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Leverage Data Source Discovery By @Attivio | @CloudExpo #BigData #DevOps #Microservices
Data source discovery is the engine that drives Big Data analytics

Leverage Data Source Discovery to Become a Data-Driven Organization

As enterprises capture more and more data of all types - structured, semi-structured, and unstructured - data discovery requirements for business intelligence (BI), Big Data, and predictive analytics initiatives grow more complex. A company's ability to become data-driven and compete on analytics depends on the speed with which it can provision their analytics applications with all relevant information. The task of finding data has traditionally resided with IT, but now organizations increasingly turn towards data source discovery tools to find the right data, in context, for business users, data scientists, and BI analysts. These tools provide self-service data access speeding time to insight.

Data Source Discovery: The Great Divide
Every analytics project goes through a data source discovery stage when a business user or analyst submits a request to IT to find out what data is available for solving a problem or answering a question. Eventually, IT returns a list that the user narrows to a select few sources from which IT can build a data mart. This cycle repeats until the analyst has the right mix of information.

According to Forrester, organizations spend 80 percent of any analytics initiative on data integration. That means only 20 percent remains for developing business insights. And it can be even less. One data manager from an investment bank noted that data discovery and integration consumes upwards of 90 percent of every analytics program his firm undertakes. Sound familiar?

Moreover, 80 percent of data integration is spent on data source discovery - identifying and profiling data sources. So 64 percent of an entire analytics project can be consumed by a process that typically only scratches the surface of potentially usable data. As much as 90 percent of information stored by organizations today remains unknown and untouched.

As more connected devices and the Internet of Things (IoT) send us ever larger volumes of data, the importance of data source discovery can't be ignored. That would mean ignoring critical insights that improve decision making - and leaving substantial revenue and cost savings on the table. The lack of good tools for data source discovery continues to narrow the process bottleneck between data managers who own the data and business users who need access to it.

Failure: The Consequences of the Data Process Bottleneck
A recent Gartner poll found 60 percent of Big Data projects fail to go beyond piloting and experimentation due to an inability to demonstrate value or because they cannot evolve into existing EIM processes. Looking at these failures more closely, Svetlana Sicular highlights several reasons for "Big Botched Data," two of which tie directly to data source discovery:

  • Asking the wrong questions and lacking the right skills. IT does not have the business domain expertise to pull the right data. It simply doesn't have the necessary context that business users possess. On the other hand, business users and analysts don't know where all the relevant data lives nor do they have the technical skills to access it should they be able to locate it.
  • Big Data silos. While Hadoop has made the storage problem easier, it hasn't solved the challenge of finding and sharing data across the enterprise. Often individual business units create their own Big Data environments that other groups can't access. And not only do these environments silo the data, they also making sharing insights more time consuming and difficult.

Success: What Does a Data-Driven Organization Look Like?
Data-driven organizations have learned - often the hard way - that while having an ambitious Big Data vision creates excitement, only a thorough focus on the basics produces long-term results. So these organizations invest in automated data source discovery tools, which:

  • Reduce the time spent on gathering data
  • Leverage the untapped potential in hidden information
  • Accelerate BI initiatives through data self-service

Data source discovery is the engine that drives Big Data analytics. It sets the stage for greater revenue, profitability, and operational efficiency.

About Stephen Baker
Stephen Baker is the Chief Executive Officer for Attivio, The Data Dexterity Company, where he previously served as COO. With over fifteen years of experience as a top executive within enterprise software, he has a proven ability to build and lead high performing organizations. Prior to Attivio, Stephen spent nearly six years as the President and Chief Revenue Officer for RAMP Holdings (formerly EveryZing) and, previously, served as the CEO of RB Search (a Reed Elsevier company). Earlier in his career, Stephen spent almost eight years at Fast Search & Transfer, where he served as General Manager.

Stephen holds an MBA from the University of Pennsylvania – The Wharton School as well as a BS in Music and Marketing from Hofstra University

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