Skip to main content

Top Five Characteristics of Effective Data Management


Effective data management can entail a large swath of processes, best practices, and tools, ranging from data administration to performing complex operations at scale with various entities and objects within multiple data silos. 

Despite the large array of functions and processes, data architecture is a key to effective data management. “There’s been a lot of change over the course of just the last five years,” said Christopher Sanders, head of data at Ippon Technologies. “If you think about the past 10 or 15 or 20 years, the changes in technology itself make a data architecture both more important and harder to keep up to date.”

Planning, planning, and more planning!

The planning process also introduces the first opportunity to identify – and rectify – pain points. An organization may already know it has a problem with data silos, for instance, when managers have had to create workarounds to get the data they need for a particular report.

“We often tell clients that some of their existing systems could be more valuable if used in unison, but they don’t have a common identifier between the two systems,” Sanders said. Going through the planning process also may shed light on the issue of data duplication, as when users draw data from one source and then save it for their use in the future.

There are risks to either not having a thoughtful, defined data architecture or having one that is out of date. The organization’s systems may be too inflexible to meet its future-looking requirements. The lack of a defined architecture means a lack of transparency about the data and system resources available, their condition, and their usage.

Integration and interoperability

The second characteristic of effective data management is improving data integration and interoperability across the organization. This presents significant opportunities to improve operations. Anything that reduces redundancy will cut costs. Additionally, it increases organizational confidence in the data.

Just as a defined data architecture provides consistency for how data systems are set up, data integration allows a cohesive and consistent business logic to be applied across all the systems that draw on that data.

Since the ultimate purpose of compiling all that data is to gain insight and advance the organization’s interests, undertaking systematic processes to combine the data and enable pulling from a single unified pool leads to broader and more timely business insights.

Data Quality

The third characteristic of effective data management is achieving good data quality. This involves processes and measures to ensure data is error-free and fit for its intended purposes. It includes data validation, cleansing, and profiling techniques to identify and address data anomalies or discrepancies.

“Today most organizations know that it’s a missing link,” said Hayley Ortega, Ippon’s head of client success. “Among the symptoms of bad data quality is redundancy, which in turn leads to higher costs related to data. They may be experiencing performance issues. They may be having inefficiencies because they don’t know who to talk to about a certain dataset, or their data’s just not in a good format to run models with it.”

When it comes to bad data, the symptoms often correspond to the risks – lost revenue and inaccurate analytics (which in turn lead to poor decision-making). Sometimes overlooked is damage to the organization’s reputation or brand.

Metadata Management

The fourth characteristic of effective data management is metadata management. This is the descriptive information attached to data – its structure, meaning, origin, and usage. If the data quality is suspect, then so is the metadata.

Managing metadata means capturing, organizing, and maintaining it to facilitate data discovery, understanding, and governance. This involves building metadata repositories, data dictionaries, and data lineage tracking (such as who generated the data, who used it, and when, etc.).

The importance of metadata management lies in its risks, such as applying outdated business rules to data and the time wasted verifying data definitions and sources. Most importantly, without metadata management, there is no single source of truth – the data the organization relies upon is accurate, comprehensive, and verifiable, and everyone in the enterprise can trust it.

Master Data Management

The fifth characteristic of effective data management is master data management. Master data management involves creating a single, authoritative source of master data and ensuring its consistency, accuracy, and integrity across the organization.

The risks attached to the failure to manage an organization’s master data include creating redundant data, including changes reflected in one place and not others. Related, it leads to inconsistent, incomplete, or obsolete data being used in multiple systems or applications. And it adds to the high cost of data storage and maintenance.

By emphasizing master data management, organizations consolidate data consistency across systems, along with providing reliable, complete, and timely data. These benefits lead to lower costs in maintenance, which also translate into maximizing the investments organizations make.

For more information on creating the best data governance for your organization, visit or download our latest eBook "The Future of Data Management" here.

Post by Steven MacLauchlan
March 13, 2024