The Data Supply Chain - Its Definition and How to Use It

This is the transcript of a podcast hosted by InetSoft on the topic of "The Data Supply Chain: Its Definition and How to Use It." The speaker is Jessica Little, Marketing Manager at InetSoft.

We have all heard of supply chains in the context of manufacturing, for example, but what exactly is a data supply chain? The data supply chain is a lifecycle for data that basically propagates and procures data on behalf of the corporation.

So if you look at data as being bigger than the platform, when we truly talk about enterprise data, we are talking beyond the data warehouse. We are talking about enterprise data as an asset on behalf of the corporation.

What the supply chain does is it basically looks at data, the inputs and outputs of data across the company, across systems, across platforms, across organizations and really manages it as an asset.

Common Vocabulary Around Data

We apply the same principles of a normal manufacturing supply chain to data, and the advantage of that is that executives speak that language. As we discuss supply chains, we are really establishing a common vocabulary around data that executives can understand. The advantage of that is that executives will be more likely to fund the data, more likely to support enterprise data, more likely to pay for the business intelligence tools around data that we need in order to manage it. So it's actually a really effective expectations management tool.

In fact, if we can talk about the data lifecycle from a raw goods standpoint through to manufactured products at the end, then you have got a tangible product at the end of the supply chain. Executives start to get that more. The advantage of the supply chain analogy is really also about the quantification of the value of data as it goes through the supply chain.

We are all really used to the concept of the production line, and finished foods emerging at the end of the production line are valuable and quantifiable and saleable by the company. And so executives learn about the data supply chain, and they go, “Ah, you know what, I can probably apply the tools and efficiencies to the data supply chain that I can’t to my regular manufacturing supply chain. I can speak the same language and maybe even get the same value from my data.

Why should people in the data warehousing community embrace the concept of a data supply chain? People are being asked increasingly by executives to justify the data warehouse purchase, to rejustify the data warehouse both in terms of financials, what's the ROI of our data warehouse, and in terms of business value, what have we gotten from it.

Any good executive will ask the question, “What have we gotten from our data warehouse lately, and how much has it cost us?” So the data supply chain enables this vocabulary around the value of data, what the data warehouse has delivered in terms of the finished goods, and what the values of those goods are. It's often easier to imagine data as just another product, more goods you can sell.

Nimble Business Decision Making

And those goods in translation are things like nimble business decision making. So the information, that’s the output of the data warehouse, is also that tangible asset that’s coming out of this data supply chain. And executives really want to know at the end of the day, what have I paid, what have I gotten for it, and how has it helped the company strategically. And I think the same principles of a manufacturing supply chain can be applied to those questions as well.

Every organization has its own set of data integration challenges. If everyone is so busy putting out day-to-day fires, how will they know it's time to consider the data supply chain? The data warehouse is bigger than the sum of its parts. We see it a portfolio of business capabilities deployed over time.

So the right time to really look at a data supply chain is when you are looking at the data asset that you have, and you realize the opportunity for its reuse. So as you start adding applications to your business intelligence portfolio, the opportunity for reusing the data in the data warehouse is higher than it's ever been. So that, in turn, results in cost savings and economies of scale.

So the right time to really consider a data supply chain is when you are looking at the reusability of the data that you have already procured and deployed through your data supply chain because that’s what's going to give you the bang for the buck. That’s what's going to save the company money, and that’s what's going to generate higher productivity among the knowledge workers who are using the information for real daily decision making.

It's usually best to wait until you are able to reuse your data, Actually, in a lot of ways, the data supply chain is bigger than the data warehouse. We actually see the data supply chain being used not as much as a justification for the data warehouse as it is as a justification for formal data management. This is the umbrella over the data warehouse, and the data warehouse is one of potentially many platforms along the data supply chain.

Data Goes to Die in Data Warehouses

A lot of us in the data warehousing community actually have seen the data warehouses for a long time as this platform where the data goes to die. That’s not exactly the description of a data warehouse we want to hear. In fact, data is really much more dynamic than that.

There are entry points and exit points all the time, and the velocity of the data coming in and out of our systems is much higher. So I think the point of the data supply chain is less about the individual platform and more about the formal institutionalized data management practices which transcend a data warehouse or any system and really are helping the enterprise manage its asset.

Speaking of the business community, are companies actually buying into all this? Well, I think you always have to prove the business value of individual applications. Where the data supply chain conversation really helps is when you are asking for capital expenditure. So there is the infrastructure component of the data warehouse, and then there is the business value of the data warehouse.

There is a link between monies that need to initially be laid out and the overall business value. There is always some sort of initial capital investment that has to be made usually before you start deploying all those quick wins by way of the analytics.

Seamlessly Weave Data Throughout Business Processes

And that’s when the data supply chain model is helpful because you can compare that to the way that company is doing business now and all of these organizational and system silos, and then say, “If we seamlessly weave data throughout our business processes, we are going to achieve huge efficiencies and cost savings just in the reuse alone.” So it's really about the initial funding of the data warehouse platform and the data management functions that pervade it when you are talking about the supply chain.