Geo-Intelligence: The Convergence of GIS and Business Intelligence
Location analytics or geo-intelligence is transforming the way business intelligence supports organizations. Discover the seven-step approach towards getting the most out of location analytics.
Government, finance, retail, health and human services, natural resources, public safety, transportation and utilities have all leveraged geo-intelligence to manage data and obtain valuable insights. Geo-intelligence is a strategic combination of the application of geographic information systems (GIS) and business intelligence (BI).
Geo-intelligence helps organizations get the most out of their data and information systems.
GIS is often siloed in GIS departments, and that continues because BI professionals typically analyze business data for patterns and trends using traditional tools such as tables, charts and graphs. They are unaccustomed to using maps as an analytical tool. GIS is often already in the toolbox of their business but is pigeon-holed as a complex enterprise technology requiring a significant investment, which is ironic because when data is shown on a map, patterns and trends are more easily revealed than when using charts, tables or spreadsheets. If GIS is not leveraged, the dimension of diagnostic, predictive analysis and the resulting insights are missed.
That information is crucial. To improve, grow or even develop a sustainable business plan, a business must fully understand its context – not just the names of competitors but also the science of where it is. Using the reliable, fixed framework of location (office locales, customer addresses, sales territories, marketing areas, facilities and so on), a dive into the analytics and data-based insights reveals hidden realities.
But you can’t buy those insights off the rack. Analytics is a bespoke process: it’s about discovery, continuity and thoughtful engagement. Let’s walk through the seven steps towards getting the most out of location analytics.
The first step is to create targeted spatial questions. What do you need to know? Are you worried about pending city development or changing demographics? Spatial information is vital even to businesses not bound to a building. A telecommunications business, for instance, where infrastructure for its customers is already in place, still needs to know about customer location. Telecommunications businesses want customers to buy family packages for multiple phones, TVs and computers, so they need to know who is living where and who will live in new developments. A number isn’t enough. They must ask questions like who buys which product, who can they cross-sell to and where can they find similar customers in their existing markets.
The second step is to explore the new data. Is it fulsome enough to answer those questions? When data is lifted off a spreadsheet, put onto a map and made visual, often holes in the data are exposed, and essential information for making suitable strategic changes is missing. When you see that it’s not complete or won’t deliver the deep analysis you need, you can adjust it.
With the release of Insights for ArcGIS in 2017, Esri blended a range of GIS and business intelligence capabilities into one simple analytics application. This unique data analytics workbench allows you to explore spatial and non-spatial data in one view, answer questions you didn't know to ask by providing a richer understanding of your data.
Canadian banks discovered they needed a geographic profile around their branches. To expand their business, they needed to deepen their relationship with customers. They wanted people to invest with them and take out mortgages. When they learned that most people resist doing that online and prefer to talk to a financial advisor about those transactions, ideally someone nearby, the banks realized they needed to better understand their branches’ neighbourhoods through psychographs.
Once they had the geographic profile, they were ready for the third step: model and analyze the data. In this step, problems are broken down into bitesize chunks. With basic demographic information (average age, income and size of local residences), the banks put questions against that information to determine how to service the community. Which branches needed more advisors than tellers? Insights for ArcGIS offers access to advanced analytics such as regression modelling, predictive modelling and Z-score in a very easy and simplistic workflow.
The seven steps to successful spatial analysis
This takes us to step four. You have the data, you know the questions and you have analyzed the data, now you can interpret the results.
Probably more than any other industry, the retail sector knows the impact of location on their business. Even though this industry is always collecting data, sometimes that data is untested and unexamined. And in the fashion industry, which sells dreams as much as it does trousers, sometimes perception outshines facts.
The marketing department of a men’s clothing store claimed that their customers earn at least 100K annually. In fact, the data revealed that their average shopper earns much closer to 60-70K. Without checking their vision against the analytics, the retailer was building their business on false information. To successfully sell a dream, you must know your reality. In Insights for ArcGIS, the creation of charts and graphics in the form of cards facilitates dynamic interaction and cross-card analysis.
As with all good processes, the next step(five) is to ‘repeat as needed’. Sometimes, this can be just about keeping an open mind and exploring being counter-intuitive. The menswear marketing people weren’t deliberately misleading their retail bosses; they were merely listening to aspirational information rather than verified data.
Understanding and using analytics is an iterative process, and the first plan is not always the right one. Also, in many cases analytics or analysis doesn’t get communicated clearly to the executive bench–there’s a loss on translation in taking the technical and delivering it into something understandable.
So how can you close the loop? Let’s look at step six. Heads up – this is the cool part of the process that most analytics don’t do. These predictive analytics can make the pass/fail difference for a business or a city. The information gathered through analysis workflow charts and graphics can be shared in the form of cards across the executive bench for easy visualization.
Insights for ArcGIS allows users to analyze datasets to produce actionable intelligence. The image above demonstrates an in-depth analysis of the Canadian employment scenario and its visualization in different formats.
Often the results of location analytics must be communicated to people who are very busy, maybe can’t read spreadsheets and are unfamiliar or uncomfortable with data. And often these people need to make policies, set new directions and look for key policy indicators at a click. Story maps and dashboards are simple and effective ways to show people what they need to make informed decisions.
The city may have an influx of immigrants from China, so which bank branch will need at least two Mandarin speakers on staff? Should a loan be given to the first wine bar in that neighbourhood because it’s ahead of the hipster curve, or is it destined to fail? If the analytics reveals a pattern of sloppy workmanship or unprofessional behaviour by a developer or a contractor, an insurer saves headaches and money by walking away from that proposed new building. Once action is decided upon, that’s step seven.
Geo-intelligence helps to increase a company or a municipality’s effectiveness. It’s not a one-off reveal with a curtain being pulled back. Like the planning it is designed to inform, it’s an ongoing process that requires regular updates, checks and reviews to make sure it answers evolving questions with accurate and full information so that people can design appropriate strategies confidently.