There are 2 kind of companies:
1. The ones aiming to be data-driven
2. The ones being data-driven
There isn’t a unique correct path to become a data-driven company.
The problem is not the data, the problem is to have data aligned with the company objectives on what data is needed and fundamental to grow.
The rest is just old-minded bullshit like “let’s track everything and then we make decisions”. That, just does not work.
It is like when you ask to a new fan of Metallica “What album of Metallica you like most?” And the answer is “all of them”. Most probably the kid just listened “Nothing else matters”.
Companies can not just saying let’s track everything and then make we decide. It ends, most of the times, in a pile of tracked and saved data that most of the people are unable to understand.
When it comes to data, more does not mean better.
Data evolves and it should be taken as a constant work in progress. Most of the companies nowadays aim to have from the beginning of a product, the best tracking without considering that it might change.
Most of the companies are not prepared to adopt radical changes. And to become a data-driven you should be ready to make them.
What happens when a company decides to become a data-driven company?
A normal behaviour is to rush in trying to attract data specialists, to make science before knowing the real problem. What is the problem? We need Data and know how to handle it. But what data do we need? Most of the time, we never stop to think about our Data Strategy.
The problem of not identifying the problem is devastating most of the companies out there.
Alright, you need a strategy first. You need to make sure all data resources are positioned in such a way that they can be managed and understood efficiently.
Having a data strategy ensures that data is managed and used as asset not as another sort-of-tool where specialists deliver information to other departments on a request system.
By establishing common practices and processes to manage and share data across the company, a data strategy ensures that the goals and objectives are
There are couple of fixes to that behaviour that are not hard to understand.
Make someone accountable for the Data Strategy. One of the most important responsibilities will be company-wide management and the use of data as a strategic asset.
This means, working together with every single department or business unit to design a common way to manage data, but more important to make sure that the culture evolves to a data-driven way of thinking and decision making.
The mission and vision of the company should trace the path for creating the data strategy. With a solid link between data with company objectives you are making sure the data is a part of the company’s DNA.
As the company’s goals has become clear, the Head of Data, needs to create a business-driven data and analytics strategy and develop a data-driven culture (!!!).
This means modifying every single corner of a company that must be modified. From C-Level’s decisions to Innovation Teams’.
However, even the best strategy can fail if the business culture is not willing to change. If the culture is not data-driven you will not make it even when your data strategy is golden.
When the previous steps are completed is time to start attracting the specialists you need to set your strategy in action.
You have a strategy, you have a data team. But remember this needs to have a company-wide impact. In this article from Irina Peregud are explained with good examples some points that I agree with.
To be more data-driven you should assure that your company has:
To get better executives on board
Executives must be willing to make radical changes towards a data excellence. If not, well, they must will. We always know of executives pulling in their own direction only because gut feelings plagued with wrong assumptions. That can be devastating for a business.
A data-driven company enables all members of the organisation to have instant access to needed information in order to make data-driven decisions in a matter of seconds, not days.
A company’s success depends on its employees’ data literacy: the ability to read, analyze, and argument with data. This is the basis of a common understanding. It can be challenging to unify all the different interpretations of the same data but it is the only way to make everyone in the company get to a common and shared knowledge.
Automation of data management structure
It is not possible to improve the data-driven processes if the data management workloads are not automated and optimised. It can help companies execute transactions, make faster decisions, rethink strategies, measure Objectives…
It requires a deep transformation in the company’s mindset and culture. A data-driven culture is the key that will define how data is interpreted across the whole organisation, how is its impact on the company’s strategy and how you democratise the data.
Your aim is to have everyone on this. You should think big or go home. You might set a learning curve from the first of the on boarding meetings until the new mate gets to a certain level of understanding.
There are different ways to set this. You can even define different learning curves with different levels of difficulties depending on the impact of the new mates with the data strategy.
This difference defines all companies like the first I mentioned today.
If you have a good strategy aligned with company’s objectives, good executives, and the rest of things mentioned before and you are still not making any decisions based on data, if you are still tied to old procedures, it is time to identify what is the problem.
It could happen that the decision-makers are not aligned with the company’s strategy (data’s). Therefore, maintaining and evolving a Data Strategy is a daily task that needs to be reviewed, scored and modified.
So, you better create cycles to measure your strategy.
So far we talked about data-driven companies and their challenges. But not all is about data.
Sometimes you have to make decisions based on gut feelings and see if it works or not. When it comes to empathise with your customers there is another dimension for the product: feelings. You surely can not grow empathy coming from numbers from behaviours.
A proper data strategy will help you to demonstrate with numbers that your feelings are correct or not. But data should not replace empathy.
Empathy is a capacity to understand or feel what others are experiencing. A data strategy will help you to drive a company to a better place. Data is just an asset to validate your assumptions about the customers.
Actually this pattern can be applied to any company wide decision: OKRs, Data, etc.
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