With the rising prominence of AI (artificial intelligence), companies are considering how to become more data driven in their decision making. Various sources indicate that companies are increasingly interested in applying AI. Still, many are not yet moving forward with it.

As someone who has researched business success and failure patterns for over 25 years, and who worked in an early career position that today would be called a data scientist, I find that companies can get the most from their data if they not only see the potential benefit, but also recognize possible challenges. Remember that AI is a tool and data is a resource. They are not magic bullets. Just like other tools and resources, AI and data can be valuable if applied and used appropriately. Much like other tools and resources, however, AI and data do not work well if misused. And, an unfortunate, but very real, issue is that inadvertent problems with the data can easily lead to things going seriously wrong.

Nonetheless, data can play a valuable role. Based on my research into business success and failure patterns, I see evidence that, even years ago, companies were able to achieve greater success if they used data effectively. This was the case long before AI became well known, before we had the vast quantities of data now available, and before we had today’s sophisticated analysis tools. But, back then, as is still the case today, getting the best results depended upon using the data properly and understanding what the data can and cannot tell you.

Becoming successfully data driven does not mean blindly following the data. It means making sense out of the data and asking questions when necessary to help turn that data into meaningful information. It’s so important to grasp both the tremendous potential benefits of the data as well as the challenges and pitfalls that can occur. That’s why I have previously written about the importance of combining the human element with the data, and about why good data science requires more than technical skills alone. I wrote about those issues back when popular books about Big Data were saying that no human input was needed. Today, however, we are seeing a much more widely accepted view that human input is often essential to get the most out of the data and that machine only data analysis can entail biases.

As AI has gained popularity, we see more and more articles about it in the business press. The Wall Street Journal has published material about AI. And, more recently, (May 22, 2019) Forbes published its Issue 06 “How to Turn AI into a Core Competency”. This Forbes material, which consists of several articles about AI, is very good because it not only gives examples of how AI is being used, but it also takes a balanced view. Pointing out that there can be challenges with AI, the Forbes material not only highlights AI’s value, but also discusses AI’s tendency to get overhyped. It covers areas like the need for human input and the danger of biases. It stresses the importance of data quality and points out that data science is placing a greater emphasis on problem solving, not just on coding.

Based upon my many years of researching business success and failure patterns, and on my prior work experience with data analytics, I find that it is vital to take a balanced view with AI and with becoming data driven. Data can shed valuable insights. And, AI can help us get more of that from today’s voluminous data sources. But, it is still important to understand what the data means, to be sure it is of good quality, and to recognize that human input is often needed to insure that the data is meaningful.

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