Data-driven mindset is a key of success. Words YES! But actions are better. Why and how to have a data-driven mindset to lead to data-driven actions?
Let’s deep dive into the fundamentals of a data-driven organization.
Data-driven is a must for all size of organizations
Successful tech startups have to be data-driven. They only can survive in the market by measuring everything they do, everything their customers do, and everything else.
Envious of these disruptors, traditional corporates have been wondering for a while whether they could apply the same principles or not, whether they could reach the same level of value creation or not.
It created many digital transformation initiatives.
But can traditional corporates become as obsessed with measuring as companies that started based on a data-driven foundation such as Amazon, Google, Alibaba and Tencent to name a few?
Organizations perpetuate the behaviour that made them successful.
When companies built themselves on the shoulders of charismatic, daredevil, intuitive leaders, they won’t easily let data take the place of these leaders’ intuition and boldness, at the risk of being proven wrong by data.
Being data-driven means to submit oneself to the power of data, the power of truth, the power of the market.
These leaders must change their point of view, their mindset, their believes. The company leadership team must install a data-driven culture. Otherwise, nothing will change.
Leaders must show infinity humility to be questioned by data and to acknowledge that the world has changed since they learned their trade and be in the risky position of a learner.
Reasons for being data-driven
From the organization perspective
Benefits for the firm:
- Decision: decision-makers which are mainly managers are able to make more informed decisions for the sake of the firm.
- Real-time: date gives the pulse of the organization at any time
- In-depth: data about the firm is a multi-dimensional view (at the micro and macro levels)
- Engagement: data help to create engagement among the teams with an open heart. Anybody can generate significant insight. The stimulation of the collective united around the same goal where data can tell quickly if something goes wrong.
- Talent retention: being data-driven improve the retention of employees because they see that their actions have a direct impact on the business
From an employee perspective, what are the advantages?
Benefits for the employees
- Efficiency: you will be more efficient. As you build up decisionmaking processed based on data, you gain in productivity.
- Accuracy: you will take, on average, less poor decisions. Nobody likes to make wrong decisions.
- Stress: the uncertainty of deciding without hard evidence can be stressful.
- Employability: if the rest of your industry is operating at a higher level than you, you lose attractiveness on the market.
- Growth: becoming data-driven is not easy. It will force you to develop new intellectual muscles, learn new skills, and, most importantly, change your mindset.
- Career: data-driven is the future of all functions: marketing, management, HR, information system, operations, finance. Whichever department you work in, your career will benefit from all of the above.
All perspectives should be considered and put forward equally, especially optimize the chance to inspire the willingness to change at all levels.
Before starting, assess the amount and the quality of data you have
About to take the curve of the data-driven organization? spend time asses what you have in hands.
Corporate often say that they don’t have enough data. Another perepctive is: they already have too much data that they are not leveraging. Time to assess the data you have before thinking of collecting more that won’t use properly.
The 80/20 rule can be applied. Only 20% of the data can result in 80% of the value when leveraging at their full potential.
Here are the signs that you have too much data and 20% of the most valuable data are underused.
- Poor data quality: this is the number one reason for reducing the amount of information collected. Quality above quantity is a common mantra but rarely followed.
- Incomplete data: in a survey, or a registration, too many questions lead to less completion rate. When a piece of information is not mandatory in a form or a screen, why include it at all?
- Not used: if no report, no decision-making process, no segmentation, nobody is using a piece of information, discard it.
- Silos: it is easier to consolidate simple dataset then complex ones. We generate more insights from simple data models gathered from all the systems than trying to create comprehensive reconciliations between some systems.
- Understanding: nobody understands the entire data model anymore. It takes a meeting with seven experts to explain a single end to end process.
Less is more: removing data and processes can generate enormous value and open space for the modernization of information systems.
One question to access the data: how actionable would this data be? Unactionable won’t be used.
And if you don’t use the data, don’t collect! Unused data is of poor quality, reduces the engagement, and creates clutter.
Process of implementation
It’s important to implement a strict process in order to avoid the drawback of an uncontrolled implementation.
You should start with strong data foundations and process mapping, that allows, in time, to build more and more advanced models.
This helps also to:
- understand that they will get more value by sharing their data (counter-intuitive)
- invest in good quality integration projects (cost) to avoid fragmentation
- build a data-driven strategy (time)
The first step to be data-driven: elevating new skills with hiring
The first steps corporates take in their data-driven transformation are often to
- create a data science team
- launch a data audit, a data warehouse or a data lake project,
- install analytic tools on top
However, this is only the first step. This is the foundations of the house.
After the first step, the budget of data-driven transformation might be gone but the most important is what is left to do.
what is the point of setting up the conditions to be data-driven if it doesn’t help to add value to the organization?
In that scenario, what is left is to deliver value and start the transformation because an over skilled isolated data team cannot achieve much.
Creating a dedicated data team will not change the culture of the company.
On the opposite, it sends the signal that data is somebody’s else problem when it should be everybody’s concern. Having a team producing more analysis and more insights does not do any good if the decision-making process is not taking this new data into account.
The answer is zero. You don’t need any data scientist to become data-driven. You have to raise awareness of the risk of relying on intuition to make decisions and the competitive advantage of backing every decision with hard evidence. First, work with the data you have.
Another reason (from a ROI perspective) that you don’t need data scientist at the transformation initiation phase is: the data scientists rarity and thus their daily rate.
