Digital transformation, but for real

In 2022 you will see many organizations using advanced digital techniques. Some even see an opportunity to fully digitize a large part of their processes. But is this also digital transformation? Not necessarily.

How data should change

Suppose you want to insure something. According to Martin van Capelleveen, Enterprise Architect Digital Transformation, you will most likely be dealing with a jungle of business processes. “First of all, you contact an insurer. This assesses the value of the object to be insured and calculates the premium that you have to pay on the basis of a risk model. Do you then suffer damage? Then you report this to the same insurer. He will have the damage assessed and pay out an amount or not. Then you also have to have the damage repaired. There is a good chance that we will already be a few weeks further. For example, if you are an entrepreneur, you will have to deal with stagnating business operations.”

That can be done differently, says Van Capelleveen. “Suppose you start today as an insurance company. Then you are unlikely to start thinking about your organizational structure: which people should I hire, and which processes should I set up? No, your motto is: I do everything digitally. You don’t want to capture the world in processes, you only see objects and events. Everything starts with a digital print. Do you have to insure my house? Then you record attributes of that house that are relevant for an insurer. For example: is the neighborhood below or above NAP? Or is the house in a region that is prone to burglary? You then have to be able to monitor for events that lead to damage, and take action based on that.”

Platform

How does that work in practice? “Back to that example of me and my house,” says Van Capelleveen. “I am an object, so is my house and the same goes for my dormer window. And the event is that my dormer window has collapsed. What shall I do then? I record that damage, for example by means of a photo. And I’ll post that photo on your platform. Parties that are now taking action are on the same platform. This could be an appraiser, for example. He gets into his car, does the valuation, and records the results, also on that platform. And you guessed it: that data again comes under the eyes of a contractor and he also takes action. In this way, not only was my claim for compensation quickly assessed, but also my problem was quickly resolved. And that much cheaper than with the current model, which is based on processes.”

But that’s not all, he says. “Suppose one of your customers is an entrepreneur in greenhouse horticulture. It generates a lot of data on your platform. You gradually gain a better understanding of which events in that context lead to which damage and what the effect is on the business operations of that company. By making connections with external sources, you gain insight into power failures, storms and hail and potential risks of flooding. If you see that there is a power failure, you send a third party with an emergency generator to guarantee the continuity of the company. And while you are there, you might as well take care of the maintenance of the special light facilities within the greenhouse. After all, it can just fail, with all the consequences that entails. You can also use sensor data, which allows you to predict when critical installations are likely to fail. Gradually, together with your customers, you move from an insurer to a service provider that takes care of the retention of your company. So you add a lot of value.”

Stumbling blocks

What are the common stumbling blocks we often encounter in digital transformation? Data was hidden in various sources. Often also technically different systems with different data structures. The larger the company, the more likely that data is stored in many different databases, giving the organization a disproportionate and inaccurate understanding of their data. Data classification can help here to organize the data logically and bring it together again.

With people entering data manually, there is always a high chance of bad data. A human-dependent data collection process will always be the main cause of data quality issues. A typo, a contextual understanding of a name or location, a missed number, etc. are all minor cases that affect the quality of the data over time. Unfortunately, we often encounter many long-standing data environments that have never been cleaned for the errors present. People often do not even know what the data quality is of the data they possess.

A work of art: Achmea

Is this method reserved for young start-ups? “Absolutely not,” says Van Capelleveen. “Look at Achmea, a company that dates back to 1811. They are making this happen step by step. They also realize that you are more successful in an ecosystem of parties. This gives you access to a shared digital platform. A platform where data from various sources together offers you the insights on which you can expand your business models. A good example of this is BlueLabel.”