About the Author

Victor Harrison is founder and principal at Tele-Media Consulting, a services-oriented consultancy focused on assisting companies in the converging Telecommunications, Media, Entertainment and Consumer Electronics industries, with customer-focused product and service strategies. Prior to this, Victor was responsible for Communications industry business development activities for IBM’s business consulting, strategic outsourcing and managed services groups and worked with cable companies, telecom service providers, Hollywood studios, content developers and distributors as well as hardware, software and network equipment providers. He can be reached at vih@comcast.net.

Businesses are at various levels of maturity relative to investments in business intelligence data and systems. For the most part, Business Intelligence (BI) as an IT and data investment has been the domain of Fortune 500 companies. Advances in cloud computing and SaaS/managed services bring BI to small and mid-sized companies and offer compelling solutions to business users of large organizations that struggle with getting the information they need and want from their data and IT. In particular, what is increasingly becoming the norm is small and mid-sized companies that have big company data issues. (For an example, see sidebar on Stubb’s Bar-B-Que.)

Location intelligence (LI) is the bridge between business intelligence and market intelligence, the bridge between the database analyst and the business user, and the bridge between the integration and correlation of various enterprise data systems such as CRM (Customer Relations Management), ERP (Enterprise Resource Planning), and BI. Location data has been the missing link in systems that allow companies to optimize and leverage their investments in data and to recognize the promises made by vendors that data analytics offer, in fact, a competitive advantage. BI is an analytical system that usually relies on data from transactional operational systems such as CRM and ERP. Figure 1 illustrates the conversion required from transactional systems to analytical systems.

Location intelligence is more than just the ability to plot data on a map, although visualization is a big component. LI is the next evolution of business intelligence, because:

• It provides BI with a value proposition beyond large companies and makes BI available to mid-market companies;
• It offers a cost-effective solution to complex data analytics;
• It extends the existing BI infrastructure and environment of companies that have already invested billions of dollars in these systems by making BI solutions scalable across the enterprise and down the food chain; and
• It brings the data closer to the user and makes it more actionable.

We spoke with four senior LI company executives from the field to gain their perspective on this subject: Tom Link, Owner/CTO, SpatialKey; Jim Pollock, President of Vertical Markets, aWhere; Jean-Sebastien Turcotte, Executive Vice President/CTO, KOREM, Inc.; and Luc Vaillancourt, CEO, Spatialytics. Their comments are included in this article. These companies provide consulting, strategic and integration services, as well as next-generation LI tools, technologies and solutions with a focus on keeping LI simple for the customer.

LI and BI:The Differences, Advantages, and Options

All of those interviewed unanimously agreed that business data are already spatial because all businesses are place-based entities, and that LI is a subset of BI. Databases such as Oracle Spatial, Teradata 13, and Neteeza Spatial have now all made the storage and consolidation of location information across business data easily accessible. The main difference between LI and BI from a user perspective is the difference between the consumers and producers of data. Tom Link of SpatialKey explained, “the trend in BI has been to focus on the production of the data—the data warehouse, the database analyst, the cube, which often results in rigid reports that the business user has to figure out how to apply to day-to-day management. LI, on the other hand, is about the trend in visualization, usability, interactivity, and shareability of the information. The focus of LI is really on the person that has to consume and use the data to make decisions.”

Many people confuse the map with location intelligence. The map is definitely in many cases a better way of visualizing data that is already in a BI system. See Figure 2. for the pattern recognition available from displaying tabular data on a map, which is unrivaled when geographic distribution of the data is in fact the insight. However, there is much more to LI than just plotting data on a map. Figure 3 illustrates the data sources, analytics and technical platform required to deliver location intelligence.

It’s All About the Correlation

The power of location intelligence is in the ability to correlate data down to, for instance, the location of a store, a customer, a medical clinic, etc., in relation to location influences such as weather, culture, socio-economics, or competition. “The real location intelligence is making use of great things that come out of GIS (geographic information systems) and bringing them into a BI workflow and being able to ask very sophisticated questions, such as, ‘Where are my stores that are underperforming relative to competitors’ stores?’ as opposed to the simple sales question, ‘Where are my stores performing or underperforming?’” said Tom Link of SpatialKey.

Jim Pollock adds, “Often, we discover hard-to-visualize similarities through mathematical spatial analysis. Conversely, we often see visual patterns in a map that are hard to do mathematically. Maps and numbers are great complements, not either/ors. Mapping is part of this, but just one more visual input. It helps to visualize patterns that emerge or do not emerge. If no pattern emerges, that could be a message, as well.”

What Are the Options?

