Every puzzle needs the right pieces in the right places. Image by Midjourney prompt by the author.

The data-quality-opportunity

Data is influencing an increasing amount of our decisions. How do we make sure we have the data we need and how do we get the data we’re missing?

Our decisions are limited to the data, insights and experience available to us (1). At the same time competitive advantage is rooted in our ability to see what nobody else can (2)(3).

But how do we know what data we need to make better decisions helping us better serve our customers, deliver desired outcomes, increase operational efficiency, gain competitive advantage etc.?

How do you know which pieces are needed and missing? Image by Midjourney, prompt by the author.

The Gap

Today the most common approach is to use whatever data we already have or can easily acquire. Data is plentiful, in fact we already have too much of it, so why do we need more (according to Seagate 43% (6) of the data captured through operations remain largely unused )? But: “Only 15% of companies get meaningful value from their data investments” (7). So volume of data does not equal value of data. How can we change our perspective from volume to value?

Data serves decisions, not the other way around (8)

We don’t need data, we use data to achieve what we need. And the simplest way of seeing it is that data helps us make better decisions. So how can we look at the data through the lens of our decisions instead of through the data itself.

Luckily there is a very simple readily available solution.

The solution

Judea Pearl and Dana Mackenzie points out that:

“The world is not made up of dry facts alone” (5)

The difference between people and data is huge. If we ask a human or our team of human experts:

What leads to ‘x’ (‘x’ being whatever we are trying to achieve)?

They will be able to create a map showing what ‘forces’ (a ‘force’ can be anything having an influence on something else(9)) lead to what we are trying to achieve.

Because humans understand relationships and causality (5).

Humans see the world through releationships and “forces of influence” understanding that more or less of A leads to more or less of B.

If you ask a computer it doesn’t understand neither relationships nor causality. It will only see what is correlated limited to the data it has available.

A computer can calculate correlation and probability based on the data available to it.

Humans and computers are great partners. But computers don’t know what data they are missing. Humans do .. if you ask them!

The solution is therefore to sit down with the team and ask: what leads to more or less of ‘x’? And then through a few rounds of asking this question map out what the ecosystem surrounding what we are trying to achieve looks like taking advantage of the deep expertise of the team.

If you want to explore your first system mapping exercise this is a great place to begin: https://everythingnewisdangerous.medium.com/how-to-make-a-system-map-in-the-simplest-possible-way-a8cce6b7acae

(As an illustrative model. I asked Perplexity what influences a physicians decision making and made the model below).

‘what influences a physician’s decision making based on input from Perplexity.’

A model can be made through interviews or a workshop. The input is then qualified and filtered, making the model as representative and simple / clear as possible.

The team now prioritizes where they want to have influence by simply discussing and marking the model.

Clearly highlighting (purple) where we would like to have influence in the ecosystem helps inform the teams strategy.

At the same time we also map out where we have data and what that data can tell us in relation to where it is on the map.

The model now shows where the organization wants to have influence (purple), but also what data is has (green). The map becomes a very clear visual showing the relevance of the data we have and a great tool to discuss and find the data that is missing to make better decisions related to what we want to achieve.

Next map out where we need data (yellow) in order to make better decisions related to our strategy (purple).

The team maps out which data they need to make the best possible decisions. Then create a roadmap for getting the data and starts collaborating with the right colleagues and expertise in order to aquire the necessary data.

Now we have our data-gap and we need to turn the gap into an opportunity.

Working with our resident or team of data experts, ethnographers (or if you’re in a pinch your agent or AI/LLM-model) we can figure out which data is needed for the areas where we decided to have influence, what type of data would be valuable and a roadmap for getting it.

Simple.

To sum it up:

1. Use your human expertise to create a map of your ecosystem*
2. Identify the most important areas of influence
3. Identify the data we do have
4. Identify the data we need
5. Prioritize and create a roadmap to get it

*if we want to the computer can be a great resource to qualify the map or find forces that the team is not aware of.

Sources and further reading:

(1). Curiosity eats creativity for breakfast, https://uxdesign.cc/curiosity-eats-creativity-for-breakfast-03e96dc74996

(2). Customer as a competitive advantage, https://uxdesign.cc/customer-as-competitive-advantage-19a6ede62852

(3). It’s not what you look at that matters, but what you see — Walden, https://everythingnewisdangerous.medium.com/its-not-what-you-look-at-that-matters-it-s-what-you-see-walden-b530288014c8

(4). Jon Steel, Truth, lies and advertising, https://www.goodreads.com/book/show/397499.Truth_Lies_and_Advertising

(5). Judea Pearl and Dana Mackenzie, the book of why, https://en.wikipedia.org/wiki/The_Book_of_Why

(6). Seagate, Rethink Data Report, https://www.seagate.com/files/www-content/our-story/rethink-data/files/Rethink_Data_Report_2020.pdf

(7). Kavin Hanegan, The Hidden Data Crisis of 2025 (And Why Most Companies Aren’t Ready), https://www.turningdataintowisdom.com/the-hidden-data-crisis-of-2025-and-why-most-companies-arent-ready/?t

(8). What is your distance to the customer? https://www.linkedin.com/pulse/what-your-distance-to-the-customer-helge-tenn%C3%B8-6vbgf/

(9). The Omidyar Group & Kumu, Systems Practice workbook, https://docs.kumu.io/disciplines/systems-practice