• Erwan Hernot

How Data Could Change The Power Distribution

Datafication is the process of taking all aspect of life and work and turning them into data. As examples: Google’s augmented reality glasses datafies the gaze. Twitter datafies stray thoughts. LinkedIn datafies professional networks. Expert systems datafy some parts of human expertise. As a consequence, volumes of data are staggering. But quantity is not the only factor to consider: data brings more to managers than statistics. It helps them to take actions. What does make data science different from statistics is that the data problem is getting incorporated back in real world and users interact with that product and generate more data. In others words: using data is not just predicting the future but also causing it. It could change the distribution of power in the corporate world.

From heroic to distributive leadership


Data changes the essence of leadership: from heroic to distributive. Leadership, in its classic definition, is heroic. This is the well known story of the Man-Who-Has-Answers-And Acts, usually the CEO. This leadership style could be irrelevant in the digital era. Companies don’t succeed because they have this leader but because they have more and better data AND distributive leadership teams that set clear goals, define what success looks like, ask the right questions. In order to enable theirs managers to lead from data analysis, they are willing to share data and to role model this behaviour. Managers at all hierarchical levels can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings.


Relying too much on experience


Data are familiar to heroic leaders. This is the way it is used that could change, hence changing the way decisions are made. Throughout the business world today, heroic leaders rely too much on experience and intuition and not enough on data. They need to break a bad habit: pretending to be more data-driven than most of them actually say they are. Too often, they spice up their reports with lots of data that supported decisions they had already made using the traditional HiPPO (Highest Paid Person’s Opinion;) approach. Although, a growing number of senior executives are genuinely data-driven and willing to override their own intuition when the data don’t agree with it. Few things are then more powerful for changing a decision-making culture than seeing a senior executive concede when data have disproved a hunch. Following this model, managers could spread this data driven behaviour in their team, starting with two simple techniques. First, they can get rid of the habit of asking “What do we think?” and replace it by “What do we know?” which goes in details with “What do the data say?” when faced with an important decision. They get in depth with more-specific questions such as “Where did the data come from?,” “What kinds of analyses were conducted?,” and “How confident are we in the results?” Their people will get the message quickly if managers develop this discipline.


Silos and egos slow down decision making process


If data is to transform organisations and power distribution, senior leaders need to recognise two problems they don’t tackle easily now.

Problem 1. Silo mindset The greater the task specialization in the organization, the more likely there will be disagreements between leaders. For instance lawyers are trained to see the world in one way, engineers another, and accountants yet another. Moreover, holding a particular position in an organization causes one to see the world through the information that comes with that position.

Problem 2. Data is for senior executives to analyse and acted upon This statement is wrong because speed is key. Decisions need to be made where data is for 2 reasons.

  1. Data has limits Data is often limited in scope, lacking richness and often failing to encompass important non-economic and non quantitative factors. Much data is top aggregated. The obvious solution for senior leaders overloaded with information is indeed to have it aggregated. The problem is that a great deal of information is lost in such aggregating. The bottom line tells a lot about the condition of an enterprise. But it doesn’t explain the problems of quality or the poor maintenance on the floor of a factory. What do a tracked projects review say about what is going on in research laboratory? It is fine to see the global picture but only so long as nothing is going on in the details. A surprising amount of data is unreliable. Something is always lost in the process of quantification. Anyone who has ever produced a quantitative measure a reject count in a factory knows just how much distortion is possible, intentional or unintentional.

  2. Processing data can kill insights Separating data gathering from problems formulation may be convenient for the plush corporate planning office. But it damages the relevance of strategy. Strategy is an interactive process: it requires continual feedback between thoughts and actions of all leaders from first line managers to senior executives. Strategists must learn on the field. An effective organization puts information and the relevant decision power in the same location. Very often, information is created from data but transferred upwards the organization, and expertise is often not where it needs to be. The smart leaders will create an organization flexible enough to minimize the “silo mindset” and maximize cross-functional cooperation. People who understand the problems need to be brought together with the right data, but also with the people who have problem-solving techniques that can effectively exploit them.

Open data and scientific method lessen silos and egos!


Data must be shared and made accessible for everyone! In this VUCA world, agreements about how to do things are  key, because then, you can act quickly. Goals alone are not a safe place from politics. They create territories worth to fight for. Goals, data – and a method – are! Turning data into information useful to make decisions and take actions implies an extension and a sharing of the scientific method :

  1. Ask a question

  2. Do background research and gather data

  3. Construct a hypothesis (problem formulation)

  4. Test your hypothesis by doing an experiment

  5. Analyze your data and draw conclusions aka produce information.

If data is opened to everybody, if data scientists are at the heart of a shared decision making process, if a distributive leadership empowers people to use them, then the scientific method will keep egos at large and stakeholders within the company, will discover insights, reach agreements and take the relevant actions faster.

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