Responsiveness, the key capability for value creation with data
A check-list for value creation with data
Hello, I am Louis-David Benyayer and you are reading Datanomics and strategy, my weekly newsletter on business strategy in a datanomics era.
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For this edition, I‘ll discuss one piece of news and propose a check-list for value creation with data and analytics. I’ll conclude with two interesting webinars to attend and as always with some personal updates. I hope you’ll envoy.
In the news
Why it matters:
We already know data is a new asset class and the conversation is intense regarding the way to account for it in balance sheets and to capture its value. This example shows a new emerging way for capturing the value of a data asset: use it as collateral for a loan. Is it the sign of a new class of securities? What if this trend expands? Does it mean the bank accesses to the data in case the airline fails to pay back its loan? New open questions to regulators when it comes to personal data.
Responsiveness, the key capability for value creation with data
The bank, the data scientist and the branch manager
To introduce the check-list, let me share a personal experience I had with one client. This leading bank had just recruited a chief data officer and his task was to build solutions with data and analytics to solve the bank’s business problem. Nothing really original, but challenging though given the maturity of the company regarding analytics.
The CDO does the job perfectly: he starts by interviewing the managers in charge of the business and operations to identify the business problems and then he prioritizes them according to the probability of solving them with data.
“Churn” makes it to the top of the list: a lot of clients are going to the competition, which is becoming a major problem for that bank. Stimulated by this interesting challenge (how to reduce churn) the team works hard to source past data, identify clients churning, running models to identify the variables to explain why they churn, … And then apply the model to the current list of clients and predict which would churn.
They end up with a list of clients with a high probability of churn in the next quarter.
Pretty cool, right? So the team goes to the branches to meet the managers and proudly reveal that secret list of clients that will churn in the next three months. They had 10 meetings with 10 branches and they all developed the same sequence. 1) the team explains the problem 2) they explain how they worked 3) they announce they have the list of the clients that will churn and then …
the branch managers interrupts and says “I also have the list, here it is”.
The team was smiling first but stopped when they saw the list was 80% identical to theirs. And this occurred during each of the 10 meetings. How could this be possible? The branch manager had a very clear answer: “we know they will churn because they all are having their second child and are moving to a new house, they need us to finance the acquisition and the problem is that our competition does a better job than us. The problem is not to know they will churn, our problem is to have the good services to keep them with us”.
A check-list for value creation
This story is interesting because it captures the necessary elements to create value with data and analytics:
a clear problem related to competitive advantage: here, the churn
an explicit value creation KPI to act on: here, the lifetime value of clients
data resources and capabilities: data of the clients, talented data scientists, IT infrastructure, …
operational capabilities to respond. This was the missing part of the example I described previously. Having the prediction didn’t help at all because the company couldn’t respond to the signal.
The first three elements are critical, you can’t do anything without them.
But they are not sufficient and the fourth one is less present when it comes to identifying strategies and opportunities of value creation with data.
Using this framework helps to capture the promises of of value creation with data and analytics.
A key question you could ask yourself before making any investment decision or launching any project is: “how will we respond to the signal?”.
But let’s be clear, you shouldn’t interpret this as a Go/NoGo decision matrix. It’s a guide for better setting the scope of the project and envisioning the processes or operations you need to change to have an impact on performance, beyond data and analytics processes.
In the next editions, I’ll be more specific on these 4 components.
Class started yesterday at ESCP for the MSc in big data in business analytics (I am scientific co-director of that program) and we kicked the program off with my course “Data-Driven business strategy”.
We are particularly lucky to have L’Oréal, Etalab, Bouygues Construction and La Redoute as partners to that course. They challenged the students with business questions to solve with data. The students have now one month to explore, prototype and test. Final presentations will take place on October 20th.
If you’re interested to see the results and participate in the presentations, hit reply!
The OpenData Institute organises regularly webinars and online training, you should definitely have a look at them if you want to understand better the open data opportunity for business. I selected two (free), they take place TOMORROW:
Making music with weather data. The Conditional Orchestra is a web application that converts current weather data into music — a personal project that took two years to realise. In this talk Richard reveals the creative inspiration behind the idea, the technical challenges that kept him up at night and what he learned from the experience.
How to make better use of data-enabled services in the public sector. To help local authority leaders and service designers make better use of data the ODI has developed the Data and Public Services Toolkit. The toolkit is designed to help build a business case for integrating data into services and to overcome the barriers faced along the way.
Thank you for reading, I hope it’s been useful to you. If so, please consider sharing the newsletter with people you feel could be interested.
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Have a good week
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