Blog_Modern-Data-Solutions-for-the-Supply-Chain
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The Pandemic was a wake-up call that the supply chain needs systems and support that keep it resilient, responsive, and adaptive. Traditional data management approaches just won’t cut it, as they rely on physically replicating data from multiple systems into a consolidated repository, a process that is time-consuming, error-prone, and complex. The supply chain relies on up-to-date information from numerous sources, and it requires “military” levels of availability, to avoid shortages or shipping delays. Fortunately, logical data warehousing points a way forward.

Not Your Usual Data Warehouse

In this post, to begin an exploration of logical data warehousing for the supply chain, I’m taking a look at the Gartner® document entitled “Adapting the Logical Data Warehouse for the Supply Chain” (Gartner ID #G00760575).

The Gartner Data and Analytics Infrastructure Model (DAIM)

In this report, Gartner references the Gartner Data and Analytics Infrastructure Model (DAIM). As Gartner explains, “Our Data and Analytics Infrastructure Model classifies use cases to provide a foundation for infrastructure decisions.”

As Gartner says in “Adapting the Logical Data Warehouse for the Supply Chain,” “Structured/known data has established business value. Finance and supplier information would be examples of this,” whereas “Unstructured/unknown data has not yet established its business value or purpose, if any. It is data for which we do not have an already defined structure. New, digitally captured data sources, such as video or audio, would be examples.”

“Known questions,” says Gartner, “are those that are already understood and asked, defining the data’s business value. These would include regular predefined reporting used within the supply chain,” whereas “Unknown questions are those that are still to be explored or are still unanswered. They could be as simple as questions you did not think to prepare and optimize for, or as complex as new predictive models drawing on the skills of a data science team.”

Use Cases, Infrastructures, and Personas

Next, Gartner presents four major use cases/infrastructures and positions them on the DAIM: Operational intelligence, data warehousing, data lakes, and data science.

Where, you might ask, is the logical data warehouse? The logical data warehouse is a giant circle surrounding all four use cases and the entire DAIM. This suggests, to me, that a logical data warehouse embraces and includes the functionality of the four. I say this because logical data warehouses can include existing data warehouses, on-premises and cloud databases, etc., and then extend all of these resources with logical connections to additional data sources and analytics engines.

Gartner overlays a structure of personas over the DAIM: casual users, business analysts, data engineers, and data scientists. As Gartner explains it, “Supply chain strategy leaders must address the specific needs of different user skill levels that will interact with the data.”

Get Logical

The way I see it, when building a logical data warehouse, the key word is “logical.” That is, a logical data warehouse is not a physical appliance but a layer of functionality, and this would be good to keep in mind when applying your logical data warehouse design to the individual needs of your organization.

Gartner, Adapting the Logical Data Warehouse for the Supply Chain, 3 October 2023. GARTNER is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

Saptarshi Sengupta
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