Traditional BI analytics are limited because they generally don’t consider location. According to Jean-Sebastien Turcotte of KOREM, “The geographic dimension is often marginalized because traditional IT rarely assesses the value of geo at the beginning of the project.”

Companies that wish to own their own system, therefore, have two options when it comes to LI, according to Luc Vaillancourt. “For new projects, they can develop a fully integrated GeoBI system that incorporates the geographic/location dimension from the beginning, or for existing legacy systems they can retrofit the system with a geospatial visualization component, and LI acts as a bridge to these existing data systems.”

A third option is becoming increasingly important to small and mid-size companies with big data issues and small IT budgets, and for executives in big companies with big IT budgets who are frustrated with the quality of the information they can extract from their systems. The third option is a managed service that is offered by companies like aWhere and SpatialKey. With a managed service, a company can take advantage of the spatial/location analytics of the provider’s location intelligence platform, and the company doesn’t have to worry about the most important and expensive component of a location intelligence system, the data—the external third party data, including the mapping data.

According to Turcotte, “Businesses always underestimate the costs associated with the data, which are typically 35-45% of the cost of an LI project. Also, 30-35% of the initial data investment needs to be factored for annual maintenance of the data. When we implement such projects, we can spend 20-50% of the time structuring the data to make it insightful, and when we go back a year later, we find that the company hasn’t been maintaining the data; the data maintenance is the most critical component and is where most companies cut costs. Accuracy of the data is where the data costs rise; the more dynamic the data, the more often you have to update the information.”

In today’s highly competitive global marketplace, which is in a continuous state of transformation, possessing the ability to visualize and analyze BI quickly and affordably becomes a critical key to success. Now, companies of all types and sizes can leverage sophisticated LI in a predictive mode for planning and strategic purposes and can gain a better understanding of trends and patterns from their data as well. For example, an insurance company can leverage third-party ‘cause-related’ data to help predict severe weather patterns and conditions such as hail storms. They can then notify their ‘at risk’ customers in advance so they can adequately protect their family, vehicles, homes and other assets. This LI can benefit all by increasing safety and reducing claims, while improving customer service and enhancing the overall customer experience.

Food Distribution: The Art of Demand Allocation

Stubb’s Bar-B-Q is a small but aggressive and fast-growing manufacturer of barbecue sauce in Austin, Texas. A key business challenge for Stubb’s is demand allocation or cost-effective distribution and growth. “With 400,000 grocery and retail store opportunities out there, the question is, ‘How do I pick and prioritize the right stores to distribute my product to in a powerful way?’ Another associated problem for companies like Stubb’s, which have to compete with Kraft and other multi-billion dollar brands, is that we can’t afford to buy syndicated market data and we are too small for IT,” said Chris West, Vice President of Sales for Stubb’s.

To help address these business issues, Stubb’s called on aWhere, Inc., an LI Company focused on how consumer goods companies understand and manage their business. aWhere has been working with Stubb’s to improve their marketing, sales and forward-looking supply. They started by looking at a two-year sales history. Next, aWhere ran a regression and spatial analysis to help to predict the number and location of stores Stubb’s could add. By analyzing the location opportunities within a retail chain, Stubb’s can present a very compelling story as to which stores should and shouldn’t carry their product and which stores would benefit from promotions. They looked at three different levels of geographical analysis to help determine this: 1) demographic profile of their buyers; 2) demographic profile of potential target stores; and 3) overall demand and/or growth rate for their category at a market (macro) level.

Now Stubb’s has a better idea of predicting the number of stores they can add and the next set of stores to which they should distribute, rather than grappling with many thousands of stores they think should be buying their product. aWhere does this by geographies, since Stubb’s needs enough stores in each area to make distribution and marketing cost effective. This system provided Stubb’s with distribution guidelines and a road map for growth.

LI helps to build a compelling story as to why a retail chain buyer should buy a product they should be carrying on their shelves. In this case, Stubb’s can tell the buyer which stores they want to be in, along with why they have the demographics of customers who would want to buy their product. This reasoning helps to make Stubb’s come across in the marketplace as a smarter and bigger company. Stubb’s and aWhere even looked at new trends, such as gluten-free sauces, and where the demand is.

Jim Pollock, President of Vertical Markets of aWhere, said, “Smaller companies like Stubb’s Bar-B-Q are hungry for affordable LI. There is no need for a GIS department, with LI now offered as a managed service on a hosted platform from companies like aWhere.” Today, with the aid of sophisticated and scalable LI tools provided as needed and on demand, small and mid-market companies can prioritize the stores with which to work and predict where their product will sell the best.

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