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Data driven organization

45 percent of work could be automated with current technologies and 80 percent of work is attributable to existing machine learning capabilities. [1] Automation and the change of employee roles and responsibilities make a big impact on change management. People don’t have to solve anymore mundane tasks, only the most difficult and complex problems are the ones going to the employees. The most important task for the modern employees is to support the ongoing development of new technologies. [2]

Data-driven culture relies on the quality of the data and the understanding of the data business wise. Your organization must be able to trust the data. It doesn’t get you far, if you are just able to collect a lot of data in your data lake. You must be also able to clean the data, enrich it, combine it with the data from other sources and then understand all of it. After that, your analytics, predictions and your AI will be beneficial and effective.

Man sitting next to a drawing of a planet with different icons

Benefits

Data-driven organizations can make sense of a broad range of structured and unstructured data, and apply that knowledge to business planning, budgeting, forecasting and decision supporting. Organizations can predict outcomes more efficiently and simulate the outcomes for a wide range of uses. Identifying competitive advantages gets easier. Relevant dashboards to measure success and to drive success can be created. [3]

According to McKinsey’s report, data-driven organizations using customer analytics are 23 times more likely to outperform in new-customer acquisition, 6 times more likely to retain customers, and 19 times more likely to have profitable results. [4]

Culture

The change in the organization culture might be the most difficult task towards data-driven world. If you have always used your gut feeling for decision making, it must be difficult to rely just on data.

Here are some examples on sports how the culture change can be difficult. Wilt Chamberlain was maybe the best basketball player of his era. He had only one weakness – he couldn’t shoot free throws. Data showed that shooting free throws underarm has a much better success percentage than shooting overarm. Wilt Chamberlain changed his style to shoot free throws underarm for a short time with a success – and then switched back. The reason to switch back was that the underarm style was called “Granny style”. [5]

Another example is the penalty kick in World Cup football. The ball has a speed about 100km/h and it travels only about 11 meters, so the goalie must predict where to go to save the goal. According to a study, the best place to shoot is in the middle of the goal, because the goalie is standing still only 2 times out of 100 penalties. The reason why players don’t kick the ball in the middle of the goal is that the players would look so stupid on those few times when the goalie stands still.

By the way, there is a 60% chance to win in a football shoot-out if your team can shoot first.

 

References

[1] McKinsey report: The age of analytics: Competing in a data-driven world (https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world)

[2] McKinsey report: Getting big impact from big data (https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/getting-big-impact-from-big-data)

[3] KPMG: Data-driven-business-transformation (https://assets.kpmg.com/content/dam/kpmg/ca/pdf/2017/01/data-driven-business-transformation-final.pdf)

[4] McKinsey report: Five facts how customer analytics boosts corporate performance (https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance)

[5] Malcolm Gladwell: The Big Man Can’t Shoot (Revisionist history podcast)