Analyze, Visualize, Act: The Evolution of Situational Intelligence


Situational intelligence solutions first emerged around five years ago under the unofficial motto, “Visualize, Analyze, Act.”

Implicit in that statement were the actors for each of those verbs. Situational intelligence solutions visualized big data problems, but it was users who analyzed the visualizations and then took action.

In recent years, as big data analytics have become more capable and affordable, the description of situational intelligence has shifted to, “Analyze, Visualize, Act.”

As the description has changed, so have the actors.

Today, situational intelligence solutions analyze large amounts of big data and then visualize the results of that analysis for users. And increasingly, based on prescriptive and predictive analytics, situational intelligence solutions either recommend courses of action to users, or take actions directly.

Don’t worry–situational intelligence continues to require human intervention. Users are still the ones who supply the constraints that situational intelligence solutions apply to solve problem. They are still the ones who specify what courses of action might be appropriate to recommend or carry out.

As automated action becomes more common, maybe the next tagline will be, “Analyze, Visualize, Act, Report.”


How is Situational Intelligence Different from Business Intelligence?


Situational intelligence and business intelligence–they sound similar. They rely on computers. They help businesses make better decision. So what’s the difference?

Forrester Research defines the business intelligence market as “a set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.”

Similarly, the Gartner Group defines business intelligence as “an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.”

What’s missing from these definitions that sets situational intelligence apart?

Situational intelligence includes the historical perspective delivered by traditional business intelligence, plus it takes into account the immediacy, diversity, and scale of the modern data landscape.

Specifically, situational intelligence adds four characteristics to business intelligence:

  • Multiple analyses: Situational intelligence extends traditional business intelligence by also analyzing operational data, location or geography, network relationships, and external factors such as social media, traffic, weather, and video. These additional analyses allow you to ask and answer a much broader range of questions about your business.
  • Pinpointing data: Situational intelligence locates the small set of anomalous data you need to attend to among the high variety and velocity of big data. Business intelligence excels at summarizing categories of data.
  • Immediacy: Situational Intelligence is focused as much on present, real-time and future performance as past performance, whereas business intelligence is mainly rooted in past performance and trends extrapolated on such performance.
  • Bi-directional: In addition to responding to user commands, situational intelligence often initiates interactions with users by highlighting anomalous data or risky situations through alerts, alarms and visualizations. Business intelligence is mainly driven by users requesting information through queries and reports.

An example might help clarify the distinction.

You can imagine that, as a business owner, you might want to view a report of product sales by ZIP code. This is a typical business intelligence activity.

Imagine now that you can also view a map showing the percent and amount of product sales by city, neighborhood, street, or any other ad-hoc geospatial area you specify. Maybe you want to identify the effect of severe storms on sales of your product at particular stores, view the impact of your social media initiatives in various neighborhoods, or make more accurate predictions about buying patterns at different stores to improve your production and distribution planning. These are just some of the ways situational intelligence extends beyond business intelligence to improve business.


Situational Intelligence and Smart Cities: Crisis Response


So far in this series on situational intelligence and smart cities, we’ve looked at services in constant use such as transportation, water, and energy. How might the concept of progressive use cases help evolve smart infrastructure for more intermittent services, such as crisis response?

As a first step, situational intelligence supports integrating all first responder communications and locations onto a single, citywide GIS system, possibly on a video wall in a city operations center. By knowing where first responders are located and using route optimization analysis, city officials can dispatch the closest and best-suited people in time of crisis. Quick response saves costs, property and lives.

Operational savings can be applied towards increasing coordination between city agencies in response to crisis. With all first response systems shown on a single map, the city operators can more easily dispatch appropriate teams to the scene. For instance, a large apartment building fire might require fire fighters, police for crowd and traffic control, shelter workers and counselors for displaced residents, and even animal control to help rescue and reunite pets and families separated in the fire.

With a history of crisis responses to analyze with other information about weather, building inspection reports, crime trends, and even social media, city officials can expand situational intelligence to crisis prediction models, early warning systems, and crises prevention programs.


Situational Intelligence and Smart Cities: Energy, Part 1


The previous entry in this smart cities series examined how situational intelligence could be used to improve and optimize municipal water systems. Let’s look at how progressive use cases can help evolve smart infrastructure for city energy services and consumption.

As a first step, situational intelligence supports aggregating the energy consumption of city facilities into a single view of energy usage across city operations. Aggregation leads to efficiency in accounts payable and provides leverage for negotiating better energy rates with suppliers.

With the savings from accounting efficiencies and lower energy rates, city officials can invest in the energy efficiency of city facilities. Increased energy efficiency lowers costs, and the savings flow directly to the bottom line for improved fiscal health. Situational intelligence supports analyzing and scoring buildings according to their energy efficiency and projecting savings from capital investments in efficiency.

With buildings and facilities operating more efficiently, cities are in a position to begin implementing microgrids and energy storage devices to keep city services running in times of crisis, take advantage of renewable energy sources such as solar and wind, and participate in demand response programs that would increase reliability, lower energy bills and reduce carbon footprint. Situational intelligence maps and projects energy availability from storage devices and from renewable sources.

Energy and water, our most recent topics in this series, are constant needs for city residents. In a future blog post, we’ll see how progressive use cases might help to evolve services of a more intermittent nature, such as crisis response.


Analytics On Display At DistribuTECH 2015


Analytics are on display—literally—at DistribuTECH 2015, the North American utility conference running this week in San Diego. At the Space-Time Insight booth, I walked through a virtual reality inspection tour of a substation, with asset analytics displayed next to malfunctioning equipment for complete assessment and troubleshooting.

The roster of exhibitors at DistribuTECH includes nearly 80 companies claiming ‘Data Analytics’ as a primary descriptor of their products and services. Exhibitors can claim only a handful of descriptors, so their “vote” for ‘Data Analytics’ demonstrates industry interest in the topic.

Analytic offerings come from several different sources:

  • Dedicated analytics companies such as Space-Time Insight
  • IT companies such as Intel and Oracle
  • Traditional utilities vendors that offer analytics such as Elster and Siemens
  • Consultants such as Accenture and CapGemini

And of course, roaming the exhibition hall turns up permutations resulting from partnerships, OEM agreements and other forms of collaboration between these sources.

This plethora of analytics companies may prove that the utility sector is finally recognizing the growing challenge and opportunity of big data and the Internet of Things.

But it seems like many analytics solutions are still offered as siloed systems, focusing on one vendor’s equipment, one asset class, or one part of the utility value chain.

Some of the dedicated analytics companies, such as Space-Time Insight, and some of the consultants, such as Accenture, are grasping the opportunity of situational intelligence to span the analytic silos within utilities to create actionable insight to improve reliability, safety, and affordability.

Time will tell whether the trend toward insight across silos, as well as within them, grows to keep pace with big data and the Internet of Things in the utility sector.