Balance Your Data Management Strategy and Optimize Security and Flexibility

It’s understandable that IT teams focus on data as a sensitive asset that requires protection, but data is also an essential element for effective decision-making. Organizations often struggle to balance optimizing data value and accessibility while establishing guardrails to keep data secure.

One way to bridge this gap is through master data management, a practice that includes data governance and database organization, tied together with the right software solutions. More specifically, it is data lineage, which is within the scope of master data management capabilities, that provides greater data flexibility. When I work with organizations to improve their data management, one of the biggest issues I see is a lack of recognition of the value of properly managed data. 

An organization can gather all the data in the world, but if that data is not properly organized and verified, it’s almost worthless. Dashboards will be incorrect, predictive analytics will be inaccurate, and the decisions that rely on those information sources will be errant. This is why the fundamentals of master data management are so important.

Traditionally, data analytics programs involved a relatively small group of leaders using data to make decisions that affected broad sections of the organization — an approach we can consider macro-level decision-making. Data democratization, on the other hand, moves decision-making to the micro-level; for example, a sales team manager may use data on sales reps’ activities to inform decisions about those employees. Data democratization gives more people more access to more information, so they can make better decisions at lower levels.

However, expanding access to this data creates tension with cybersecurity objectives. The more users who have access to information, the greater the risk to that data. Data marts and data masking help organizations strike the right balance by controlling the flow of information and prioritizing data access based on the decisions individuals need to make.

Limiting data can be as important as controlling access. When intrusions occur, cybercriminals often are not targeting the protected fortress of a primary database — they’re looking for copies. Infiltration through a DevOps team is one popular tactic, because those employees likely have access to database copies that may be only a few months old.

Organizations should limit the number of copies of their data. Data virtualization can reduce data proliferation while still providing users access to the information they need.

As mentioned earlier, data lineage — understanding the history of each piece of data — is a key component of master data management. Which databases did data touch along the way? How was it changed? By whom, when and why? 

With this information, an organization that experiences issues or inaccuracies in its databases can track the trajectory of a problem to understand and correct it. For instance, data lineage can help an organization pinpoint an intrusion into its data infrastructure or identify a bad actor from within. Master data management solutions that reside on an organization’s existing systems can perform most of this work automatically.

Data lineage is easy to overlook, but it’s a powerful tool for secure, accurate data analytics. If your organization’s predictions are off, or if it struggles to understand certain aspects of the customer base, there’s a good chance that decisions have been made using faulty information. Master data management can restore the value of data and minimize the issues that organizations sometimes run into.

Story by  Drew McMahon, a field solution architect focused on modern data solutions and analytics. He has been with CDW for more than 14 years and is an expert on IBM, Qlik, Tableau, Splunk and Power BI analytics solutions.