The world of spatial data

Location and Business Intelligence: The Great Divide

Business intelligence (BI) teams are largely disconnected from one of the fastest growing data and analysis trends today. Location Intelligence (LI) is predicted to grow at nearly double the rate of business intelligence between 2022 and 2030*. An explosion of location data from mobile and IoT devices, social media, imagery, and other sources is generating hundreds of new use cases from business units.

In 2019, Gartner said, “Moving forward, analytic and information leaders must put the “where” question to all new BI and analytic projects.” Yet nearly five years later BI teams, constrained by a lack of understanding, skills, and the basic mapping and spatial analysis capabilities of their BI tools are struggling to deliver.

Many organizations have dedicated Geographic Information Systems (GIS) teams, huge investments in geospatial technology, have built massive spatial data sets, and deployed dozens of geo-centric applications for the business, primarily operational users. Too often, however, these assets are not readily available, or freely shared, with BI teams. There’s both a technology gap and a cultural gap too wide to bridge.

BI tool vendors have incorporated simple spatial data sets and mapping features into their products but have reached a chasm beyond which they won’t, or can’t, cross. GIS teams, frightfully conscious of the lack of geo-awareness among BI teams and how they might use shared spatial data, maps, and analysis tools in ways not intended are reluctant to build a solid, wide bridge for BI teams to travel freely.

Likewise, GIS vendors have added simple charts, graphs, and other BI artifacts to their tools but have stopped short of building yet another BI platform. Both BI and GIS vendors recognize that expanding their feature sets into the others’ domain would be a massive, risky undertaking against entrenched players in domains they don’t fully understand. So, there they stand, each on the brink of their familiar domains looking at greener pastures they may never graze.

Spatial and Non-spatial Data in Business Intelligence Tools
What Comes in the Box

Today, nearly all business intelligence (BI) tools include some basic spatial data in the form of point, line, and polygon features to create map charts for dashboards and reports. This data is usually acquired from government sources or purchased from commercial spatial data vendors and installed locally in the BI environment. It generally includes only high-level data such as country/state boundaries, points for cities over a certain population size, and a limited amount of descriptive data for each spatial feature such as country name, city name, or zip code.

The descriptive data Is primarily used as a key to link the provided spatial features to internal business data from ERP, CRM, or other systems as well as other external data such as 3rd party demographic or lifestyle data. It’s this linked data that interests BI teams. Their stakeholders want to visualize this data as map charts and perhaps perform some spatial analysis on it, such as count the number of customers over the age of 50 within 5 miles of a store.

In addition to the data that ships with BI tools for local installation and access, most also include web access to a variety of professionally compiled and styled base maps that provide a geographic background onto which the provided boundary polygons and points are overlaid. Base maps from Google, Bing, OpenStreetMap, and others show satellite imagery, roads, points of interest, and other spatial data. Base maps provide contextual information for the overlaid data for visual analysis of what is nearby or to see patterns and other spatial relationships among the data.

When You Need to Think Out of The Box

Beyond the spatial data provided for local installation, and web-accessed base maps, BI teams are left to their own devices to find or create custom spatial data (like their own sales territory boundaries) or custom base maps (with their own layers and styling) for their dashboards and reports. This often means leaving a familiar BI tool for a frustrating search for spatial data, a painful ETL process, learning a new mapping tool, and custom coding. It can also lead to localized spatial data silos that are out-of-sync with data created by the GIS team, creating a low-trust environment where stakeholders question spatial data sources, accuracy, and fit for purpose.

What’s needed is a way for BI teams to easily discover, access, and use the spatial data created, used, and maintained by the GIS team in support of the day-to-day operations of ERP, CRM, and other systems. This data accurately reflects the spatial state of the business, is authoritative and trusted, and dashboards or reports that use it will have credibility in the eyes of stakeholders.

Spatial Visualization and Analysis in Business Intelligence Tools
What Comes in the Box

Spatial visualization and analysis uses the location of things to explore data in a geographic context, identify patterns, determine relationships, make predictions, and more. It helps answer questions such as “Where should we open (or close) stores?” or “What is our policy exposure if the hurricane follows this path?”. There are thousands of location shaped questions like this spanning all industries and business functions, and there are hundreds of spatial visualization and analysis tools to address them – far more than any BI tool could reasonably support.

BI tools generally support the mainstay of spatial analysis: map visualizations. Users can add a few layers of spatial data to a map, such as crime locations, roads, and zoning and through nothing more than visualization may see a pattern that crimes are most often committed near intersections in commercially zoned areas. No spatial calculations are required. As long as the data is layered using the same spatial reference, and there are not too many layers to consider, the eye can see relationships and the mind infer patterns with no help from spatial analysis algorithms.
Some BI tools may also support a limited set of computational spatial analysis tools such as drive time buffers, which are hard for the mind to calculate from only a visualization. Questions such as, “How many patients can drive to our office within 15 minutes?” are best left to spatial analysis proximity tools. Proximity tools are just one category of spatial analysis tools. There are tools for overlay analysis, buffer analysis, image analysis, spatial statistics, machine learning, and much more. Only a handful of these make it into BI tools, and most are quite basic.

