3 Ways to Bridge the Data Discovery Gap

You understand that data is the lifeblood of innovation and competitive differentiation. The key is to get the right data into the hands of business analysts when they need it. Sounds simple in theory, but challenges abound. However, for every challenge enterprises face surfacing and connecting the right data, there is an answer.

Let me explain:

From Process Bottlenecks to Busting Bottlenecks

For any data gathering process to work well, business and IT must be aligned. When they aren’t on the same page, when there needs to be a continual back and forth discussion about what data is needed, there is a bottleneck. IT doesn’t know what the data means, business analysts and data stewards don’t necessarily know what data is there.

To bust the bottlenecks, put an automated process in place that supports ITs role of data gathering and provides the analysts with self-service data selection.

From Slow to Speedy Data Integration

Data integration is slow partly because of the process bottlenecks mentioned above. If it takes a long time to find the right data, then it will take a long time to make the connections between the data sets. And it’s the connections - the relationships - that make the data meaningful.

It doesn’t have to be slow. When you automate the data gathering process, you take away the manual work IT needs to do. But you don’t just gather the data; this automated process will index it and create a catalog of datasets from which the business can search.

From Incomplete to Complete Data

Every enterprise deals with dark data - the hidden datasets within the enterprise that someone might know is there, but it’s not well understood or easy to find. What happens is IT provides only some of the datasets to leverage for analysis, and the business is left with wondering if they are doing analysis on the right data.

A 360-degree view of enterprise data is possible. This dark data can be surfaced with the right tool. This tool can scour the enterprise looking for data sources, cataloging them as it goes. It can also pull in external data sources that are key to the insights the business is trying to surface.

Master Data Management Isn’t the Answer

Master Data Management (MDM) has its place in the enterprise, but a semantic data catalog gives a view into all your data: structured, unstructured and semi-structured. It can deal with new and emerging data sources, and it can bring in relevant external data.

The semantic data catalog is virtual - so it can scale easily as your data grows (and it will grow). But even more important, an intelligent layer is added to give meaning to the data, to help find the connections that the business needs.

Download our new white paper, Bridging the Data Discovery Gap, to learn more about these three contributors to the data discovery gap and how you can bridge them.




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