Managing Massive Unstructured Data Troves: 10 Best Practices

by Chris Preimesberger

With the variety of data sources exploding exponentially, this important addition should be at the forefront of a data management project.

Any data management project should begin with a primary business goal, which dictates how the data is analyzed and correlated.

Organizations are facing exponentially growing varieties of data from emails, enterprise resource planning (ERP) systems, CRM systems, databases, file shares, communities, social media and so on. The full data landscape across channels must be uncovered before correlating the data into any relevant insight.

Historically, the remedy for disparate data was to move it to a centralized data warehouse. But moving the data is a huge task, and these centralized data warehouses quickly turn to legacy systems and an increasingly complex data environment.

Replication happens when data is moved to seemingly eliminate complexity. It often leads to a more complex problem where duplicate data is now housed in multiple places. Companies should seek out duplicate data and remove it, when possible.

Indexing technology reaches, consolidates and correlates all data and information sources, wherever they reside, securely.

This process uncovers critical information relationships that previously couldn’t be discovered. They’re some of the most powerful insights you can uncover.

Every company has subject-matter experts. When indexing aggregates all available work by an individual, experts are defined by true contribution—not a static profile.

Data on its own is meaningless. By combining data points, users have complete visibility into all systems and access to subject-matter experts, and can uncover correlations previously unknown. This creates new insights needed to solve complex business problems.

Knowledge becomes a highly valuable asset when leveraged to make critical business decisions. This leads to greater returns on a data management investment.