Data is overwhelming us. Across the industry, we’re on a quest for information, constantly chasing data points and hustling like crazy to engage with the latest data solutions that will fill our stacks of servers to the brim with knowledge. But what if it turns out that all that data isn’t helping, but actually hindering us?
Consider a practical scenario in the retail space: You’re trying to decide whether to offer a discount or run an advertisement on a product in order to hit your sales quota. That simple decision requires consulting multiple data sources, all of which live in different data silos. If you don’t take the time to parse through the data, you’ll miss important signals that provide key insights. Do you have enough stock? Have you offered a recent discount on a similar product that increased sell-through? Is there a demand for discounts on this product? That’s a lot to think about. And for just one SKU.
I believe escaping our current Digital Dark Age is the great challenge of our day
Thanks to the amazing advances in data processing, we have access to more data today than ever – and that pool of information grows exponentially by the day. But all that knowledge can be staggering if we don’t have a means for using it. This wealth of information can become “dark data” – insights lost in the machine. I talk about this issue often, and with great passion, because I believe escaping our current Digital Dark Age is the great challenge of our day. But solving it also represents the next giant leap in human productivity.
Let’s return to the retail space, where transforming information into insights is mission critical. Not only are retailers responsible for tracking anywhere from hundreds to hundreds of thousands of ever-changing SKUs, but they are also constantly weighing decisions based on market, customer behavior, inventory, production and more. The platforms containing this data – as well as all those rich insights they can reveal to us – haven’t been designed to effectively speak to one another in real time and give us practical guidance. Information is not insight. Without understanding, accessibility to data has limited value.
Information is not insight. Without understanding, accessibility to data has limited value.
The challenges of the Digital Dark Age often extend beyond consumer considerations. Did you know that two employees on the same retail team could have competing – even diametrically opposed – incentives? Two employees, for example, hold a stake in what color fabrics are ordered for factory production and when it should be delivered. But the inventory associate may want to hold out a little longer to try to gain a clearer market signal, while the shipping associate is motivated by having the product delivered on time. How do they decide the path forward? Who makes that decision? Because these workers are evaluated differently, they have different success metrics. But at the end of the day, they should not be at odds with each other – both their goals are important to the company.
I believe that this business – and, frankly, every organization – needs a solution that can hybridize information across all data sources, providing a single, clear snapshot for all to see. Unfortunately, recent history has shown that the industry continues to invest in ever more data solutions, simply heaping more information onto the data pile, rather than finding a way to make sense of it. That, at its core, is the problem of the Digital Dark Age.
Still, we are witnessing examples of retailers who understand the challenge and are using AI and machine learning to begin solving these data problems. Humans’ processing limitations and linear thinking are inherent limitations as our data pools – both internal and external – continue to grow. By employing new solutions to integrate silos of information, stitch together disparate digital signals and translate them into quantitative insights, an organization can create a stronger unity of focus while also becoming more nimble.
The Digital Dark Age is no longer “Mission Impossible.” I view it as a puzzle we can solve. Information accessibility and data points are just stones on the path toward understanding. Knowledge, however, truly is power. To achieve that level of understanding requires a new approach in which data is automatically connected through integrations. This greatly reduces the complexity of connecting data for analysis. Artificial intelligence (AI) then churns through the collected data – running millions of decision scenarios, constantly, all aimed at predicting the future of the retail operation given different probabilities. This is what we call Decision Intelligence.
By creating and sharpening the tools to excavate insight from mountains of data, retailers, among others, now have the ability to mine untapped knowledge and uncover a more efficient and sustainable way of doing business.
Next: How to access all relevant data points and turn them into actionable insights that focus on impact.