In 2012, the sentence “data is the new oil” was publicly recorded for the first time. Its attribution is not certain, but the analogy deserves some reflection and explanation. Investor Ann Winblad said “Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity; so must data be broken down, analysed for it to have value.” As of today, a lot of companies have realised there is value in data, but not many have yet understood how to maximise this value.
Attending to a lot of industry conferences, the majority of talks are around tools and techniques where data is used to make sales increase, costs decrease, to optimize clicks… but the feeling is that we are listening to isolated examples from some business unit leads who are courageous and innovative, not manifestations of a cultural paradigm shift that companies have experienced.
Why should companies make efforts to put data at the centre of the decision making process?
Data-based decisions are better decisions. They offer deterministic expectations, they allow careful and surgical planning of resources, and they enable deep and meaningful understanding of facts. When there is a scientific model at the base of a forecast, everybody can agree on what good looks like before anything is done, with little room for people to misinterpret what a number means and what success looks like. Even a bad decision, when data-based, can be good because the error can be analysed and avoided in the future.
A simple example: is 10M$ sales in a quarter a good result or a bad result? In a world with no deterministic expectations what would happen is probably the following:
– A salesperson would say “we did amazing, 10M$ is a 20% lift vs last year and the market is growing at 10% so we delivered over and above any reasonable expectation”
– A marketing person would say “this great result happened because of our efforts, we did great campaigns, both online and offline, making our product visible to millions and millions of people, generating 10M$ sales”
– A finance person might notice that 10M$ in sales was actually achieved with 12M$ investment thus generating a 2M$ loss so the result was not good at all
– A supply chain person might point out that the 10M$ in sales in the quarter were hard to sustain and supply is now low so the expectations for the following quarter might have to be revised down because new units have to be produced before they can be sold.
It’s all relative and, in a world like that, it is very difficult to call out what the outcome of each action really was, and what the consequences for that outcome should be. With refined models, people can clarify, ahead of any action, how they would feel if that was the outcome, agree on what metrics define the success or failure of an initiative, and drive it with confidence.
What should companies do to put the data at the centre of decision making?
The key element is not tools, it is people. To harness the power of data, companies need to grow and recruit people who have particular skills. People that can: synthesise complex analyses in meaningful insights; work on large datasets using the most powerful and recent tools/techniques; ask the right questions; understand the business; who are driven by curiosity and genuine desire to understand things. I am not referring to cubicle-type data-nerd data-crunchers, I am referring to data-savvy business professionals, people that can sit at ease if front of C-level executives and have a conversation about a business issue, and then go back to their desk and figure out a way of solving that issue, using data.
The right people will drive a cultural change. The right people will refuse to pull data all day, it’s a repetitive job and depletes smart people in a very short time frame. The right people want to have a part in the decision making process, want to have a seat at the table and want to be listened to. These people will prove the value they add and this will slowly cause their business partners to ask for a data-savvy person to be involved in decision making, because it is not just about the data, it’s about the opinion, the insight and the point of view on it.
Ultimately, companies and leaders need to take firm action on the setup of their organisation, understanding that a hierarchical tweak simply won’t do the job. The person in charge of data should sit at C-level to ensure their involvement in every aspect of the company life, to guarantee neutrality and support to the ultimate decision maker, the CEO.
The pace at which data is generated today is massive, far too high for any manual process to keep pace. Companies need to have the courage to reinvent their processes around the opportunities offered by data-based automation and decision making, to let go of the practice of controlling every step of the user/customer experience and let algorithms do the job. Data-based decisions can, in fact, be automated on the basis of the desired goal and the people in the company should focus on managing the process that gives the desired output, and not on managing each step of the generation of that output. Machine learning has made this possible, and new frontiers of personalization are now common practice in several technology companies and allow them to design experiences around each individual customer they have. Not to mention predictive analytics and behavioural models, which are helping companies shape strategies every day more customized and fragmented, all of this backed up by sophisticated data visualisation tools that allow you to understand the data more easily.
This is the full meaning of customer-centricity and the benefits for managing a company in this way are huge. P&Ls can be broken down to the level of marketing and sales channels, to initiative level, and ultimately to the level of each customer, allowing unprecedented transparency and accountability for each investment that is made. The P&L today can evolve from a huge averaged collection of inefficiencies that, at high level, compensate each other, into the sum of highly optimized micro-P&Ls that, when combined, show a much better company. It is no longer the “consequence” of a fiscal year, it can become the “cause of it, a tool to drive decisions every single day.
If this sounds familiar it is because it is, the data revolution and the industrial revolution are based on the same principle: automation and scalability. The industrial revolution was about addressing the issues of mass production of goods in an automated and scalable way, via mechanical and physical processes and the full control over each step of the production process. The data revolution is about addressing the issue of mass communication and distribution, via computational and mathematical processes, with control limited to input and output variables and trust in algorithms and models.
It is a deep cultural change, in most cases it means letting go of consolidated (and sometimes very effective, in the old world) ways of working, but I view this as a necessary evolution that companies who want to thrive in this day and age should have the courage to make.