Situational Intelligence Makes Electric Vehicles More Predictable


A recent article in Cleantechnica lists some staggering annual growth rates for electric vehicles in 2014: 45 percent growth in Japan, 69 percent in the United States and 120 percent growth in China. Fifteen new models of electric vehicles are slated for introduction this year, according to EVObession.

Even with these numbers, the overall population of electric vehicles remains relatively small: 100,000 thousand in China, 110,000 in Japan and 290,000 in the U.S. As this map of California shows, electric vehicle adoption varies greatly from county to county, even within the U.S. state with the highest rate of adoption.

Given this rapid but uneven adoption of power-hungry electric cars, how can utilities adapt? It’s difficult to predict where and when cars will need to charge, and how much charging they will need. Until penetration rises and becomes more even, it’s difficult to justify investing in widespread infrastructure upgrades to support charging. Can making smarter use of existing infrastructure meet charging needs for now and the near future? Because electric vehicle adoption occurs in pockets of concentration, utilities face the risk of multiple charging vehicles overloading a portion of the grid and causing localized brownouts or blackouts.

Situational intelligence offers analytics and visualization across space, time and node, providing an ideal approach for integrating electric vehicles into the existing power infrastructure:

  • Spatial analysis shows which households have electric vehicles or are likely to acquire one, where vehicles have traveled and are likely to travel, and where drivers typically recharge their cars. It also shows the location of chargers. When placed on a map of utility assets grade by reliability, this can pinpoint places where vehicle charging is likely to cause problems.
  • Temporal analysis shows when electric vehicles are likely to charge in relation to forecasted supply and prices of energy. This helps drivers and utilities match the demand of charging with available supply.
  • Nodal analysis shows frequently used chargers and optimal locations for new chargers. It could also show drivers routes that are estimated to consume less power and thus extend vehicle range.

For example, electric vehicle maker Tesla Motors recently announced a software upgrade that includes this type of situational intelligence. According to a Business Insider article, with the update, “a [Tesla] Model S will know where it is, where the closest Supercharger is at all times, and how much battery charge it has remaining, as well as how far it has to go to a given destination, if that information is available.”

As the population of electric vehicle grows, drivers and electricity providers will benefits from increased analytics to ensure smooth driving.



Invite Situational Intelligence to the Board Room


IBM says that it’s time to invite data scientists to the board room Fortune recently declared “The Algorithmic CEO.” A UK financial company, Deep Knowledge Ventures, has appointed an algorithm to its board of directors.

What is happening with analytics at the corporate board level?

According to a 2013 survey by Tata Consulting, three consistent challenges that executives face in realizing ROI from Big Data projects are

  • “Getting business units to share information across organizational silos”
  • “Building high levels of trust between the data scientists who present insights on Big Data and the functional managers”
  • “Determining what data to use for different business decisions”

These challenges have little or nothing to do with technology.

The first two are issues of organizational culture. The last pertains to business strategy.  Organizational culture and business strategy are squarely the purview of corporate officers and directors.

One could fault corporate officers and directors for being behind the Big Data curve. It’s not like this stuff just happened in the past two months.

What is new is the awareness of potential competitive advantage by applying analytics across silos of IT, operational, and external data, not just within silos. Corporate officers and directors (should) work above individual data silos, looking for advantage. They have not had powerful, easy tools for analytics across silos–until the advent of situational intelligence solutions.

A defining characteristic of situational intelligence solutions is the ability to access and correlate multiple sources of IT, operational, and external data into a single platform for analysis. Related to that is the ability to visualize the results of analysis for multiple types of users on multiple devices.

Thus, situational intelligence solutions are useful board-level tools for sharing information across silos, building trust across the organization and collaborating on what data can support the best business decisions. Maybe it’s time to invite situational intelligence to the board room.


Why Geospatial?


“80 percent of business data has a geographic component.”

This meme has been hanging around the World Wide Web since its inception. Does it represent reality? Who knows. More important, does it represent a useful concept?

As stated in a 2013 report from Deloitte, “a place is no longer simply a point on a map or a political jurisdiction, but a living, evolving hub of information, a convergence of digital and physical worlds.” This is why geospatial views have such prominence in situational intelligence—place provides more subtle, rich, and even sub-conscious information to aid our interpretation of data than tables, charts and dashboards, detached from the locale they reference, can provide.

Placing information in a landscape or familiar environment allows multiple senses to feed our intuition about what the data means and what we should do about it. Badwater Basin, in Death Valley, and Mt. Whitney, in the Sierra Nevada, are at roughly the same latitude and about 85 miles apart as the crow flies. With only that information at hand, you might expect similar data readings about how equipment installed at these locations is operating. On the other hand, if you’ve ever been to those locations or have a topographical map available, you can see that you’re comparing data from the lowest and highest points in the Continental U.S. With these two locales, wildly different readings are within the range of normal, and an artifact of place, rather than an indication of trouble.

In addition to providing more context, geographical placement can provide powerful cues that help us retain and share information. People who memorize seemingly superhuman amounts of information use a technique called a “memory palace.” They select a familiar physical environment that they can easily visualize, then associate the information to memorize with locations and objects within that visualized environment. When they want to recall information, they can bring up the associated location or object, or simply take an imaginary stroll through their memory palace to locate the information.

Geography functions like a memory palace for situational intelligence—a well-known physical environment to which people can connect information. Mention a notable site, such as the overlook at Niagara Falls, and everyone who has been there has immediate access to information such as the ambient noise, the dampness, the size of the parking lot, and whether there’s anything decent to eat nearby.

