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Collaborative BI sets stage for collective user action


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Location intelligence finds its way into BI processes

Slicing and dicing data cubes is no longer the best way to uncover spatial relationships in corporate data. A new set of tools is mapping the route to improved business intelligence.

Business intelligence (BI) systems have long struggled with the location dimension of the data contained in everything from spreadsheets to data warehouses and the transactional business systems that feed them. 

Customer, product or channel dimensions can be visualized and analyzed with a wide variety of graphical tools. But geographic data is forced into these tools like a square peg in a round hole. 

Help is on the way. Location intelligence technology brings a new set of tools to the table, ones better suited to the job of tapping into geographic information meaningfully and effectively. 

Location intelligence has a role to play in these BI functions: 

  • Reporting and visualization. Traditional BI is dominated by standardized reporting with some limited filtering available to business users. Location intelligence plays a similar role by providing maps that give users the ability to pan, zoom, drill up or down, turn geographic layers on or off and do some limited filtering for thematic mapping. 

    Within all of the leading front-end BI tools, interactive maps are replacing or augmenting standard table and chart views of geographic data. Business data is typically mapped as dots or icons, shaded polygons or “hot spots” based on some characteristic of the data, such as sales volume or cost. Recently, more advanced geographic visualizations such as three-dimensional representations and space-time animations have found their way into some BI tools and end-user dashboards.

    Whether simple or advanced, visualizing data on maps is often a vast improvement over pie and bar charts. In many cases, it leads business analysts and other users to findings and conclusions in much less time and with less effort than spinning, slicing and dicing a cube of tabular information looking for spatial relationships—relationships they might never even find using the old method.

  • Data integration and quality. Geographic data shares many of the same problems associated with other types of data, such as multiple source systems and spatial data formats, data quality issues and semantic inconsistencies. But it adds a few more, like different geographic reference systems (so things don’t line up properly on maps) or different spatial dimensions (for example, one system maps buildings as points, another as polygons).

    Location intelligence technology can help with spatial data integration and quality improvement efforts. Tools are available to convert various spatial data formats into a common one; re-project data from one coordinate system to another; and clean up and validate spatial data before it’s used. The goal is to create a single version of the truth for spatial data that can be used throughout an organization to create accurate, meaningful and consistent maps and serve as a foundation for advanced spatial analysis. In the new world of self-service reporting tools for end users, effective processes for spatial data integration and quality management are critical. 

  • Advanced spatial analysis. In both business intelligence and location intelligence, a relatively small group of people play with the data to discover what they don’t know about it, employing heavier-duty analysis methods such as ad hoc querying or data mining. Somewhere, there is an imaginary line that divides simple location intelligence from advanced spatial analysis. One way to think about the line is to say that all analysis that can be done in the average person’s head through visualization is on the simple side, while analytical tasks that require spatial statistics, clustering and forecasting or other spatial computations and models fall on the advanced side. 

    In short, if you can’t get it from looking at a map, you need some advanced techniques. Advanced spatial analysis tools relate to familiar advanced analytics software for statistical analysis, data mining, real-time forecasting and business optimization, but they’re modified to address the unique characteristics of spatial data and relationships.

  • Collaboration. Social networks are driving a rush toward increased support for collaborative BI capabilities. Standardized reporting, querying and advanced analytics all have their limits, and it’s often difficult to uncover information about the cause and impact of business problems and corrective actions that could be taken. But often, multiple users working collaboratively can fill in data gaps, raise new considerations and make collective judgments and decisions. 

    Maps have always been magnets for people, as evidenced by the countless social media sites that use Google Maps, and many business professionals want the same kind of collaborative experience to be available in the workplace. These “prosumers” have been heard, and maps now can easily be created, shared, annotated, extended and reshared using cloud offerings without the need for IT resources or in-depth location intelligence skills. Self-service has come to location intelligence along with collaboration. 

    It’s easy to see the parallels between BI and location intelligence, since they share major areas of focus and serve similar purposes. But until now they largely have evolved independently of one another. Today, a perfect storm of technology, data and a DIY mentality among end users is bringing these two ships together and creating a wave of intelligent maps and location-based analytics.
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