The Gap Shrinks Between GIS and BI

Much has happened since my last post (more about that later).  2013 marked a significant increase in momentum of the business world embracing GIS as a fuel additive to BI.  Today I came across this article that inspired me to return to posting the proof in the pudding.  It is by Drew Robb and posted by, an outlet dedicated to CRM, ERP, and BI topics.

The article provides examples of GIS implementation with BI and Big Data at General Motors.  Under his section titled “Behavior Modification” Robb says:

In tandem with Big Data and remote sensor networks, what is emerging is a services-based GIS model providing applications for a multitude of devices. You end up with maps combined with analytics, mashups, social media, marketing and sales data.

It is a mouthful difficult to digest without having had a taste of what “maps combined” can provide.  Take a look at the examples in the article as you imagine how GIS can help your business organization.


My Business Intelligence software has a map…What’s next?



Jorge Garcia on this TEC Blog gives us an important reminder that:

BI software providers have been enhancing their data analysis capabilities through the intensive use of geolocation information and the visualization of their results in a geographical manner.

Yes, and isn’t it valuable?  But as I have mentioned before, this typically includes linking and aggregating records to color-coded geometries such as countries, states, counties, and zip codes.  With the right data, census blocks and block groups may also be used.  Customization would include using geometries such as sales regions, store locations, or store influence areas.  Doing so can be an over-generalization of the data, as Forbes contributor Steve Milton points out in this article.

…business analysts have often oversimplified [the location] dimension of their data because of a lack of the BI system capabilities or access to location indexed data.

This type of mapping works well for the casual Business Intelligence (BI) user, but not for the type of analysis that decision makers need to gain serious insight into their vital data.  Visualizing data on the map can have a much more powerful effect for decision makers through dynamic context filtering using the map interface.  What does this mean exactly?

BI software is geared first and foremost at providing a snapshot of the most important key performance indicators (KPIs) which provide a real-time, holistic snapshot of the state of health at a glance.  As BI software has evolved, more in-depth tools have added value through data analysis, or drilling down from the holistic view to more detail…the kind of detail that reveals the answers to important questions.  I briefly mention this distinction in an earlier post where I suggest that the BI user should have both methodologies available to reach the same goal.

BI software makes available graphical visualization tools that allow the user to understand the data at a glance.  Picture such visuals as bar charts, pie charts, graphs, speedometers, red-to-green traffic signal-style gauges, etc.  These visuals represent a measure of specific criteria of the attached records of the database view.  Then, as the user performs a selection query or applies a filter to narrow down the number of records displayed, the visuals automatically update to represent the specific criteria of the smaller set, or sub-set, of database records.

In this same way, a map can also be attached to the records of the database view with the map color-coded to represent the specific criteria of the data.  My point is that the map is used in the same way any other visual graphic.  The difference, however, is that this map widget adds a little something extra.  The map allows the user to begin seeing spatial context and relationships in the data, such as clustering and proximity, that went previously undetected.  This sparks the user’s interest further to more data exploration, applying refined filters, and asking “what if” types of questions to determine why the data is the way it is and bring to light new business drivers.

This is the turning point when the map should help and not hinder the user.  This is the point when the map should become intelligent.  The map should become an interface for querying and filtering.  Now zooming in or clicking on the map will also automatically update the visual gauges.  The map now drives the data exploration and analysis as an intuitive user interface for selection queries and filters on the data views.

To illustrate this concept, I will dedicate the next few blog posts to this notion of taking the BI map to the next level.  To stay organized, I will give some of my own names to the ideas I will be presenting, starting with the basic BI map, or map widget.

Schedule permitting, I will move on to present my ideas for:

  • Chloropleths and color ramps
  • Scale context filtering
  • Gnat assets
  • Custom map aggregation
  • Bi-proxi hot clusters
  • Real-time roundups

After illustrating these concepts, perhaps I will create my very own ranking system based upon my wish list and begin reviewing and ranking available BI software solutions in the marketplace.

Stay tuned!

GIS provides a complimentary platform


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Here is a great article making a valid point about disparate system strategies between business intelligence (BI) software and analytics.  I like the author’s descriptions of why they need each other and that a complimentary strategy is required. I especially like how the author, Dave Stodder, suggests that the strategy must overcome the people issues associated with utilizing technology.

