Transformative analytics: Three steps toward an insight-driven advantage


Posted by Nitin Mittal on May 5, 2017

How can hospitals reduce insurance company reimbursement denials that cut into their revenue? How can fleet operators leverage enormous data sets soon to be generated by vehicle-embedded sensors? How does an oil company optimize wellhead operations on an offshore rig? Transformative analytics could hold answers to these and many other outcome-focused questions.

Transformative analytics is a term used to describe corporate executives’ ability to define and pursue desired business outcomes rather than getting bogged down in the technological details of how insights are uncovered, then having to figure out what they mean.

It is the product of two major trends that are shifting the way corporate decision making is carried out: the continued proliferation of data and new “exponentials” technology. The data contains latent insights waiting to provide solutions to business problems and opportunities. The exponentials—cognitive computing technologies—are keys to unlocking those insights.

The data is certainly available to answer such questions: By one estimate, more than 7 million patients worldwide transmitted data to providers using home health monitoring devices in 2016.1 An estimated 250 million connected vehicles will be on the road by 2020,2 generating enormous data sets for manufacturers, dealers, and consumers. Large oil and gas companies are producing some 1.5 terabytes of data daily from sources such as oilfield equipment sensors that is stored and analyzed to perform predictive maintenance.3 The challenge is locating the answers in the midst of such huge data volumes—it’s like looking for a particular needle in a stack of pins and needles a mile high.

That’s where exponential technologies come into play. Knowing what the desired outcome is—e.g., reducing reimbursement denials and predicting fleet and oilfield maintenance issues before they become problems—organizations can use machine learning, predictive analytics, artificial intelligence, and natural language processing tools to find patterns in their data that lead to solutions.

How can organizations begin to capitalize on the potential of transformative analytics?

  1. Formulate and embrace an outcome-based analytics strategy and identify the capabilities to execute it. This includes operating model design, program management, and change management.
  2. Build the infrastructure. Implementing the strategy will require data management and information delivery capabilities that support improved performance, deeper intelligence, and richer insights. Some of these capabilities may already exist in your organization, while others may need to be developed.
  3. Establish the operating processes needed to execute the analytics strategy. This will involve choosing an effective service model. It could even lead to opportunities to monetize your organization’s data, perhaps by packaging and reselling it or by trading insights in a data or analytics consortium or exchange.

Are you seeing opportunities to use transformative analytics in your organization? We’d like to hear from you.

1 Johan Fagerberg and Anders Frick, “mHealth and Home Monitoring,” Berg Insights AB, Januay 2017, http://www.berginsight.com/ReportPDF/ProductSheet/bi-mhealth8-ps.pdf.
2 “Predicts 2015: The Internet of Things,” Gartner news release, January 26, 2015, http://www.gartner.com/newsroom/id/2970017.
3 Abdelkader Baaziz and Luc Quoniam, “How to use Big Data technologies to optimize operations in upstream petroleum industry,” 21st World Petroleum Congress, June 19, 2014.

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