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Big Data Pragmatism: The Real World of Analytics Practitioners

The Real World of Analytics Practitioners

“Dream big, start small, don’t waste time, and start now.” In one sentence, De Large Landen’s Vice President and Global Head of Analytics Teuni Breg provides both a sense of urgency and word of caution against “boiling the ocean” when integrating analytics into business decisions across the organization. Her comments were the highlight at the unveiling of a new research study by Dun & Bradstreet and Forbes Insights to a gathering of big data and analytics executives in New York City in June 2017.

The study focuses on exploring the current state of analytics adoption across numerous use cases and industries. Researchers wanted to understand which companies perceive themselves as being “analytically driven enterprises” and better understand the challenges, pitfalls, and best practices associated with establishing analytics an integral part of business decision making.

“We use analytics to make more efficient decisions. In addition to finding faster, better, cheaper solutions, we use analytics to help manage the overall organization,” said event panelist Wagner Vagar of HSBC. His comments illustrated a common theme among the panel discussions. Individual analytics groups in key business functions are being asked to contribute across the organization. In a world where data scientists and analytics professionals are in high demand, companies are pooling and sharing scarce expertise and leveraging it across the enterprise.

Another event panelist, Andrew Koehler of Verizon, pointed out the difficulty in finding great people. “We're a team of about 50 people doing reporting, data management, and predictive and prescriptive analytics. Unfortunately, the predictive and prescriptive function is handled by about 15% of those people. We have about six or seven data scientists. After that, only half of our employees are doing models across finance, marketing, sales, and global operations, while the rest is being outsourced,” stated Koehler.

Many participants in the discussion pointed out that their analytics use cases stretch across the organization. Koehler highlighted his organization's use case growth. “Sales loves to hear from us, especially when we're providing a perspective on churn – predicting or revealing the risk of losing revenue or customers. In global ops, we're forecasting dispatch activities and trying to plan resources in different areas based on when we think we're going to anticipate dispatches. For our finance team, we are forecasting booked revenue results. I am trying to get them ahead of that gap at the end of the month when they're getting ready to report results. My goal is to help them anticipate what they might be seeing so they can get their messaging ready,” he said.

Chris Corinaldi of Fundation pointed out how analytics helps a start-up like his stay competitive. “Risk management historically was about minimizing losses. More and more, we're using data and analytics not so much for minimizing the losses and risks, but managing them within a certain level of tolerance. Even if you're a bank, you still have a validation obligation and perhaps a much tighter risk tolerance. We're also using data and analytics to help us aggressively grow the business within that tolerance,” stated Corinaldi.

Vagar closed the day with a comment that provided a qualitative observation that validated the findings of the research study. “I can say now, in our business, 100% of the decisions are really based on some level of analytics.”

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