What’s the most important thing in real estate? According to the old saying: location, location, location. But the sentiment can also be applied to enterprise master data.
As we’ve discussed in previous posts, the power of a particular data set often increases if you more closely examine the areas where it overlaps with other types of information. And the more areas we can effectively combine to assess these overlaps, potentially the more valuable your enterprise master data.
Location data is unique in this regard, as it isn’t particularly useful on its own; you need to combine it with some other type of information. But once you do, it can unlock valuable insight—from driving improved analytics, forecasting and business intelligence to informing larger strategic initiatives.
As a quick exercise, consider any type of information your company absorbs, produces or manages today—be it product, customer, supplier, or virtually any information about your owned physical assets—and ask what advantages you might gain by adding “where” to the equation.
Here are some examples of how location data brings value to other types of master data:
Physical Asset Data + Location Data
The data from your physical assets, from owned properties and buildings all the way down to individual machines and devices that require regular support and maintenance, is one of the best ways to gain value with location master data.
For instance, you might be a company that provides unique display spaces inside a number of different retailers. Each of those retailers might have their own requirements depending on store size, local taste preferences, or any number of other regional variations. Being able to create a data model for what type of displays you provide and then tying that to a location data model to detail where they are located, you could decrease the manual effort and human errors that are made during your monthly reporting processes.
Or perhaps you’re an energy company that needs to manage and assign routine maintenance to all your wells, as another simple example. The sky is nearly the limit, with applications that are typically only constrained by the type of business you conduct. There are applications here for property and lease management, employee management, construction project oversight, retail, manufacturing and many, many more.
Product Data + Location Data
Similar to physical assets, product data can also greatly benefit from the “where” dimension. If you offer products as a service, for instance, you likely have numerous ways you could apply location data to determine where your products are at any given moment and shift inventory to manage seasonal spikes in demand. You might manufacture a product that generates IoT data which triggers a maintenance call or inquiry to your customer if values fall out of a certain range. By combining information about your products and where they’re located, you can manage a subset of that IoT data and build workflows that automate the process of repair scheduling.
Or you could simply want to identify where your products are stored and drive analysis to make better distribution and supply chain decisions.
Customer Data + Location Data
Location data can enhance your quest to deliver customer-centric products, services and customized offerings. Think beyond just your customer’s address to gain an understanding of which stores they frequent in order to push the relevant sales and promotions. Today’s marketing landscape is filled with tools and apps that can offer richer detail using geo-location. So why not factor some of that data into your customer data management initiative to enhance your golden customer records?
From a B2B perspective, location data can enhance your understanding of where your vendors and suppliers are located in order to better manage your contracts and relationships.
Supplier Data + Location Data
Build a supplier hierarchy data model that displays the relationships between parent and child companies, identifies which are your active suppliers and then aligns all of that information against geographic locations in order to drive supply chain efficiency.
These are just some of the examples of how you can add value to your existing master data by including location as an additional dimension to your data management projects. It’s no longer enough for businesses today to think about their most valuable resource, data, as a static entity that exists on a server (or even worse, in a spreadsheet!) somewhere by its lonesome. The companies that will go beyond their competition are the ones willing to most creatively manage that resource in new ways that unlock value and drive innovation.