The idea of data as a business asset has been central to much of the thinking and writing about big data that’s emerged over the last few years. But what value does big data really hold for business? Data in and of itself is really of no value at all. Its value comes in being part of a process that collects, stores, analyzes and transforms into insights and action that drive business improvement. In that sense, data is just one component in a process, much like raw material is just one component in a traditional supply chain. The challenge we face today is actually linking and efficiently feeding that supply chain.
I’m not the first to posit the notion of a data supply chain. Business consultants at companies like Accenture and Cognizant have written about the concept, and Venture Capitalist and Former General Manager of IBM Watson, Manoj Saxena, talks about it during this SXSW panel presented by Umbel, the company that I run. What interests me most is how we will solve the customer data supply chain problem for marketers.
There’s been a lot of focus and innovation on the front and back ends of the data supply chain. Storage, which was once considered a prohibiting factor has largely been solved thanks to solutions such as Amazon AWS. On the other end, there are hundreds if not thousands of companies creating algorithms and cognitive computing solutions that are focused on BI and AI leveraging big data to drive marketing. What is still missing for a complete marketing solution is the link between the stored data and the applications that automate and take action. I believe this is something CMOs and CIOs need and want to control so they are free to try new vendors without the risk of losing the direct connection to all the customer data they work so hard to generate.
What makes the data supply chain a supply chain?
To get at what I mean about the missing link, I’ll start with a basic explanation of the supply chain analogy. If you look at various descriptions of supply chains, from the cross-industry-standard SCOR model to the SupplyChainOpz blog’s “beginner’s guide”explanation, the definition boils down to this: A system of processes in which something enters the system, undergoes a transformation, and emerges on the other side as something of value to be delivered to people who can use it.
In a traditional supply chain, it’s a raw material coming into the system, being transformed by a manufacturing process and emerging as a product a company can take to market. In a data supply chain, it’s data coming into the system, being transformed by analytics and emerging as a set of useful insights a company can apply toward improvement. If you’re a marketer, the data coming in is going to be information about your customer from myriad sources — websites, social networks, mobile apps, CRM and CMS systems, etc. — and the value coming out is going to be in the decisions or actions that are taken based on automated algorithms, human insights or both.
A broken chain doesn’t work
What the supply chain analogy tells us is that you can do everything in your power to collect all the data available to your business, but it will do you no good if you’re not able to unify, access and feed that data into a system that will enable action. Conversely, you can have the best AI or BI tools in the world available to you, but if you don’t have a rich source of data coming in to which you apply them, they’re useless.
Data, data everywhere
I think one of the greatest challenges for marketers comes at the point in the process of unifying, accessing and delivering the data through the supply chain. For most companies, customer data is scattered across silos of information that can make it difficult to access, much less integrate into a cohesive whole. And the more data there is, and the more sources it comes from, the more of a challenge bringing it together is going to be. But you have to be able to do just that if you want to provide a rich data set that analytics can transform into actionable insights. This is done with a technology platform designed to unify data across multiple systems that can send customer data both in and out. I think we will see more value being realized around this part of the chain in the near future as CMOs demand more efficient, complete data supply chains that put them in control of their customer data.
The idea of the data supply chain reminds us constantly that the means to extract value from data already exists — but it’s only as useful as the data we feed it.
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