Not having access to the advanced visualizations and computational spatial analysis tools found in pure play mapping platforms is one of the major shortcomings of all BI tools today. What is provided out-of-the-box goes only so far, and again BI teams are left to their own devices to bridge the gap. This usually means one of three things:

  1. Go to the GIS team, if you’re lucky enough to have one, ask them to do it for you, and wait… and wait…, or
  2. Do it yourself and deal with finding or creating the right spatial data, picking the best map visualization or analysis, and custom coding using one or more external mapping tools, or
  3. Try to make the high-level data work by aggregating business data to the level of the spatial geometry data shipped with the BI tool, losing insight resolution along the way.

None of these alternatives are optimal when decisions based on the most accurate, detailed, and authoritative spatial data are needed today with answers coming tomorrow or next week, or further in the future.

When You Need to Think Out of The Box

SelectHub found that dashboarding and visualization were the top two desired features of BI software from a recent survey of 600 businesses, and the ability to see the data in a centralized location was seen as crucial. While BI tools have greatly expanded data access and visualization options for tabular data within dashboards, the options for spatial data have remained limited. Consequently, for more visualization and analysis options you need to leave the central location of your dashboarding tool and enter an unfamiliar realm of spatial data, cartography, and spatial analysis.

The survey also found that BI users saw advanced analytics of any kind, spatial or non-spatial, as “amenities, not necessities”. Data science with its data mining, machine learning, and artificial intelligence tools is another realm where BI teams don’t frequently venture, relying instead on data science teams to do the heavy lifting and provide them results for visualization, blending with other data, or additional analysis. BI teams have neither the computing infrastructure nor domain knowledge to do it themselves.

The same holds true for spatial visualization and analysis. To get to the most insightful maps or advanced spatial analytics you need to leave your BI tool and use a more capable location intelligence (LI) environment. What’s needed is a way to access these capabilities without a huge investment in money and time to acquire new technology and learn how to use it.

Turning Point

If BI teams find themselves spending too much time finding or creating spatial data outside of their tools, building dashboards with maps that add little to the story, and teasing insight from simple spatial analysis tools, what can they do? They do not want to become geography experts or cartographers any more than they want to become data science experts or programmers. One approach is to leverage a location intelligence platform that provides all of the spatial data, visualizations, and spatial analytics they need from within their familiar BI tool.

Location intelligence platforms come in all shapes and sizes. Some are full featured GIS systems that have all that’s needed to create, manage, access, use, and share spatial data across the enterprise. Others focus primarily on using, not creating, spatial data. Some primarily provide spatial data. Nearly all of them make their content available as web services with “pay-as-you-consume” pricing models.

Regardless of an organization’s size or needs, Esri’s location intelligence platform is known for its breadth and depth of functionality, ease of use, and competitive pricing. As the global leader in location technology, Esri’s platform is already entrenched in tens of thousands of organizations worldwide, and their users create, maintain, use, and share spatial content within its core user base. Sharing it with BI teams, however, can be challenging.

Filling the Gap: Attain Insight Map Intelligence

What is needed is a an affordable, simple, quick to implement bridge to access this wealth of spatial data, map, and analysis services from within your BI tool. Attain Insight Map Intelligence provides a bridge between Esri’s ArcGIS location intelligence platform and IBM Cognos or SAP Business Objects BI platforms.

Designed for Esri ArcGIS Location Intelligence Platform

Unlike general purpose middleware and ETL products that have limited support for spatial data and maps, Map Intelligence is specifically designed for LI to BI use cases. It allows you to easily access spatial data and maps published and shared within the ArcGIS platform. Dashboard authors can blend this content with business and other data to create custom maps or enrich their data with spatial attributes for use in other BI visualization or analysis tools.

No Additional Spatial Technology is Needed

BI products often include a limited-use commercial OEM or open-source mapping platform so users can create custom spatial data, maps, and spatial analyses beyond what is delivered out-of-the-box. Likewise, integration solutions from other vendors are often based on their own mapping platform. Map Intelligence in not a location intelligence platform and does not introduce any competing technology to the ArcGIS landscape. This eliminates disparate silos of technology, spatial data, and applications while promoting a one-platform location intelligence strategy for the enterprise.

Maps Transform from a Local Resource to an Enterprise Asset

Map Intelligence lets BI teams tap into the spatial data, maps, and analyses created by GIS teams who entrust business teams that understand where the data comes from to create their own maps and spatial analysis products. GIS teams have neither the time nor desire to make pretty maps for BI teams. They are supporting operations. Map Intelligence delivers a self-service environment that puts trusted spatial and operational data in the hands of BI users.

Some BI users may go beyond reuse and into the creation of their own, departmental spatial data using the same ArcGIS tools and workflows as their GIS colleagues. Rather than local siloed data created with mapping tools shipped with the BI product, this data is created, used, and shared using a common platform. Most importantly, Map Intelligence promotes a whole new way of thinking how spatial data, mapping, and analysis is accessed, created, and used by BI teams as part of an overall location intelligence strategy for the business.

To find out more about Map Intelligence, download the fact sheet or book a personalized demo.

* Future Business Insights and Grand View Research Inc.


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