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IBM Canada president Dino Trevisani. (Stephanie Lake)
IBM Canada president Dino Trevisani. (Stephanie Lake)

LEADERSHIP LAB

Why data insight is the crystal ball leaders can’t afford to be without Add to ...

This column is part of Globe Careers’ Leadership Lab series, where executives and experts share their views and advice about leadership and management. Follow us at @Globe_Careers. Find all Leadership Lab stories at tgam.ca/leadershiplab

What leader wouldn’t want a crystal ball? Or an app with the same capabilities? A leader is supposed to be able to see around corners and into the future in order to make decisions that take people and organizations to new places. In practice, we expect our leaders to have some combination of wisdom, communication skills and maybe even charisma, but what they most need is the ability to accurately predict where their decisions will lead.

“Big data” may have been the leadership term of 2014, with most organizations realizing they need an approach to collecting and using the oceans of data produced by every action and interaction. But in 2015 the most successful will be those who can most quickly convert that data into meaningful and accurate predictions of a future state – those who can predict what the data means in near real-time. In today’s always-on, data-driven world, the prediction horizon has been compressed from years to minutes. Data insight is the new crystal ball.

That’s because big data analytics tools help leaders better allocate resources, develop new revenue streams, personalize services, sustain competitive advantage and manage risk.

Plan Flight, an app designed for the airline industry that helps pilots make the best decisions on how much fuel to carry, does all those things. Fuel is 30 to 40 per cent of an airline’s operating budget and it is the pilot who decides how much fuel is loaded on to the aircraft before takeoff. Carrying excess fuel can cost airlines millions of dollars per year. The app combines five years of historical data for that day of week and time of day with real-time weather and airport conditions. It enables the pilot to test different scenarios for various outcomes in order to make the right decision for safety while minimizing cost.

For airlines, more tightly managing fuel costs while maintaining safety becomes a competitive advantage in what has become a largely commoditized business. In fact, survey results have shown a “tougher competitive environment” is by far the strongest reason organizations adopt analytics. In the case of airlines, predictive analytics can be applied across the supply chain to predict where and when they will need fuel reserves.

It can also be applied to prevent theft. Criminals engaging in mortgage fraud steal $4-billion (U.S.) to $6-billion yearly in the United States. (No comparable data for Canadian banks is available.) With predictive insights from analytics, one bank stopped a fraud network that stole approximately $140-million while improving its ability to predict, detect and prevent future mortgage fraud.

Deciding where to invest resources is just as compelling a challenge. Consider the field of medical research. For every 100 discoveries made in medical labs, only 15 ever make it to the real world in the form of new therapies or treatments, revised health care practice guidelines or better health policies. In other words, by far the bulk of money invested in health care research never helps a sick person get better.

Yet, medical professionals can improve patient care and reduce costs by extracting relevant clinical information from vast amounts of data to better understand the past and predict future outcomes. That’s why Memorial University recently launched the Translational and Personalized Medicine Initiative, which aims to help predict what research and process and policy changes will have the greatest effect on improving patient health. By looking at ongoing patient data and recognizing historical patterns about health, combined with laboratory and genetic data, health care practitioners can make recommendations that help keep people healthy rather than waiting until they become ill.

As leaders look ahead to how they will get better at using data to predict the future and improve outcomes, here are a few considerations:

1. Find the people who have both analytical and leadership skills and work to develop these skills in your community. If you relegate predictive analytics to an IT silo, your business will never fully benefit from its transformative capabilities. Similarly, if you don’t partner with higher education institutions and help build new skills within your organization, future innovation stagnates. University of Toronto students are currently working with the latest cognitive computing technology as they learn to think like high-tech entrepreneurs and develop effective, commercially successful business plans that solve big data challenges. These opportunities are only possible if leaders partner with leaders to invest in skills development. And as Professor Florian Zettelmeyer, of the Kellogg School of Management says: “Big Data Doesn’t Make Decisions, Leaders Do.”

2. Take a holistic approach, as you should with all your data resources. Run a pilot project if you must, but ultimately the value comes from mining all your data streams, in whatever form they take, to paint the most accurate picture of the future in all your scenario planning. Any variable can change your future. We worked with a European retail bakery and found that weather affected the types of products that they were selling. When it rained, they moved more cakes; when it was sunny, they went through more panini. Once they understood that weather was a big determinant, it completely changed how they approached sales, inventory and other areas of the business.

3. Use your leadership. The Vancouver Police Department has long been considered a leader in law enforcement technology. The force uses analytics to discover relationships and causal linkages and to predict where criminal activity is developing or moving. The VPD is then able to deploy more – and better-informed – officers in the community, to help prevent crime.

Analytics is already in use to predict earthquakes and tsunamis, credit card fraud, neighbourhood crime, health risks and the likelihood that a specific person will buy a specific product, among many other things. Now that the world understands the power of big data, predictive analytics will continue to grow as a powerful competitive advantage and risk-management tool. One prediction I’m certain of is that tomorrow’s most successful leaders will know how to put it to work.

Dino Trevisani is president of IBM Canada.

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