“Organisations must bring together leaders from IT and business units, particularly marketing, to improve understanding and foster better collaboration.”

Unfortunately the author doesn’t give away all his secrets of accomplishing this complimentary strategy.  My suggestion for consideration is a common language that both BI and analytics can use to bring together the full array of data (structured and unstructured) in a way that users can easily visualize the information and answer their questions.

What is my secret to accomplishing this complimentary strategy?  Geographic Information Systems (GIS) provides a common platform for bringing together data and information from multiple systems, sources, and avenues in a way that is easy for users to understand at the “speed of sight.”

GIS is a very scalable solution that can be implemented in a modular approach and can integrate at many levels with most any system that houses spatially-aware data.  The disadvantage includes the overhead of an additional system for maintaining, but when this additional system provides synergistic cohesiveness to all other systems, the pros usually outweigh the cons.  To have one system that can effectively serve as a single point-of-entry, visualization interface to data from mulitiple systems that brings everyone to the same level of understanding is priceless!

Painting Statistical Pictures

In a previous post I talk about one difference in approach to visualizing data: viewing the report and drilling down to the map, or viewing the map and popping up the report.  Mapping software has utilized the pop-up bubbles for a while now to display data associated with features on the map, and here is one that caught my attention.

This one came to me via an email newsletter from Safe Software, the makers of FME (Feature Manipulation Engine) software, wherein they highlight a project by the City of Hamilton’s Public Health Unit in Ontario, Canada.  They use FME to automate data visualization through KML (Google Earth), as well as statistical analysis results.  The format speaks for itself.

BI data in the bubble

What I love about this approach is that, in addition to the appealing and informative data inside the bubble, the points on the map are also color-coded, contain a symbol, and display a label, all of which provides insight and meaning at-a-glance before ever needing to pop up the detailed data.  For comparison, this is kind of like using conditional formatting in Excel to color-code records based upon a key attribute, except that here we can use color, symbol, size, and label all at once.  Here key data attributes are displayed on the map to reveal the pattern in the data and provide a platform for easy interpretation by all.

The Technology Love-Hate Relationship – Part II


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I love this article written by  WILLIAM H. DAVIDOW:

Virtual Reality Is Addictive and Unhealthy

Here’s why…

As a technologist and fan of augmented reality, I first noticed Davidnow’s stance toward people constantly staring at their smartphones and listening to their iPods.

…I see people walking down the street, eyes fixed on the screens of their mobile phones, ears plugged into their iPods, oblivious to their surroundings…to reality itself.

Davidnow makes a good argument that tools are making the rules to the point that our tools are now managing us.  He gives some solid examples to argue his point, such as the role “tools” played in facilitating the 2008 economic crisis and the liquidation of Borders bookstores.

He talks about the rapid evolution of tools compared to the evolution of human society and makes a statement that I agree and disagree with:

I now believe that our minds, bodies, businesses, governments, and social institutions are no longer capable of coping with the rapid rate of change. And it is obvious that this change is indeed more rapid than any comparable change that came before.

Currently working under the umbrella of a local government entity, I see examples everyday of our struggles to keep up with change.  As I posted before, however, I believe we are still capable, even jittery, to inject more technology into our lives.  We are addicts!

By the end of the article, I had already come to the same conclusion that Davidnow iterates:  “…take control of your tools.”

How do I reconcile with my addiction to technology?  First, I loath staring down at the screen of my phone.   The tools must be designed around our lives, not such that we are required to mold our lives after the tools capabilities to adopt or use.  I think that as the technology becomes more ubiquitous with reality, the tools must be designed to by non-pervasive.  Opaque, side-notes that are easy to obliterate at will on a second’s notice to allow us to focus only on our reality.

Second, who’s to say that virtual information is not an important part of reality.  Just because my eyes and ears cannot receive certain kinds of “real” input directly from my limited senses does not make that information any less important to my here-and-now.  The information must be real-time and relevant, not distracting.

