Data to value stream – the consumer side.

A data team turns data into insight. But to to turn that insight into value, you need consumers that are able to take the insight and make changes in their processes.

There is an entire workforce of data people ready to turn data into insight. But how that insight is used determines whether the company stands to gain any value from the initiative.

For example: let’s say that through experiment you find out that 20 percent of your current acquisitions are coming from referrals. If you are currently doing nothing to optimise referral rate, it is feasible to assume that you could be doing 2-5x better with some effort. But if you don’t have a marketing team that can create targeted referral campaigns, the insight adds no value to the company bottom line.

In the same way, if you hire a data team but do not ensure that the rest of the company is able to do something with the data, your data initiative will not pay off.

Why can’t consumers turn insight into value?

  • Lack of priority (motive)
  • Lack of resources (ability)
  • Lack of understanding (means)
  • Lack of experience (in avoiding common mistakes)

Lack of priority

This one is easy to mitigate. Make sure that company priorities are communicated objectively through goals. If the goal of each team is clearly communicated not just to them but also to the other teams, they can work together towards the same priorities.

I’ve sometimes heard managers complain that their managers don’t give them clear goals because they want to keep things flexible.

Flexibility through confusion is not a good tactic, and what helps instead is ensuring that the goals are clear, and that there will be new goals/targets/OKRs if things change.

Lack of resources

This one is as before, a matter of priorities. If there is a good understanding of goals and the impact that different initiatives bring against those goals, then budgeting accordingly should not be a problem.

However, if a company lacks the transparency and previous data-driven decision-making experience, then resources may end up diverted in a non-data driven way, and initiatives that could pay off are not implemented in favour of maintaining the status quo.

Transparency and goals (objective measurement against objectives) are foundational to resource allocation.

Think about it this way:

Suppose you sell a product for a profit of 100. Suppose some channels have a CAC of 30 and some have a CAC of 200. In a perfect world you would cut some budget on the 200 channel and assign it to the 30 channel.

However, reassigning a budget from one channel to another can have massive impact on a department. Such, there will be many excuses why data will not be used.

To prevent such cases you need to make sure that the performance of each channel is transparent to everyone, and that the goal is also openly and objectively stated.

Lack of understanding

A lack of understanding of how to implement a process change or how impactful a change could be, will cause insight to be discarded.

This one is more difficult to solve because one must know they should learn, be willing to learn and also know what to learn.

The willingness to learn typically comes from willingness to work towards targets in a more efficient way that will allow reaching them.

However, a professional who has never worked with data, might not see how it would help them. This is why typically the initiative, if it has not already come from the employee, has to come from their management. A workshop on how to do data driven optimisations focusing on their processes is a good starting point.

If the knowledge and goals exist but change isn’t happening, then the issue likely stems in how easy it is to change a process. Assigning clear ownership and giving coaching is a good way to make it easier to decide for a change.

Lack of experience

This one commonly happens when working with data, as data usage is rather new in most fields.

To solve a lack of experience without replacing talent, the focus should be on expediting learning, removing technical challenges and allowing the employees to focus on experimenting. Bringing in outside experience to offer some best practices will also help in avoiding common pitfalls.


Creating a data driven organisation is no easy feat, but it has been done successfully before. The recipe is invariably the same: Start with data driven management, because the change is systemic.

A workshop on the topic to your senior management paves the way to creating this culture.

Save yourself the years it would take this conclusion to trickle from the tears of your frustrated data team. Get in touch for training.

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