this practice, you make sure the data is easily accessible, regardless of where it is stored within the organization’s infrastructure, be it on-premises, in the cloud, or hybrid environments. Thanks to optimized data access, your employees can always have the required information at their fingertips to perform their work and make informed decisions.

Here is an example from our experience. Dental Axess, a global integrator of CAD/CAM and dental imaging solutions for dental clinics, encountered challenges related to data access. Within the clear aligner production supply chain, vital data was exchanged across multiple platforms such as Dropbox, email, and Google Drive. This fragmented approach frequently resulted in data misplacements, delays, and a lack of synchronization between patient records and corresponding manufacturing data. To solve this problem, they decided to develop Xflow ‒ a comprehensive platform designed to centralize all data associated with clear aligner manufacturing. Xflow aims to streamline the entire workflow, seamlessly managing tasks from scanning patient data to producing clear aligners.

You can read more about how we helped Dental Axess implement this platform in our case study.

Make data easy to analyze

Though it seems quite similar to optimized data access, data visibility involves providing users with a unified view of data, allowing them to access, analyze, and get insights from data regardless of its source or storage location. Data visibility aims to promote collaboration and enable users to make decisions based on a comprehensive understanding of their data assets. However, access control remains important for this practice as well, so consider implementing mechanisms and policies to regulate who can view, modify, or interact with data.

Get prepared for unexpected disruptions and failures

Because any disruption in data management operations can significantly affect the organization’s efficiency, enterprises must ensure that their data remains protected and available at all times, even in the face of disruptions or failures. That is what makes service durability extremely important. It includes deploying redundant systems and backups, implementing disaster recovery plans, and continuously monitoring the performance of data management systems. By prioritizing service durability, you minimize the risk of data loss or downtime, while data remains accessible and usable for critical business functions.

Following the above-mentioned practices alone is not enough and you should be aware of some challenges related to EDM. Data silos, lack of processes and systems, resource constraints, data inaccuracy, legacy software, data overload, and more ‒ get prepared to address these challenges when managing your data.

EDM tools can be broken down into 4 categories

From data storage to data processing and analytics, there are lots of software solutions that businesses can use to manage their data. EDM tools can be conditionally divided into the following categories:

Master data management (MDM)

MDM tools help organizations address the challenges associated with managing master data, such as data inconsistency, data duplication, and data silos, by providing centralized control and governance over master data assets. The most popular software tools here are Informatica MDM, IBM InfoSphere MDM, Talend MDM, and Reltio.

Data security and privacy

This category requires no presentation ‒ it includes solutions for protecting sensitive data, monitoring access, and maintaining compliance with data privacy regulations. Tools like Varonis Data Security Platform, IBM Guardium, and Informatica Secure@Source are on the frontline to keep your data safe from hackers.

Data warehousing

Data warehouse is a centralized repository that stores structured and organized data from various sources within an organization. Think of it as a big, organized storage unit for all your company’s data. But instead of turning all the data into a huge pile of disorganized mess, data warehousing tools organize and structure it. The most popular solutions in this category include Amazon Redshift, Snowflake, Google BigQuery, Microsoft Azure Synapse Analytics, and IBM Db2 Warehouse.

Data integration

In brief, data integration tools are designed to turn data from different sources into a unified format, enabling seamless data flow across various systems, databases, and applications. Some popular data integration tools include Informatica, Talend, Apache NiFi, Microsoft SQL Server Integration Services, and more.

AI for data management

Just like with any industry today, AI impacts enterprise data management, fundamentally transforming how businesses manage their data. Take cloud platforms, for instance. With AI, these platforms can swiftly process vast amounts of data, leading to better insights and informed decision-making. What’s more, AI-powered algorithms can detect and respond to potential security threats in real-time, as well as automatically enforce data governance policies to ensure regulatory compliance.  

By embracing the power of AI, ML, and big data analytics, businesses can unlock valuable insights, streamline processes, and make data-driven decisions. That’s why it’s essential to incorporate these technologies into your EDM strategy.

If you need help with integrating technology into your enterprise data management processes, drop us a line. Our team can help you develop custom solutions that incorporate modern data management tools and practices to advance your business.

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