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How Document and Information Processes Help Feed Big Data Initiatives

How Document and Information Processes Help Feed Big Data Initiatives

Achieving the strategic benefits of Big Data analytics means we need to think more broadly about getting value out of all our information.  In writing The Big Data Conundrum some time ago, I observed that by taking a closer look at business processes, including the management of information contained in documents, businesses can more fully realize the benefits of Big Data analytics and data-driven decision-making—just one being increased profitability.

This has been substantiated in research by Andrew McAfee and Erik Brynjolfsson of the Massachusetts Institute of Technology (MIT), which found “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”1

Currently, when reviewing the information flowing through our business processes, we often focus on departmental, tactical objectives:  expediting invoices in Accounts Payable, faster employee onboarding from Human Resources, more efficient contract management in Legal, to name a few.

By focusing on these tactical goals we may overlook the role of improved processes in the success of Big Data initiatives. In addition the value of structured data in enterprise data warehouses, there is valuable business information contained in unstructured documents, much of it unrecognized, and constantly flowing through existing processes.

By focusing on these tactical goals we may overlook the role of improved processes in the success of Big Data initiatives

Tapping this value means stepping “out of the box”, starting with asking questions such as: “What if we knew…“? “What if we could measure…”? ”What if we could predict…”?

Certain business processes generate more structured data: simply put, data found in rows and columns. If such information is deemed valuable enough to retain, it typically resides in enterprise data warehouses.

Importantly, these datasets can include external sources such as citizen/patient data, public or subscription datasets and sensors or machine monitoring data. 

Structured data requires far less preparation for the analytical technologies commonly used for Big Data. Depending upon the scope and clarity of the strategic objectives, looking for valuable, actionable information through analysis of structured data can be relatively straightforward.

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Highlights from the Full Paper

  • Businesses can benefit from analyzing and cataloguing big data because this information can increase profitability, among other things
  • Unstructured data (blogs, emails, other web content, etc.) must be processed before analysis: this is the challenge for innovative information processing
  • Business processes and communication across all departments and agencies must be streamlined in order for big data analysis to be successful
  • Managers and big data analysts must work together to initiate projects to improve information processes & utilize big data analysis
  • Access to big data information will better inform data-driven decisions  & improve customer service and competitiveness

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1 McAfee. Andrew and Erik Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review, October 2012.

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