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Making Data Discovery Easier

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Making Data Discovery Easier

Imagine if searching for data was as easy as ordering from Amazon. When we go to an online store we search for the items we need, filter, find them, compare and review them. We select our items, move them into our online shopping cart, and, with astonishing immediacy, our purchases are delivered to us. Based on our purchase history, similar products are recommended to us, which we can consider, purchase, or ignore. The process is simple, intuitive, and even for the die-hard bricks-and-mortar shoppers among us, irresistibly convenient.

Contrast that with what most of your business users experience when they set out to look for the data they need to do their jobs. At a time when data volume and complexity is increasing, they often don’t know where to start looking—and spin their (and other people’s) wheels uselessly chasing down false leads. Or they encounter a sea of data with no context that’s meaningful to them—and so miss finding good data relevant to their question. Or they discover data that’s rife with quality issues—and so become distrustful of their data sources.

Your business is likely awash in data—and that can be a good thing. Aggregating data from across multiple sources can provide valuable insights your business can use to improve the customer experience, innovate more rapidly, find new efficiencies, and build your competitive edge. But when data is not easy find, when it is difficult to understand, and when trust in that data is low or nonexistent, your data has no worth.

How, then, do we bring the Amazon experience to the world of data? One way is to demand a simple and intuitive data catalog that allows business users to find and shop for trusted data from one central location. And why not go a step further and incorporate machine learning functionality so that the catalog can “learn” from a data user’s past behaviors to make specific recommendations for similar “data purchases,” much like Amazon does for frequent shoppers.

Delivering good data is key, of course. And that can’t happen without a strong data governance foundation to ensure that data quality is high, ownership is clear, and accountability is transparent. For business users who need quick access to enterprise data, a data catalog populated by data that the organization agrees is valuable is a god-send. Wasteful hours spend searching for (sometimes useless) data are a thing of the past and your business users can now spend more time doing what you’re paying them to do: solve real business problems.

With technology models borrowed from the consumer world, businesses are finally gaining the power to find the right data quickly, evaluate its lineage, and enrich its value. As a result, they’re unlocking the power of data to serve as an actionable tool for competitive advantage.

 

Stan is the co-founder and CTO at Collibra and leads the global product organization. He’s responsible for product management and UX, Collibra’s Center of Excellence, and Collibra University, Collibra’s online learning platform. Prior to founding the company he was a senior researcher at the Vrije Universiteit of Brussels, a leading semantic research center in Europe, performing application-oriented research in semantics.