April 11, 2019
Businesses spend a lot of their time trying to get the RIGHT answers from their data.Digital transformation and the capabilities of large data collection have promised them actionable insights that will make them more profitable, more competitive and in the land of buzzwords, more future proof.
If you don't know what questions to ask, you have to go back to the business problem you are solving. How are you currently solving it and does it require any data? If yes, you then need to find out if a data-driven approach can enhance your existing solution. This can be achieved with machine learning models but they are only as valuable as the questions you are asking your data.
Questions are more important than answers for several reasons:
- An answer is meaningless unless you understand or know the question.
- You can only unlock an answer if you think of the question and as a result the value of an answer is tied amongst other things to the quality of your questions.
A question will often lead to many more which you wouldn’t even have thought of had you not asked the first one.
For these reasons, Mind Foundry has designed its software to facilitate your questions and help you unlock more meaningful answers from your data. However, sometimes it might be hard to find the original question and which is why we are sharing some starting points.
Why is it hard to ask questions? Often it is because we do not know what can be answered or what can be achieved with machine learning.
Machine learning algorithms identify relationships between input features and target variables. In the process, they answer the question: is there a relationship in my data? Is there structure? As a domain expert you might already be aware or have intuitions of some relationships, but have not been able to prove or quantify them. Machine learning can help.
You can also try understanding why you are collecting that data in the first place. Why is it valuable for you? What information are you looking for? Machine learning will help you find information which is lost in your data.
Go back to the questions you already asking and answering, even if you aren’t using data, and ask yourself whether there really is no available data? Or can you source some inputs which might help enrich your answers? Ask your peers what questions they are asking and which data they are using.
Finally, if you are still struggling to find the right questions for your data, you can read the following case studies relevant to your industry, or book a question exploration workshop with one our facilitators.