The difference is, with situational intelligence, the geospatial display can become a memory palace that’s shared among dozens of users in multiple parts of an organization.

I don’t know if 80 percent, or even a majority, of data has a geographic component. I do know that associating data with place whenever possible provides users with much richer and more useful insight.


IoT Day, 2015


IoTDay logo 2015

Break out your smart watches and connected toothbrushes–April 9, 2015 is the fifth annual IoT Day.

Visit the website for a map to events around the global.  You can also submit and vote for projects that you think the IoT community should tackle.

To learn more about how situational intelligence brings value to the Internet of Things, read the recent post, “Situational Intelligence Was Made for the Internet of Things.”

IoT Day is sponsored by the Internet of Things Council and Postscapes.


Situational Intelligence and Smart Cities: Energy, Part 2


In a previous post, I wrote about how progressively sophisticated uses of situational intelligence for energy can help a smart city evolve. Energy is the enabling technology for a smart city, because it powers the Internet of Things. All those sensors, mobile devices, smart street lights, programmable signs, electric vehicles and other powered devices, plus the computers and communication networks that gather, store, analyze and visualize the resulting streams of data, require a steady supply of electricity to operate.

Because it is the bedrock for the smart city, energy needs to be affordable, resilient and sustainable. These three qualities help ensure a steady supply of power for operating the Internet of Things. How does situational intelligence make energy more affordable, resilient and sustainable?

  • Affordable energy
    To keep energy affordable, Situational intelligence applications can optimize capital planning for cities and utilities, as I also discussed in the previous post. Capital efficiency helps control the price of power generation and distribution. By forecasting energy supply and demand based on multiple correlated data sources, situational intelligence applications help utilities meet demand through demand response, instead of spending money to purchase extra power on the open market or to build new power generation plants. Avoiding these costs keeps energy affordable. Marketing and customer service professionals use situational intelligence applications to analyze customer profiles and usage and develop optimal pricing and packaging approaches. Paying just for the energy services you need and use helps keep power bills small.
  • Resilient energy
    Situational intelligence applications provide spatial-temporal-nodal analysis to determine risk levels in individual assets on the grid, a precursor to lowering risk and thus raising resilience. To make the grid as a whole more resilient, situational intelligence applications help city and utility officials forecast the impact of crisis events so that communities can better prepare for and respond to severe weather and natural disasters.
  • Sustainable energy
    Grid planners, energy dispatchers, power marketers and others use situational intelligence applications to integrate sustainable, renewable sources like solar, wind, geothermal, biomass and tidal into the generation mix. Situational intelligence applications also power the analytics for integrating grids-scale storage and demand response into grid operations.

With affordable, resilient and sustainable energy, the Smart City can evolve beyond today’s small minority of innovative city departments and wealthy neighborhoods as sensor and communication networks become pervasive.

Where have you seen the Internet of Things spreading in your community?


Situational Intelligence Was Made for the Internet of Things


The growing ease of adding sensors, microchips and communications modules to nearly any object is spawning the Internet of Things. Gartner predicts that by 2020, there will be 25 billion connected devices on the planet. What kind of things? Watches, clothes, appliances, cars, industrial equipment and more, including some products and applications we haven’t even envisioned yet.

These billions of connected devices have the power to remake our world.

One recent example is a prototype toy dinosaur that connects to IBM Watson, the machine learning system that has defeated world chess champions and won the TV gameshow “Jeopardy!” The dinosaur can converse with children and answer questions, pose puzzles, assess learning and develop a type of personality based on a child’s likes and dislikes. The powerful combination of a child’s imagination and a supercomputer can remake education and even shape that child’s sense of what is possible.

Each new type of thing added to the universe of connected systems generates its own silo and/or stream of data, either structured or unstructured. To realize any value from that new data, analytics is needed to extract meaning from it.

In fact, the power of the Internet of Things isn’t just that isolated devices can now communicate. The power is that, as a result of the devices being connected, new levels of insight into everyday transactions, events and processes can be achieved.

To enable insight into the ecosystem of “things,” a layer of intelligence that spans the silos and streams of data from individual sensors is needed. That’s where situational intelligence comes in.

We’re already seeing situational intelligence for connected devices remaking the world of utilities. The data from smart meters, communication networks, fault detectors, SCADA systems, video feeds, social media, lightning sensors, weather satellites, GPS locators and many other devices is being correlated, analyzed and visualized to provide unprecedented insight to utility professionals. That insight helps them serve their customers better, save money, reduce carbon pollution, and keep the power grid secure and resilient.

We’re also seeing situational intelligence transforming the supply chain. Sure, we’ve been able to track our packages for a while now, and after a storm delays a flight, we’ll be informed that a package may arrive late. But by using situational intelligence, shippers are now able to anticipate weather delays, and reconfigure their network routing on-the-fly to meet delivery commitments despite the weather.

Increasingly, we’ll start to see situational intelligence spread throughout the economy to industrial processes, retail interactions, government services, telecom, healthcare and more. The possibilities are nearly infinite.

The Internet of Things has the power to remake the world, but much of that power will remain untapped without situational intelligence to span disparate streams of data and generate new insights. Situational intelligence was made for the Internet of Things.

I’ll be writing more in this blog about how situational intelligence helps us derive new levels of insight from the Internet of Things. Stay tuned.