8.5 INSIGHTS

FROM 8.5 YEARS OF INSIGHTS & DATA

As Lao Tzu once said, “every step is on the path.”

Well, I am about to take that next step. I have spent 8.5 great years with Capgemini’s Insights & Data global business line, first leading its financial services sector, later as its CEO. Per the 1st?of January, I am embarking on a new endeavor, outside the company. I felt it would be nothing more than appropriate to dedicate my last post in my current role to the lessons I have learned throughout these years.

8.5 years, 8.5 insights.

Not something pretentious as a legacy, just a mixed bag of observations I made in the middle of the fastest growing, most exciting part of the IT industry - where data, analytics, and AI are powering organizational change. Here they are:

1.?A bit of insights, a lot of data…

Our global business line is called ‘Insights & Data’ for a good reason: it is to convey that achieving data mastery is definitely about getting a solid grip on the data landscape, but even so on activating the insights that are built on top of it, right in the middle of business operations. In the past years, I have seen a gradual shift within our client base to achieving the latter. But in general, it seems that many organizations still put too much focus on their?data?foundations and technologies, and underestimate how crucial it is for success to leverage actionable?insights?for business.

2.?…but the shift is far from easy

Fine, we need more insights then. And we get them from data, right? It all seems deceivingly simple. Identify the right data, gather it, store it, make it accessible – then analyze it and turn it into insights for the business. But there are clearly different ballgames involved here, with very different players, sometimes abiding to rules that seem to come from different galaxies. There are parallel technology and business dimensions to cover, data management and governance, data science, storytelling, strategy, transformation, architecture, culture. Becoming a data-powered enterprise requires a multi-disciplinary team effort.???

3.?No single architecture to rule them all

I have seen them come up rapidly, sometimes go down even faster. There are many architectural visions, approaches, and technologies to store and access data. Relational databases, (logical) data warehouses, data marts, data vaults, data lakes, data lake houses, graph databases, data mesh – all in the cloud, on-premises, sovereign, at the edge. In the end, turns out there is not one, supreme way that covers all needs. Real-life practice turns out to be hybrid, and multi-cloud. It’s thus a matter of pragmatic choices, depending on the use cases that are envisioned – without dogma and always keeping it dead simple.

4.?Data monetization – where is the money?

It seemed to be the highest achievement of them all: making money with data, as a first-class product – potentially becoming more valuable than the original products and services of the company. Well, not so fast. Where imagining and even prototyping data monetization feels seducingly simple, the actual practice proves to be a quite a bit more prosaic. Limitations by data privacy, security and ownership are among the passion killers. And there’s a lot of data to consider, most of it of little importance to the outside world. If you finally have your data products, contracts, pricing and curation still pose a challenge as well.

5.?Always that 80/20 rule

There’s always an 80/20 rule lesson to be learned somewhere. I found it applies to the data, analytics, and AI lifecycle. Producing the right insights, analytics, and algorithms becomes so much easier if some of the most crucial foundations have already been dealt with properly at beforehand. 80% of the hard work is already done when data availability, quality and privacy have been built in into the data platform. Not as an afterthought, but as a project enabler ‘by design’. This way, full attention can be rapidly paid to the 20% that delivers: powering the enterprise with data – at the heart of business.

6.?Newton was right

As we already predicted?a year ago: Isaac Newton got it right. Objects with more mass have more gravity. And the closer objects are to each other, the stronger their gravitational pull. I found it’s definitely the case with data as well. First of all, in terms of the major global and local cloud providers that store and handle more and more data. But also, in general: if you want high-value insights, you need to be closer to where the data is. And more data means exponentially more insights. Partnering and collaboration – inside and outside the company – should be geared towards this simple, yet powerful consequence of the law of gravity.

7.?Still Customer 360?

Sure, every company should put its customers first. What a terrific job data has done for understanding and engaging with customers better – thriving on marketing, sales, and customer analytics. But there is more data to leverage out there. And it’s not only in terms of operational and enterprise management data. Just look at the opportunities with data from devices and IoT, CO2, smart products, climate, and the environment. These data areas deserve way more attention. My pro tip: if you aim for data mastery in the company, doing good for society nowadays might do just as much magic as focusing on the customer.

8.?Data’s Hotel California

As I prepare to move to my new role - outside the company, in quite a different context - I realize I am forever in data’s ‘Hotel California’. Yes, you can check out any time you like - but you can never leave. Once you have fully embraced and appreciated the transformative power of data, you realize its potential is anywhere. I can’t help myself nowadays looking at everything through a data lens: what are the key data sources, what does the data try to tell me, are we getting the right insights? The enterprise thus becomes a vessel of data. Such a lovely place.

8.5?Data Mindfulness

Bear with me, as I bring this final insight. It’s based on some half-baked ideas, so I felt it was right to give it number ‘8.5’. Where do we stand towards data? Look at the increasing power of AI, its ability to create content and to oversee complex situations, adapt fluently and take automated action (always with humans in the loop, mind you). Could it help us address the challenges of our time – such as battling climate change, eradicating sickness and feeding the world – in a radically unique way that surpasses the mundanity of data, platforms, and insights? Shouldn’t we become much more mindful then, about our dealings with data?

Well, with these final thoughts we can call it a wrap. Time for change now.?To quote Lao Tzu one more time:

“if you do not change direction, you may end up where you are heading.”

I wish you a most exciting and satisfying 2023, wherever your path brings you.