Data-centric thinking in business and IT management is the required change, which enables your AI initiative to make a real difference and true impact. Data is the oil that makes the chain of artificial intelligence work. That is why data management and also master data management is so crucial. An intelligent data hub can drive data-driven business forward, but more than that it is about people and strategy. We offer you three basic rules.
If there is one buzzword in digitalisation in recent years, it is artificial intelligence. The dream to let computers and machines learn so that they can make some decisions autonomously or deliver better output appeals to many companies to increase the efficiency of their processes.
However, to make this dream a reality, there are a number of conditions that need to be met. The most important is to ensure that your data management is up to scratch. As is often the case, you can only get something good out of it if you put something good in.
Your basic data are therefore crucial for a system to be able to work with them properly.
The lack of proper data management often results in chaos. More and more applications communicate with each other, but because of different data sources that are not attuned to each other, it seems as if you are feeding an octopus over which you have no control. And that, of course, is just what you don’t want.
However, three basic rules will ensure that this does not happen in your company. They are the foundations of good data management. With successful data-driven business as a result.
1. Making the roles and responsibilities clear
The importance of data has increased enormously over the last ten years. It has ensured that many people in companies have this as a core task. Think of data scientists.
Unfortunately, they are rarely involved in the core development of new applications in which data plays an important role.
This is because they are often part of the IT or business intelligence department. You can involve them in new developments by having such profiles active throughout the company. That means you also have data scientists in sales & marketing, and in finance as well. That way they can talk to each other, enrich each department with their insights and spread their common purpose throughout the company.
They will make sure that you think of a common backlog, that you build a data-share, and that you work together from those central elements. APIs and data tools will then ensure that every application has the right data.
2. Get a mutual understanding of the challenges (between IT and business)
What is your ultimate goal? Is that a powerful tool that works flawlessly? A smooth operational handling thanks to tools? These are important aspects, but they are not the ultimate goal. That is a happy end consumer.
Anyone who puts this objective first thinks from a different perspective. And realizes that you have to work together and understand each other to achieve that goal.
It is then important to make an overview of all the systems and needs and to link them. Solita likes to work with ‘traffic lights’. When listing, you also give each system a colour. Red means the data are not available for other tools, yellow means the data are in different systems, while green means data that are available in real time and scalable.
In this way, you ensure that it becomes clear where the proper flow of data is lacking and what stands in the way of the proper functioning of artificial intelligence.
3. Put data at the centre of both company strategy and enterprise architecture
Whoever takes the previous step almost automatically places data more centrally in the company strategy and enterprise architecture. This means starting from master data management. Customer data, contact data, contracts, data about the company employees, … they are all located in an intelligent data hub – Semarchy, for example. This ensures that they can easily be shared – via an API, for example – between the various applications.
Not an unimportant detail: this attitude also requires a different approach from procurement. They are used to placing the responsibility with a single vendor, whereas this approach needs that it is shared between many different parties and vendors.
It is probably not the easiest way. But that is the only way in which artificial intelligence is successful. Making such a big transformation in corporate-wide thinking: placing the data in the center in business and IT management and development, is not easy. But only after that, your artificial intelligence initiative can reap benefits end-to-end and whole company-wide. And processes toward customers are transparent and easy to understand.
Want to know more about data management and its importance before starting with AI? Watch our webinar Three Data Management Principles for Enabling AI