Why you're failing at Data

It's not that obvious

Have a look at your LinkedIn feed. I can almost guarantee that, most of the time, your feed will be dominated by two things: 1) How to do Data, and 2) Why none of these work. In this endless cycle of ideation, excitement, disappointment and retrospective naval- gazing, perhaps we should ask a more fundamental question: "Why do Data projects fail?".

Have a look at your business right now. Most likely, there is money to spend, appetite for doing things better, at least some capable people, and plenty of technology to use. And if these things are not present, then there is at least some scope to improve them already.

And yet, despite your advocacy, expertise and best efforts nothing seems to budge. Your business continues to have sprawling, messy architectures, old- fashioned ways of working, limited data literacy and an inability to get things done. Why is Data so hard?

You will of course have many smart people around you telling you how to solve this problem. These solutions (I'm willing to bet money on this by the way) will include:

1. Literacy! If everyone is trained in Data, then a data culture will surely emerge!
2. Cloud! Access to better platforms will enable a suite of new, unimaginable solutions!
3. People! If you hire the right team they will be an example and driver of change!

Cool, sounds good. Now go and talk to any of the CDOs/ Data Leaders you know and find out whether any of that actually worked. I don't need to tell you what you'll hear next.

So, the reasons your Data ambitions aren't materialising aren't actually that obvious.

Some possible (and bad) explanations

One of the best books I have read in recent times is "Why Nations Fail: The Origins of Power, Prosperity, and Poverty" by Daron Acemoglu and James Robinson, whose central question is "Why are some nations rich and others poor, divided by wealth and poverty, health and sickness, food and famine?".

Dealing with such a broad problem statement and trying to conceptualise a general, but flexible framework to solve it over times spans of thousands of years is no mean task- the authors analysed civilisations including the Roman Empire, the Mayan city-states, medieval Venice, the Soviet Union, Colonial Latin America, England, Europe, the US, and Africa.

And yet that is exactly what these authors have attempted (and, in my view, largely succeed) to do. But before we explain their theory, let's review some common, but flawed, ideas:

• The Geography Thesis- in this view, certain countries simply lack the resources & geographic advantages necessary to thrive independently. Hence, these places are simply bound by fate to suffer poverty, failure and frustrating socio- economic realities
• The Culture Thesis- in this view, certain cultures lack the necessary values & motivations to drive economic & political reform. For example, cultures that lack the "Protestant Work Ethic" are doomed to fail due to a lack of enabling cultural values
• The Ignorance Thesis- in this view, countries fail because their leaders simply don't understand the right way forward. They dabble in poorly designed policies precisely because of this ignorance and so, without outside guidance, will be doomed to suffer

What Daron Acemoglu and James Robinson conclusively show is that these varied, but almost always fatalist, theories result in inconsistent conclusions that don't reflect the specifics on the ground, as well as failing to conceptualise an effective route to progress.

For example, if Geography is the explanation then why are European nations (with limited natural resources compared to Africa or Asia) so much wealthier than everyone else? And why has Singapore advanced into a modern economy when the rest of Southeast Asia hasn't?

If Culture is the explanation for developmental differences, why then did Venice, which led the world in prosperity & trade in the 12th century, become reduced to a museum in the 21st? How do we explain the chasm between the GDP of Qatar vs. Iraq (two countries with conservative Islamic cultures, plenty of oil, the same language and even shared ancestry)?

And finally, if it's Ignorance that holds countries back then why hasn't decades of economic research & aid helped many African nations escape cycles of political uncertainty, economic failure and social chaos? Furthermore, why have some countries like Seychelles, Mauritius & Botswana realised a GDP per Capita significantly higher than their African cousins?

What the authors instead propose is that it is human-made political & economic institutions that underlie economic success (or lack of it). That is, the institutions that we create are the ones that bind our destinies- and only through institutional changes can we escape failure.

For example, the Koreas are remarkably homogeneous in culture & language, yet the people of North Korea are among the poorest on earth while their cousins in South Korea are among the richest. Why is this? According to the authors, it is because South Korea forged a society that created entrepreneurial incentives, rewarded risk & innovation, and allowed everyone to participate in the economic opportunities that modernity brought. In comparison, the North purposefully stifles these to maintain a centralised power structure that is intentionally designed to extract wealth from the many for the benefit of the few.

What this means is that economic success is not an accident nor is it inevitable- it is spurred by accountability, responsiveness & incentives that drive its people to innovate.

Different institutions create dynamics that enable or prevent positive change. Countries are not destined to be poor- they are intentionally made & kept that way because of political institutions that don't enable, incentivise & reward the dynamics of prosperity.

Understanding why your Data projects fail

Many existing explanations for why Data projects fail include Budgets (i.e. Geography), Talent (i.e. Culture), and Expertise (i.e. Ignorance). And while all of these certainly matter, focusing on these has failed to overcome the persistent challenges that Data Leaders.

What the authors of Why Nations Fail propose is a two- fold model:

1. Centralised State Power enables effective policy setting & execution instead of the chaotic atrophy that overly (or unplanned) decentralised structures can lead to
2. Inclusive Institutions that enable the power vested in the aforementioned Centralised State to be distributed across many highly enfranchised individuals & interest groups

The 1st without the 2nd will lead to an authoritarian regime that fails to enfranchise a wide swathe of the population to undertake economically productive activities. The 2nd without the 1st will result in chaotic behaviours and atrophy arising from an inability to execute.

What this is means for Data Leaders is that (according to this model) their ability to change their organisations are rooted in two issues: 1) What is the level of centralised, authoritative decision- making structures that will properly define, cascade & execute?, and 2) To what degree are people engaged & enfranchised in this process, and incentivised to deliver?

In this model, issues like Budgets, Talent & Expertise become consequential- if the institutions that enable change are in place, these issues will eventually be solved.

Summary

While businesses are not exact parallels for states & nations, I believe that there is a fascinating lesson here in how we think about organisational change. While the specific of what you need to build & invest in will change, it is only by combining centralised, authoritative decision- making structures with widespread & popular enfranchisement that a organisation can both motivate Data transformation and ensure it is driven strategically.