Not Your Father's BI: Situational Intelligence in a Real-Time World

By Rob Schilling

Insight. Understanding. Knowledge. These are the outcomes that separate raw data from "intelligence." Data itself is often meaningless, whether in the form of numbers on a spreadsheet, letters in a document, or pixels in an image. It is the act of analysis -- finding patterns, identifying anomalies, and interpreting results -- that creates intelligence.

As the information available to businesses for analysis has become increasingly varied and complex, extracting meaning from data is more challenging than ever, especially in environments where decisions need to be made in real time.

Consider, for example, the impact that information complexity and time constraints have on today's utility companies. As the deployment of smart meters and smart grids add to the already massive amounts of data that power providers need to correlate and analyze in order to operate safely, efficiently and cost effectively, many are struggling to keep up with both the volume and pace. The ability to digest and understand multiple data streams as fast as possible is critical -- accurate intelligence can mean the difference between handling a crisis (such as a severe storm or unexpected power shortage) with confidence and being caught unprepared, placing lives and property at risk.

For many businesses, running pre-defined reports on month-old, week-old, and even day-old data isn't acceptable anymore. Competition, customer expectations, and the real-time nature of the Internet are driving demand for intelligence that facilitates decision-making firmly rooted in the "now." A new approach is needed -- one that includes the historical perspective delivered by traditional BI, and takes into account the immediacy, diversity, and scale of the modern data landscape. This 360° view of relevant information is the objective of situational intelligence.

Connecting the Data Dots

Businesses with critical assets (such as utilities, oil and gas, and transportation companies) have access to massive amounts of data, from a variety of sources, both internal and external, structured and unstructured. These may include data from such sources as field sensors, asset systems, weather reports, SCADA systems, customer databases, and call detail records. The problem is that much of this data is not being leveraged to its fullest potential, for several reasons.

First, different sources of data are often siloed within disparate systems and repositories, saved in different formats, or "owned" by different organizational departments, making it difficult for decision makers to "connect the dots" necessary to understand the bigger picture. For example, one system may identify a service disruption, but determining which customers are affected, how to prioritize service restoration to those customers, and identifying which personnel and equipment are best suited to address the issue may require data from a number of other diverse systems -- systems that don't easily communicate or integrate with each other.

Second, many of the processes by which data is analyzed are slow and prone to error. Too much time spent on the tedious review of spreadsheets or manual transfer of data between departments can lead to costly delays and miscalculations in a fast-moving situation, a problem that is becoming increasingly acute as the data that organizations now need to process is growing rapidly.

Finally, critical data may be left out of an analytical process altogether because it was not considered at the time an existing BI system was implemented and is, therefore, not available to users. The result? Poor decisions are made with incomplete data.

The Six Categories of Situational Intelligence

Situational intelligence seeks to fully capitalize on all relevant sources of data by mirroring the way the human brain processes multi-faceted situations and events. Decision-makers like to be able to digest information quickly and actually see what's happening (and where and when it happens) so they can connect the dots faster than they can search for correlations within static spreadsheets or text. To this end, situational intelligence focuses on creating multi-dimensional visualizations of data so that information consumers can see, understand, and take action on unfolding situations at a glance. This "360° insight" includes the following six categories of intelligence:

#1: Operational intelligence

Operational assets (such as transmission lines, drilling rigs, cellular towers, delivery trucks, and inventory stores) are critical to the day-to-day performance of many businesses. The goal of operational intelligence is to acquire and analyze the data these assets constantly generate and keep tabs on the health of the assets so that proactive action can be taken to address issues, such as maintenance needs, before they impact service availability, safety, and operational efficiency.

#2: Environmental intelligence

Environmental events such as severe storms, heat waves, wildfires, and heavy snow can wreak havoc on operational performance. In other situations, environmental factors play a role in driving operational success. Consider the importance of wind, water, and solar in supporting green power for smart grids. Environmental intelligence utilizes data from weather reports, satellite images, emergency services, and sensors to help organizations respond to changing conditions that can have either a negative or positive effect on their ongoing operations.

#3: Location intelligence

Having insight into where things are happening is extremely important for any organization with geographically distributed assets, customers, and employees. When a mechanical problem grounds a plane in one city, being able to quickly arrange alternative plans for the delivery of inventory or goods on that plane is critical to avoiding disruptions in service (and profit). Location intelligence helps decision-makers' actions be more precise and efficient.

