3 Mistakes Good Leaders Make
“The curtain goes up on this year’s Mars missions with the orbit insertion of Mars Climate Orbiter.”
It was September 1999 when Dr. Sam Thurman made that momentous announcement. As NASA’s flight operations manager for the Mars project, Thurman was understandably proud. Launched nine months earlier, his satellite baby was finally ready for prime time.
The Mars Climate Orbiter was the first in a $235 million space mission, and it had traveled over 416 million miles to reach its destination. All that was left now was for the satellite to settle comfortably into orbit around the red planet. From this day forward, it would track otherworldly weather patterns for scientists to study on Earth. At least that is the way it was supposed to go.
A televised press conference was scheduled for 8:00 a.m. PDT on September 23, an opportunity for Dr. Thurman to report on the orbiter’s success. Except something was not right. Just after 9:00 a.m. on September 23, 1999, the spacecraft strayed off course, dipped too close to the Martian atmosphere, and burned to ashes 400 million miles from home. Dr. Thurman’s grand vision was a complete loss, a failure of interstellar proportions.
Mistake 1: Failure of Truth
Why did Dr. Thurman and his team fail? It was not for lack of expertise: NASA engineers are consistently among the finest minds in the world. It was not for lack of money: the Mars Climate Orbiter mission was fully funded. Nor was it because of common leadership flaws like hubris or greed or recklessness or disloyalty. The problem was a failure of truth, that is, failure to create a team unified by the same data, playing from the same data playbook.
Computers on the Mars Climate Orbiter were programmed to understand metric system measurements, in this case Newton-seconds. At the same time, engineers from Dr. Thurman’s team were sending navigational commands to the orbiter in English measurements, pound-seconds, thereby guaranteeing it would fly off course. Because there was no single source of truth governing its journey, the satellite never stood a chance.
It is tempting to judge Dr. Thurman harshly. Data truth seems like such a simple concept. Yet the fact is that his error is one that thousands of mid-market executives make every day. Business teams work from disparate data sources and conflicting origination dates, making faulty decisions and sending disastrous commands because not everyone has the current, core information needed to work together efficiently. Jon Thompson, founder of Blue Margin, calls this a “mash-up scenario” and describes the cost:
You are left mashing up your data from several sources into a single file. In this mash-up scenario, the painful cost comes when you want to add or change data sources, or change reporting platforms. Even updating a source system to its latest version can break your reporting, which can be costly to fix.
Jon Thompson, Founder, Blue Margin
Thompson recommends a Modern Data Warehouse as the solution: a central data store that organizes data from any and all sources, updates dynamically in real time, and is accessible to everyone on the team regardless of department, division, or job function. This kind of warehouse provides a single source of truth from which to make proactive leadership decisions, even when confronted with massive amounts of data. You can learn more in the guide to Best Practices for Data Warehouse Architecture.
Mistake 2: Failure of Bias
The story is told of twin brothers who lived on a farm in Missouri during the 1800s. One was irrepressibly optimistic, and the other a confirmed pessimist. For their birthday one year, their father decided to change their ways of thinking. While the pessimist was sleeping, he filled the boy’s room with toys and candy and all kinds of wonderful gifts. In the optimist’s room, he dumped a wheelbarrow full of manure.
Next morning he found the pessimist child surrounded by his fantastic new toys and games, weeping bitterly. “Look at this!” the youngster wailed. “Now I have to spend days reading instructions. I will be constantly buying new batteries for these toys. And it will take me hours to clean up after my friends come over to play. What a horrible birthday this is!”
The father then peeked into the room of the optimist, and found that son dancing joyfully around the big pile of manure in the middle of his floor. “Oh, Daddy!” the boy cried with glee. “This is the best birthday ever! With all this horse manure in my room, there must be a pony nearby!”
It is easy to chuckle at this classic joke, until it starts happening in the boardrooms and executive offices of your own mid-market company. In psychological circles, it is known as “confirmation bias.” Psychologist and behavioral economist Dr. Shahram Heshmat explains it this way: “When people would like a certain idea or concept to be true, they end up believing it to be true. Once we have formed a view, we embrace information that confirms that view while ignoring, or rejecting, information that casts doubt on it.” Put simply, people are prone to believe what they want to believe.
There is no doubt that effective leadership must entail a certain amount of intuition and, occasionally, an unfounded confidence in unseen things. But when a corporation invests in a bias-driven approach over a data-driven enterprise, that is a failure of leadership. Research on data-driven businesses consistently finds advantages including better leadership decision-making, proactive command instead of reactive chaos, the ability to identify new business opportunities before the competition, improved capacity to detect potential threats early enough to mitigate them, ongoing cost savings, and better operational efficiency.
Dr. Heshmat warns that confirmation bias is “a form of self-deception” that “can be numbing you from harsh reality or turning a blind eye to the tough matter of gathering evidence and thinking.” The solution he recommends is to “set your hypothesis and look for instances to prove that you are wrong.” In a data-driven business, this means setting measurable goals and identifying the key performance indicators that will track the relevant data and provide a clear, unbiased glimpse into what the current situation really is and how to move forward.
Mistake 3: Failure of Change Management
The third mistake good leaders make is simply one of change management. After creating the data systems to foster success, they assume that everyone else in the organization will immediately jump on board the data train and start using their powerful new tools without delay. Unfortunately, that is just not the way human nature works.
Research estimates that an astounding 70% of organizational change efforts fail, with one of the top reasons being lack of user buy-in. In his guide, Dashboard Design to Ensure Adoption, Thompson tackles that potential breakdown head-on.
“Most BI fails to get adopted,” he says, “for the same reason no single mobile phone manufacturer has surpassed the iPhone in number of units sold. Although competitors offer similar functionality, their phones simply are not as user-friendly. The same phenomenon happens with dashboards.”
Thompson insists that getting employees to buy in to a data initiative begins before the reporting dashboard is built, at the point of original design. “We are consistently amazed at the number of sophisticated businesses who suffer with poorly-adopted reports and dashboards that were designed by intuition, rather than a structured process.” Change management must be part of the data solution: bring your business goals to report design so the data is compelling and triggers a response, offer ongoing support that helps your people own and achieve measurable goals, and deliver hands-on training so your team maintains control and ownership over the tools they use every day.
Good leaders sometimes make mistakes, it is true. But when you proactively utilize data-driven intelligence, failures of truth, bias, and change management simply do not have to appear on your resume or your company’s bottom line. Talk to Blue Margin about building the data foundation that eliminates all three.
Three Key Thoughts
“Teams work from disparate data sources and conflicting data origination dates, making faulty decisions because no one has the current, core information needed to work together efficiently.”
“When a corporation invests in a bias-driven approach over a data-driven enterprise, that is a failure of leadership.”
“Getting company users to buy in to a data initiative begins before the reporting dashboard is built, at the point of original design.”
