Digital transformation expert Mark Schrutt reveals how the world’s top companies are using vast amounts of data to inform their decisions, disrupt industries, and get closer to their customers. Businesses that continue to rely only on intuition do so at their peril.
What if you had the data you always wanted and could tell what was truly an emerging trend that would forever change your industry? Shifting the Balance analyzes the turn towards data-driven decision-making and describes how best-in-class organizations use data to shift their field of vision so it is forward-looking instead of reactive. Case studies with practical examples of how leading businesses address key challenges on the path to becoming data-driven include:
- How companies such as Hewlett-Packard and Land O’Lakes, whose industries are defined by resellers, are connecting directly with their customers to improve satisfaction and relevancy
- How data-driven decision-making shaped the largest one-sided deal in sports, paying the owners of a team that did not play a game for 40 years over $800 million
- How companies such as Peloton and UberEats are using data-driven decision-making to disrupt and reimagine the fitness and restaurant industries
- What professional sports franchises such as the Oakland A’s, Philadelphia Eagles, and Toronto Maple Leafs can teach us about using data in game-changing business decisions
Shifting the Balance offers a roadmap that will enable organizations to make better business decisions that drive even better results, and provides a fascinating read along the way.
Experience and a little PMJ
Data isn’t just spreadsheets or clicks of a mouse. Customer input through surveys and focus groups, Nielsen set-top rating systems, and even experience, the lessons you learned in the past, can be data points. "Experience is critical in the armed forces," says Royal Canadian Air Force Lieutenant Colonel Michael Whalen. The RCAF and other defense units have a term for experience — PMJ, or professional military judgement, and it is called on for decisions ranging from ordering supplies to when a solider may find themselves under fire and experience may be their best judge of action to take. "When my butt is on the line, when my aircraft is under fire, I want to be the one that makes the decision," Whalen says.
In sports, they call it understanding tendencies. Before you could track the exit velocity of a batted ball or designed infield shifts for pull hitters, you had scouts who watched rookies take a few hundred cuts, or high school coaches sending in reports on prospects. The data and analytics of today, though, are lightyears beyond handwritten scouting reports or Xs and Os on a chalkboard. It is not only far more advanced, with enormous amounts of data points (big data) but is predictive and prescriptive (suggesting course of action).
One of the earliest and easiest examples of predictive analytics is the FICO score created to assess the credit-worthiness of an individual. Developed in the early 70s, FICO is a model that uses massive amounts of credit data to enable a bank to quickly anticipate a creditors ability to repay a loan. While there has been significant criticism around the inherent bias of FICO, it is a simple example of the early use of big data and predictive analytics.