For instance, stakeholders can use location information to answer the following types of questions: Where was the plane going? Are technical personnel available near the site to make the needed repairs? Are other planes available at or near the location to deliver the shipment? If not, what other transportation options are nearby? Which customers will be affected, where are they, and what are the revenue implications of non-delivery? The list goes on.

#4: Machine intelligence

Using machine intelligence, organizations can automate complex business rules and processes, identify behavioral anomalies, perform "what-if" simulations, and make reliable predictions about behavior of assets under certain conditions. Common questions can include: What is the likelihood of failure of certain assets over the next 10 years? How will costs and service levels be affected if I deploy new, smarter assets over the next two years versus the next five years? Why are all customers in this area reporting high service satisfaction levels except for these 10 customers?

#5: Social intelligence

The rise of social networks has given birth to volumes of data that can be mined to better understand the experiences of customers, prospects, and even individuals who are using competitors' services. Social data can also be a useful tool for capturing insight quickly about what's happening "on the ground." A map tracking social media chatter can be a powerful visualization aid, showing, for example, how many customers in the same neighborhood are tweeting that they don't have electric service.

#6: Business intelligence

Traditional BI tools are designed to slice and dice archived information and churn out pre-defined reports. Although BI by itself may not be able to meet the requirements of dynamic organizations, combining it with the five other categories of intelligence is unquestionably powerful. Imagine, for example, that in addition to looking at "sales by ZIP code," a business user can view a map showing the majority of sales by city, neighborhood, street, or any user-selected geospatial area. Perhaps a business wants to identify the effect of severe storms on sales in particular stores, view the impact of social media initiatives in various cities, or make more accurate predictions about buying patterns at different stores.

Supported by situational intelligence, BI becomes infinitely more useful, giving businesses the insight to be one step ahead instead of one step behind. The best part is that emerging technologies are enabling users to obtain this insight through a single software environment.

From Concept to Action: The California ISO

Connecting all the data dots is the driving force behind situational intelligence, but how does this actually work in the real world? IT departments rarely have the time, resources, or budget to build point-to-point connections among hundreds or thousands of systems, applications, and databases. Manual approaches to data correlation -- such as comparing printed spreadsheets from multiple programs, keeping track of critical details on white boards, or hoping that colleagues with valuable pieces of information will be lucky enough to run into each other in the hall -- are error prone, incomplete, and inefficient. Luckily, advances in visual and geospatial analytic technologies, a key enabler behind the concept of situational intelligence, open the door to a better way.

The California Independent System Operator (ISO) offers a great example of how one organization is using situational intelligence to drive better, faster, and more accurate decision-making. The ISO manages electricity flow across 80 percent of California's power grid, delivering 286 billion kilowatt-hours annually over 25,000 miles of power lines. With so much ground to cover and numerous data inputs to consider, ISO operators needed to spot anomalies on the grid and quickly understand all the nuances of unfolding situations.

Supported by software for situational intelligence, the ISO is now able to combine massive volumes of data from multiple sources -- including weather feeds, sensors, and metering equipment -- into visual displays that operators use to decide how to deploy renewable energy, balance power supply and demand across the grid, and quickly respond to potential crises.

For example, fire and wind trajectory data can be overlaid on a map of the transmission system to spot lines at risk during a wildfire. Visualizations can combine weather feeds and cloud cover data with infrared solar imagery to show the impact of clouds and weather patterns on solar generators.

This visual interpretation and analysis of data is game changing; users can quickly make sense of incoming information rather than waste time skipping back and forth between different information systems and weeding through spreadsheets. Now they can see what's happening, in real time, which is a huge leap ahead.

The beauty of situational intelligence is that it unites multiple disciplines that might already be in place within your organization today -- bringing together the historical knowledge gained through business intelligence with real-time operational, environmental, and location data, as well as incorporate feedback from the customer community. The unique integration of these disciplines enables an unprecedented degree of insight across a business -- 360° of insight.

Rob Schilling is Space-Time Insight's president and CEO, responsible for the company's strategic direction. Rob joined Space-Time Insight from SAP North America where he was SVP and general manager of the western region and responsible for all utility-industry accounts in North America. He previously served as COO for SAP Japan, where his responsibilities included the industry solutions, business operations, inside sales, value engineering, and premier customer network teams. You can contact the author at