Artificial intelligence and organizations are not always a great fit. While many organizations use artificial intelligence to answer specific questions and solve specific problems, they often overlook its potential as a tool for exploration and innovation — to look for patterns in data that they probably would not have noticed on their own. In these organizations, the focus is on supervised learning — training machines to recognize associations between inputs and labels or between independent variables and the dependent variable they influence. These organizations spend less time, if they spend any time at all, on unsupervised learning — feeding an artificial neural network large volumes of data to find out what the machine discovers in that data.

Observe and Question

With supervised learning, data scientists are primarily engaged in a form of programming, but instead of writing specific instructions in computer code, they develop algorithms that enable machines to learn how to perform specific tasks on their own — after a period of training and testing. Many data science teams today focus almost exclusively on toolkits and languages at the expense of data science methodology and governance.

Data science encompasses much more than merely training machines to perform specific tasks. To achieve the full potential of data science, organizations should place the emphasis on science and apply the scientific method to their data:

1. Observe
2. Question
3. Research
4. Hypothesize
5. Experiment
6. Test
7. Draw conclusions
8. Report

Note that the first step in the scientific method is to observe. This step is often overlooked by data science teams. They start using the data to drive their supervised machine learning projects before they fully understand that data.

A better approach is exploratory data analysis (EDA) — an approach to analyzing data sets that involves summarizing their main characteristics, typically through data visualizations. The purpose of EDA is to find out what the data reveals before conducting any formal modeling or testing or hypothesis about the data.

Unsupervised learning is an excellent tool for conducting EDA, because it can analyze volumes of data far beyond the capabilities of what humans can analyze, it looks at the data objectively, and it provides a unique perspective on that data often revealing insights that data science team members would never have thought to look for.

Note that the second step in the scientific method is to question. Unfortunately, many organizations disregard this step, usually because they have a deeply ingrained control culture — an environment in which leadership makes decisions and employees implement those decisions. Such organizations would be wise to change from a control culture to a culture of curiosity — one in which personnel on all levels of the organization ask interesting questions and challenge long-held beliefs.

Nurturing a Culture of Curiosity

People are naturally curious, but in some organizations, employees are discouraged from asking questions or challenging long-held beliefs. In organizations such as these, changing the culture is the first and most challenging step toward taking an exploratory approach to artificial intelligence, but it is a crucial first step. After all, without compelling questions, your organization will not be able to reap the benefits of the business insights and innovations necessary to remain competitive.

In one of my previous posts Asking Data Science Questions, I present a couple ways to encourage personnel to start asking interesting questions:

• Conduct question meetings. The sole purpose of the question meeting is to ask interesting and relevant questions and call attention to problems. Do not try to answer questions or solve problems during the meeting. Ban all cell phones and other electronic devices, and have your research lead conduct the meeting.
• Place a question board in a well-trafficked area. A question board invites people to post questions and problems and provides inspiration for additional questions. In large organizations, consider posting multiple question boards, so everyone can participate.

Another way to encourage curiosity is to reward personnel for asking interesting questions and, more importantly, avoid discouraging them from doing so. Simply providing public recognition to an employee who asked a question that led to a valuable insight is often enough to encourage that employee and others to keep asking great questions.

The takeaway here is that you should avoid the temptation to look at artificial intelligence as just another project. You don’t want your data science teams merely producing reports on customer engagement, for example. You want them to also look for patterns in data that might point the way to innovative new ideas or to problems you weren’t aware of and would never think to look for.