A large number of organizations are in the business of selling to businesses. They never sell to consumers, and, in fact, their business is more dependent on how well their customers are performing than almost any other factor. In this middleman role, whether as distributors or value-added providers, these organizations face unique data challenges.
One such middleman organization I worked with is a multibillion dollar building supply provider. In their business model, they provide many of the masonry, plumbing and electrical components to major home builders and developers. Faced with not only the normal challenges of running their business, they are also highly impacted by the challenges that their customers, the home builders, face.
Because of those downstream impacts, the building supply provider has a greater need for visibility into their customer's driving forces than the average organization. One IT leader at this distributor stated: "We need the data to manage two businesses. First, there is the data to run our processes, such as sales, distribution, supply, finance and human resources. Second, though, is visibility into what is driving the demand for the large home builders. This visibility and how it impacts our processes and decisions cannot be underestimated. I am torn between optimizing our internal data process and integrating from this universe of outside data. I know that optimizing both of these can make the difference."
Build an Inclusive Architecture
As data management professionals, we must play this juggling act every day. The best way to manage this balance is through a robust architecture plan. The architecture must take into account all data areas that will have relevance, including the downstream customer (and customer of the customer) data. Since there will continue to be large advances in data availability and syndication, the architecture must be not limited to what is available today.
In the case of this distributor, key economic indicators such as prime interest rates and new housing starts are early-warning indicators of how well their business demand may be in the coming months. This kind of data is available and needs to be part of the realm of the data architecture. Having a firm understanding of data areas, capture mechanisms and governance plans specifically for this kind of data in addition to other unforeseen outside data must be part of the scope.
Just as important is an architecture that allows for ease of data sharing with the customers, in this case, the builders. Insight into demand at the builder level will allow this organization to be more proactive in their supply and distribution approach. Even if the current builders do not share this level of data, it is critical to build the place markers in the architecture to include this data.
Even though the future must be taken into account with the architecture, the current data needs remain top priority. A holistic approach to internal data integration and movement as outlined in some of my previous columns is critical. The combination of traditional warehoused data and integration technologies, attention to master data and platform standards remain high priorities.
Building the Right What-If Models
Inclusive of the architecture are the what-if or predictive modeling capabilities. With most BI platforms having a planning capability, the challenge is less technical than business related.
What-if tools are very valuable, especially when fed with predictive attributes from your customers or syndicated data. For example, if we use some of the government indicators (housing starts, prime rate) along with some aggregate demand information from the builders (ratio of multifamily to single dwelling, top 10 geographic areas) as drivers for a predictive model, the business demand insight greatly increases beyond the internal indicators (sales activity, order backlog, etc.).
If engaged, business leaders will be quick to point out the predictive data drivers. As with other data-related endeavors, though, the data management professional must add a dose of data reality to what can be achieved now versus planning for the future.
Understand Two-Way Data Relationships
It is all well and good to expect the early warning indicators from the builders, but be prepared for their desire to get some data in return. Just as major retailers will give consumer product goods companies insight into their selling patterns in return for an integrated data supply chain, the same can be said for our situation. The builders may want insight into our supply chain information so they can feed their predictive models to ensure seamless production schedules. When designing the architecture, the data management professional must be prepared for this two-way data sharing and all the latency, security and quality issues associated with it.
When in the middle between manufacturers and your customers, there are data challenges that are best overcome with an all-inclusive architecture. Three of the biggest architecture drivers are: data for current internal business processes, external syndicated/government data and two-way customer-demand data. When combined appropriately, they can support even the most advanced functions, such as predictive modeling and what-if analysis.
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