Data silos have always been bad for business. They limit employee performance and sow dysfunction among different teams by restricting access to information. As Big Data and AI become major forces in business, the negative effects of data silos will become even more severe. Distributors should integrate databases now and employ comprehensive AI to avoid the data silo trap.

Although AI and digital data silos are new phenomena, an ancient parable summarizes the problem surprisingly well. It goes like this: 

An Indian king collected the blind man in his village and presented each of them with a different part of an elephant — an ear, a trunk, a tusk, a tail — telling them “this is an elephant.” Later, the King gathered them all together and commanded, “Tell me, blind men, what is an elephant like.” Each man described an elephant in terms of what part they had been shown and soon they fell to fighting, saying “An elephant is like this, an elephant is not like that.” 

In the 2016 novel The Nix, Nathan Hill explains that what is often misunderstood in the parable is not that the blind men are all wrong, but that they are all correct. “The problem with the blind men and the elephant,” Hill writes, “wasn’t (that) they were blind, it’s that they stopped too quickly and so never knew there was a larger truth to grasp.”

Understanding a customer is much like understanding an elephant. Distributors are not singular actors, but systems with many parts — IT staff, inside sales reps, customer service reps and so on. A single account interacts with each of these parts separately, creating a constellation of data. Each individual only sees one data point though, so no individual is able to fully understand the whole customer.   

AI is very good at combing through mass quantities of data and seeing patterns that individual employees cannot. However, when all the data are separated in silos, humans and AI alike will be unable to see the bigger picture. 

Consider the following situations: 

• If a customer views several items on a website, but does not make a purchase, that doesn’t tell a distributor anything. Maybe the customer is planning a purchase soon.
• If a customer unsubscribes from an email list, that also doesn’t reveal much. Maybe that person is perfectly happy and only wants to de-clutter his or her mailbox. 
• Finally, if a buyer makes a specific complaint with a customer service rep, perhaps that person is only dissatisfied with one product.

Isolated incidents don’t reveal anything. Context is key. If the customer does all three of these things — stops making online purchases, unsubscribes from emails and files a complaint — then that customer is obviously dissatisfied and is likely preparing to find a new distributor. The account could be saved, but only if all this data were in one place and primed for AI analysis. 

Comprehensive AI data analysis doesn’t only reveal churn risk. It can also personalize customer interactions to grow sales and reveal how to minimize costs. However, unless distributors have a fully integrated database, and one comprehensive AI system analyzing it, they will never piece together what is happening. They’ll have all the data necessary to capture growth, but will be prevented from cashing in on it by bad infrastructure. 

Big Data and AI are extremely promising developments. Gartner estimates that AI-driven business value will reach $2.9 trillion in the next 3 years. However, the only way to get in on those massive profits is by truly leveraging Big Data and comprehensive AI. 

As of now, only 18% of companies have a strategy for  sourcing data in a way that will enable them to “take advantage of AI’s enormous potential.” A bunch of small data silos and limited AI won’t cut it. The only way to understand an elephant is by contextually fitting its disparate parts together.

Distributors must actively break down data silos and prevent future ones from emerging to cash in on the benefits of new technology. The best way to do this is to employ one comprehensive AI platform that offers an easy way to enter data, conduct powerful analysis and provides actionable suggestions.

You have all the puzzle pieces. Now it’s time to put them together.