And last, have the discipline to use technology tools responsibly.  Learn to recognize what is and is not appropriate for your given situation and act accordingly.  Do not lose control of your divinely designed senses.  Start by following Davidnow’s good advice:

I have shut off most alerts and reminders on my computer and smartphone. I check for e-mail on my own schedule, just a few times a day. At home, I have built a physical wall around the virtual world. I let myself read news on my iPad anywhere in my home, but I answer e-mails and conduct business only in my office. I heed Staudenmaier’s advice and never end important conversations by glancing at my smartphone. My iPhone is never ­present when I am out with my wife, listening to the challenges my kids are facing, or playing and laughing with my grandchildren.

And then, when the time is appropriate, put on the Google Glasses!

Business Planning: Disparate Systems No More


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I mentioned in a previous post about the technology curve how I looked forward to the day when my phone, media player, gps, and camera were all rolled into one device.  There are many similar convergent software applications that can provide similar benefits.

Here is a great example where detailed geographic road and physical structure mapping is combined with a demographic database and location based analytic software: Maps, Demographic, Location!

The result is sophisticated mapping and database technology combined, a visual demographic engine of high specificity that taps into trends including data visualization, cloud computing, location intelligence, data analytics and in memory processing.

The significance is that the combination of these solutions provide better information for decisions than each one separately.  More is always better IF you know how to combine, filter and apply the information that is relevant for the task at hand.

As needs change and demand for these types of service solutions grow, I believe we will see more convergence and partnerships between seemingly disparate systems to solve specific problems.  I can only imagine how a geospatially enabled system such as this could empower the right business intelligence (BI) dashboard for all my business decisions!

Calgary Co-op commits to Location Analytics for Better Decision-Making



Calgary Co-op, one of the largest retail co-operatives in North America, has acquired Esri Business Analyst to:

  • efficiently map stores
  • integrate in-house business data
  • visually analyze future operations
  • use transaction information
  • better understand market direction
  • identify business strategy successes
  • combine customer data with demographic and geographic information
  • better analyze capital investment
  • enhance business intelligence
  • and gain advantage over competitors

Sounds like priorities are straight.  Imagine what ESRI Business Analyst could do integrated with a good business intelligence (BI) solution.  The wheels keep on turning!

Petco – Business GIS position

“Apply business acumen and GIS expertise to support the market planning and site selection process at Petco.”

Way to go Petco!

And here’s the why:

“Opening new stores is a critical component of Petco’s growth strategy,” said Shawn Hanna, Petco’s director of financial analysis. “We use spatial analysis to help us target the best locations for new Petco and Unleashed by Petco stores around the United States.”

Twitter Geo-fence Filtering: The Real-time Dilemna



Twitter is known for real-time data.  There are too many examples to list that have surfaced over time that have proven the value in the here-and-now style crowd-sourcing information over the lagging traditional media and news outlets.

One example is the twitter account that was setup for the recent Waldo Canyon wildfire in Colorado Springs:

Unfortunately the best data available is often the kind that is aggregated well after the fact.  This is typically the type of data of which maps are made.

Here is an example where six days of tweets were filtered and aggregated to produce a Beer Vs. Church map by Chris Crum.  This information serves a great purpose, but it is still only beginning to touch upon the type of tools to come.

Taking this type of data (real-time, streaming, feeds, etc.) and transforming it into information that can be visualized for better real-time decisions.  This is what separates the good decisions from the best decisions.  Here is an example where Bern Szukalski uses Yahoo! Pipes to transform web feeds into real-time map layers.

I envision a business intelligence (BI) software that has social media platform tools built in that will instantly map streaming data to reveal the patterns and allow intuitive visualization as they are happening!

A Picture is Worth a Thousand Numbers


Information Management offers 7 tips for effective data visualization.  One of those tips is titled “A Picture is Worth a Thousand Numbers.”

Because of our human ability to understand relationships quickly based on size, position and other spatial attributes, the eye can summarize what might otherwise require thousands of numbers to convey.

I have heard many cartographers say that if a picture is worth a thousand words, a map is worth a thousand pictures.  This description of our human ability hits at the core of the power of data visualization.  There are many great books and blogs dedicated to the subject of data visualization, and the tips provided apply to data visualization, cartography, or software development for that matter.  It all comes back to finding the most efficient way in which our minds turn data into information.  This equates to better decisions.  This is the goal of business intelligence (BI) software and why BI software should not compartmentalize, but emphasize the visualization of spatial data.