Getting More From Your Big Data Supply Chain

In a recent blog post about data exploration, Forrester's Boris Evelson discusses Tableau's recent acquisition of HyPer. He notes that HyPer addresses Tableau's previous lack of in-memory data exploration capability.

Though Evelson's blog mentions Tableau and other BI providers, his broader points center around the importance of removing barriers to data discovery, especially when analyzing Big Data stores.

Getting More From Your Big Data Supply Chain

Self-Service Data Discovery

The more data and data sources your organization has, the more important it is to take IT out of the data provisioning loop. First of all, large enterprises have literally thousands of data sources that might contribute to a successful analytic outcome. So when business analysts request data sets from IT, getting the right data set becomes a process of trial and error, as IT learns the business and analytical context for which the data is intended. The business analysts often place follow-on requests to secure more data sets, once they’ve interacted with the data from their initial request. Unfortunately, this wastes everyone’s time.

That's why Attivio believes that a semantic metadata catalog rightly puts the data user in the driver's seat. When Attivio automates data profiling by spidering all of a business’s data sources, it gathers metadata about them—and adds more—as it goes.

This collection of metadata forms a searchable metadata catalog that applies a layer of intelligence to the data. It lets you search the catalog for data -- just like you’d search Amazon for a pair of ski boots. A semantic metadata catalog is a cornerstone of data democratization.

Consider that the data supply chain, especially in Big Data initiatives, has many suppliers (sources). Manufacturers don't sift through huge volumes of undifferentiated parts to find the components they need. They have a catalog of suppliers.

The same concept works equally well for business users of data who often "manufacture" or rely on data products. An ecommerce-like interface to the data supply chain gives users a meaningful starting point for finding the right data components.

Pushing Analytics to the Edges

Removing barriers to data discovery pushes analytics to the edges of the organization. Every line of business develops its own data and analytics program focused on very specific business objectives. Likewise, the growing sophistication of easy-to-use data visualization tools like Tableau has made the move to data democracy faster, cheaper, more practical, and more productive.

Since large organizations often use multiple BI tools, it’s critical that analysts have streamlined access to all relevant data. Empowering users to explore the data supply chain through a managed self-service model maintains the control that IT requires, while dramatically accelerating the data discovery process, making the organization more agile.

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