Later, you may need help from a data scientist to get into more advanced analytics and create new insights to make more informed decisions.
The data-driven culture is the difficult mix of multiple factors in order to integrate it sustainably in the organization.
Hiring data scientist can be an imporvement at some point of the transformation when:
- you have enough qualitative data
- you need to perform high added-value tasks
But if you don’t have enough data, to begin with, their impact would be very limited.
Time to start infuse the analytical mindset in employees’ actions by training them to be data-driven.
One of the key driver to integrate the data in the core of an organisation: top management
Every element of a corporate culture trickles down from the management. Leading by example have a better chance of nudging employees to new behaviour and gradually shift their mindset. Establishing a data-driven culture is no exception.
I wouldn’t beat on a robust culture of data where the management continues to base decision making on their expertise, don’t question the information they see and don’t dig for insights themselves. I understand the uneasiness of changing the way one runs a department, division, or company.
A transformation towards a data-driven culture must come from an explicit and didactic change of behaviour of the top management. With a data-driven CEO at the helm, there is be no tolerance for any other kind of leadership then a data-driven one.
What does a data-driven CEO do? How to recognize one? How to hire, train, and coach a data-driven CEO?
There are plenty of models: Jeff Bezos, Sataya Nadella, Mark Zuckerberg, Tim Cook. I would love to attend one of these CEO’s staff meetings and one-on-ones with their managers. I would especially pay attention to the questions they ask before making a decision, and their reaction to surprising insights.
Data ownership
In a data-driven culture, employees demonstrate a strong sense of data ownership. You need to know your data like the back of their hands, the sources of information, the manipulations, the business rules. The data must speak to you.
Your manager must not be able to take you off-guard in a meeting by asking you: “why”?
You must be able to explain why one of their key indicators is what it is, with reason, and hopefully, more data.
The only reason to blame a data owner is if they don’t have an in-depth knowledge of her data. As for the data themselves, they are what they are. They have to be representative of reality. Too many corporates have a leadership culture of killing the messenger, confusing the data owner with the data. When you kill the messenger of the bad news, nobody wants to present the bad news, but only the good news. That leads to manipulation of data to make it look good, distancing it from the underlying reality.
A data-driven culture aspires to be as close as possible to reality. Avoid behaviours that can directly or indirectly drive you further away from reality.
Should all decisions be data-driven?
When asked outside of context, the answer is yes.
When asked in context, you may get different answers. There may be voices to consider other, non-quantifiable aspects, to rely on experience and intuition. It is a dangerous path:
- It is a way to give up on data-driven decision without trying hard enough to support a decision with information. It is rarely easy to do so.
- While a rational decision-making process can be analyzed and improved in time, a mix data+intuition process is the worst of both worlds.
There is one situation where one should not look for more data: if after careful analysis, you have two equally valid options. When there is no clear winner between two alternatives, looking for more data can be counter-productive. The best option in that scenario is a coin toss:
- If there were an obvious option, the first set of data would demonstrate that.
- More data most likely will not make much difference and only leads to analysis paralysis.
- After choosing one of the options, there will be no comparison with the “road not taken.”
- After choosing one option, you will adjust the course based on how the execution unfolds.
A data-driven business has enough trust in its data and analysis to make and support a 50/50 decision.
Be careful that the created data doesn’t escape to their creators during the collection
The best-seller Moneyball quotes Bill James writing: “When the numbers acquire the significance of language, they acquire the power to do all of the things which language can do: to become fiction and drama and poetry.”
A simpler version is that data must make sense.
When leverage data with simple models, it is easy to rationalize and keep it under control. But trouble begins when the models are too sophisticated that nobody understands anymore what is happening. There is an invisible threshold, when models are blindly optimized for efficiency, without a sense of purpose.
In 2013, Andrew McFee published an article in Harvard Business Review titled: “Big Data’s Biggest Challenge? Convincing People NOT to Trust Their Judgment”.
Often end-users of big data and machine learning don’t see the logic in the output and don’t follow the recommendation. That means there is missing information to make sense.
So McFee’s prediction that “as the amount of data goes up, the importance of human judgment should go down” is both scary and wrong.
Technologies are tools to augment human capabilities, not replace them. Using data, analytics, and machine learning must help us make sense of our environment, and enhance our decision making, not replace it.
A drawback of the data-driven approach: limitation in predicting the future
We live in unpredictable times. Just look back fifteen days ago. Remember the predictions you heard from experts. Fortunately for them, experts are seldom paid on the accuracy of their predictions. The ones you see on major media are just paid by how credible, or incredible, their predictions sound at that time. But everybody else is judged on results.
A prediction is usually a support for a decision. If it isn’t, it won’t have much consequence. We try to predict the future, to anticipate and position ourselves towards what we believe will happen. But, it seldom works. In times of uncertainty, it is even more of a folly.
Furthermore, predictions take time. Accumulate data, run predictive models, assess the accuracy, list options, run alternate scenarios, puts you at risk of delaying your decision making until your original data is not relevant.
A more productive approach is to make conditional decisions. That entails preparing several possible choices and the action plans associated with it and identified the trigger conditions of each of the scenarios. As situations unfold, we continuously collect information and evaluate the conditions to trigger the pre-assessed decision.
This leads to rapid actions, tied to the most recent data.