Why is fact based decision making difficult for businesses?

Businesses have to make decisions based on what they think will happen in the future, but a lot of the time they don't have enough information to make an accurate prediction. That's why they have to rely on assumptions and gut feelings, which can often end up being wrong in hindsight.

In this episode of Qcast, we'll understand the reasons that make fact-based decision-making difficult for businesses.

Sahil Brij Malhotra: Hi, I'm Sahil Brij Malhotra, director of business development at Qualetics Data Machines. Welcome to Qcast! Where we bring in our leaders, experts, and guests to talk about AI challenges and opportunities. Today we are going to interact with Mike Fowler, chief commercial officer at Qualetics. We're going to talk to him about why fact-based decision-making is difficult for businesses.

Welcome, Mike!

Mike, making decisions based on facts and data is such a fundamentally important business principle. Why then has it become so challenging for businesses to use this as a daily practice?

Mike Fowler: Of course business executives want their organizations to make decisions based on facts and data. There are a lot of organic reasons why that's difficult. The presumption is there are not enough relevant facts and data available to support decision making but in my experience, the problem is actually the opposite. There are too many facts and data available in an organization, so much so that it can be overwhelming and people don't know how to get access to it all.

I'll give you an example. I was with a Fortune 500 company and we were launching a new digital information product over the course of about 10 months when we created this, we needed to get ready to scale it and put it into production. We stepped back to look at what that entailed and we found that in creating this product, we had actually created 52 different new data sources and databases. All of them had value, all were needed to either create this product or to inform other parts of the business or other products downstream.

The fact is nobody predicted this amount of data would be derived from this one product, but it's just an example of how over the course of a broad team working to accomplish their objectives, you organically start generating new connections, new collections of valuable data, and this is why it's a real challenge for organizations to be able to leverage it all, to do effective decision making using facts and data.

Sahil Brij Malhotra: Right. It's really interesting Mike, that you brought up this point, 52 different databases so we can safely assume that there would be so many more functional silos operating in that organization. Wherein each of the functions kind of operate independently of the other and there is very little if any collaboration which took place between those functions. So Mike that is an example that kind of presents itself as a perfect use case for analytics, so what can be done to change that dynamic?

Mike Fowler: Well, first it's important to understand how an organization responds to this challenge typically when they're trying to get their arms around their data challenge. They’ll ask the IT team to bring more form to this process, and what tends to happen is that the rest of the organization is asked to more clearly define their request for data and funnel those requests through the IT team. The IT team tends to be pretty overwhelmed just managing their normal project activity so these ad-hoc requests are difficult for them to reallocate resources and be very responsive with. And so, the rest of the organization begins to feel like there's this data autocracy sort of being formed and what needs to happen is you need to reverse that dynamic and create more of a data democracy around the organization., And most people or most companies see the way to approach this is by using business intelligence tools or BI tools.

If we look at what that means the organization begins to buy licenses for each person that they hope to enable with these BI tools. All of those people that got the license have to start with step one – how do I link to all of the data that I want to connect to do analysis and right away they're back to going back to the IT organization. They go to the IT organization to say this is the data I need, where do I find it, and how do I connect to it. So right away, you're back in that challenging environment of trying to enable this decision-making to occur. If we continue forward once the connections are made, the user has to use the BI tools, and analytics features. And those are they have the BI tools, have done a really good job of creating really good analytics tools, but they can be kind of technical.

The company tends to find at this stage that all of the people that they've licensed BI tools for aren't necessarily using it. They're a little overwhelmed with the richness of the analytics feature set and how technical they need to become, so at that point, you're starting to create a handful of BI power users. Now, this is better than just the IT team providing access to the data because the rest of the organization starts to go to these BI-power users. But the BI power users run into the next challenge, and that is once they do provide some analyzed data results to look at they need to be able to socialize that information if they're trying to create real interactive analyzed output.

Everybody that they share that with has to also be licensed with these BI tools. Otherwise, they're limited to just printing out or creating pdf files for people to look at, it's fairly one-dimensional and it doesn't really create a lot of rich collaboration around the analyzed result set. So, those are some of the common ways companies will respond to it and some of the limitations associated with it.

Sahil Brij Malhotra:

Right, and again it's interesting that you mentioned BI tools and power users. Because I myself was one of the power users for a region using a particular BI tool in my previous organization. And most often than not always I felt as if I were a data autocrat, not a democrat given the fact that while I had the perfect training for using that particular tool and there were a lot of insights and actionable information which was available, I was not in full capacity to kind of share all the relevant insights with all the users that I wanted to share. I did not myself have the bandwidth to kind of train each and every user the way that I was trained and so if I would not say that but it's not about the inclination of the users to be trained but more often than not the users also are limited by their ability to train themselves at a level where they can utilize the insights and information provided by such tools in order to enable their own decision making. Thanks for the insight Mike, it is really important that organizations start looking at data and analytics from a strategic viewpoint rather than a non-strategic one.

Thank you Mike for your insightful thoughts on the impact of AI on business growth. We just heard from Mike Fowler, chief commercial officer at Qualetics Data machines. We will be back with more thoughts from Mike and our other team members soon.