Many organizations struggle with the question, "Where do I start?" when working more with data & AI. It feels overwhelming. Here are four ways to get started with data & AI.
Four ways to use data & AI
Curious how you can best start with data & AI (artificial intelligence)? Davenport describes four possible ways of using data, ranging from obtaining information to optimization:
To be able to predict, you first need to know what will happen and why. That is why we recommend that you start with the first step and then work towards optimization. This also allows employees to get used to a more data-driven way of working.
We will go through the four ways using easy-to-remember examples. At the end of this blog, you will have a good idea of the different ways to use data. You can then apply that knowledge to your organization.
Understanding the operation
The first step in terms of data is understanding what is happening.
Suppose you didn't have a speedometer. You might be able to estimate your speed by listening to the sound of the wind, engine, and tires, but it is less accurate. Yet this is what many entrepreneurs do in their business.
Data is often available, but not yet translated into information. By transforming data into actionable information - for example with dashboards - you are able to take better decisions.
Know the problemsThe second step is knowing why something is happening. You may have had a car breakdown before. If you would like to prevent that from happening again, you need to know why it broke down.
We also often see this need in organizations. "Why are my margins lacking?". If you know why something is happening, you can act on it. Or at least respond quickly.
The third step is to know what is going to happen. We have had a fuel gauge in our car for many years. Much more convenient is the on-board computer. It indicates how far you can still drive. It assists you in taking a decision if you need to refuel now or if you can make it home. From this phase you will also work with AI - artificial intelligence - and algorithms.
The last step is to influence what will happen. That is the most challenging step. An example is Tesla's route planner. On longer journeys, the route is determined so that you can recharge at a Tesla charging station along your route. So you already know where you are going to charge at the start of your trip.
In organizations, examples are recommendations on websites. Netflix has an extensive list of titles, but it knows what I find interesting or fun based on my viewing habits. As a result, they determine to a large extent what I will watch.
And your organization?Do you want to continue with data in your organization? Then I hope you have a stepping stone to take the next steps. If you need help with this, we are happy to talk to you. We offer various services from Data Coaching to temporary deployment of our consultants to complete takeover of data & AI projects.
More about BRAINSTAX Contact us