Between the Poles

Economic value of big geospatial data could reach $700 billion/yr by 2020

McKinsey Global Institute published a report Big data: The next frontier for innovation, competition, and productivity that analyzes the state of digital data, how it is used in different domains in both the private and public sectors, and offers suggestions for policy makers. In addition to looking at big data as a whole, the report examines five areas in detail including personal location data.

Real-time location data generated by mobile phones and other devices has created an entirely new set of location-based services from navigation to pricing property and casualty insurance. Personal location data is a new horizontal application domain that includes industry sectors from utilities to insurance to transportation. The growing smart phone market is creating an exponentially growing volume of location-aware data.  McKinsey sees this domain as a hotbed of innovation that has the potential to create significant value for consumers.  For example, use of real-time  traffic information for navigation will create a quantifiable consumer benefit by saving time spent traveling and fuel consumption. McKeinsey expects the economic value these innovations create to far exceed the revenue generated by service providers.

Economic value

Mobile phones with GNSS chips have created a revolution will continue over the next  decade. This personal location domain offers the potential for huge new value creation over the next ten years that McKinsey estimates at more than $100 billion/yr (about 0.14% of world GDP) in revenue to service providers and as much as $700 billion/yr (about 1% of world GDP) in value to consumer and business end users. Realizing this value will require investment in technology, infrastructure, and personnel as well as appropriate government action.

McKinsey's detailed analysis of the major applications of personal location data finds that, in ten years’ time, these applications could create value of $100 billion/yr or more for service providers  primarily from sales of navigation hardware and revenue from LBS, mobile LBS premiums, and geo-targeted advertising.

McKinsey estimates that by 2020 the benefits (difference between willingness to pay and the cost of applications) that customers, consumers and businesses, will enjoy from location-based applications will reach as as $700 billion/yr. Most of this, about 70 %, will be the consumer benefit from time and fuel saved by using GNSS navigation systems including those with real-time traffic information and mobile LBS applications. The remaining 30 % of the benefits will be through location-enabled applications such as geo-targeted mobile advertising.  Smart navigation applications are estimated to provide $500 billion in value to global consumers in time and fuel saved by 2020. Geo-targeted advertising could represent more than 5 percent of total global advertising spending by 2020.

For comparison, a recent study prepared for Google by Oxera Consulting Ltd in the UK estimated global revenues from geospatial products and services to be $150-$270 billion per year.  The geospatial industry is estimated to be growing by 13% per year through 2016.  These numbers were estimated using a different methodology to McKinsey's. The lower estimate of $150 billion/year was estimated using a bottom-up approach based on the Bloomberg BICS classification of companies worldwide and assumptions about allocating the revenue from companies that provide geospatial in addition to other services such as Trimble.  The $270 billion number was estimated by scaling up the US revenue estimated by Boston Consulting Group to come up with a world estimate.

Personal location data

McKinsey includes sources of personal location data that locate an individual to at least within a couple of city blocks in an urban settingin its analysis.

• Credit and debit card payments from cards processed at point-of-sale (POS) terminals and  automated teller machines. In 2008, there were 90 billion to 100 billion transactions of this type worldwide.
• Mobile phones - the location of such devices can be triangulated from cell-tower signals.  In 2011 there were about 6 billion of these.
• Smartphones with GPS - about 600 million devices in 2010 and the number is growing at about 20 % per year. Many smartphones also have Wi-Fi networking capability, which can be linked to a physical location of a Wi-Fi network.

In the future McKinsey expects indoor location to become more important.  They mention Shopkick, which allows merchants to track their customers from when they walk into a store, and Path Intelligence, which can track foot traffic within malls or amusement parks.

McKInsey's research estimates that the global pool of personal location data was at least 1 petabyte in 2009 and that this pool is growing by about 20 % a year. 

Location data generated by smartphones is growing at double the average rate of growth for all location data.  Asia is the leading region for the generation of personal location data because so many mobile phones are in use there. In 2010 the regions with the largest number of cell phone users were

• China 800 million
• India 650 million
• North America 300 million.

Applications of personal location data

McKInsey has identified three major categories of applications of personal location data.

• Location-based applications and services for individuals including smart routing, automotive telematics, and mobile-phone based location services
• Organizational use of individual personal location data that includes geo-targeted advertising, electronic toll collection, insurance pricing, and emergency response.
• Macro-level use of aggregate location data that includes urban planning and retail business intelligence.

Barriers to use of personal location data

Concerns about privacy and security was identified by McKinsey as a major barrier to future expansion of the use of personal location data. Laws are unclear about who owns that data and how it can be used.  McKinsey believes that a policy and legal framework that clearly describes the permissible and prohibited use of these data would be beneficial for all stakeholders.

For policy makers, McKinsey sees that a priority is incentives for innovation are in place including developing an up-to-date framework for intellectual property rules and rights, funding R&D in potential breakthrough areas, and ensuring that the infrastructure, including spectrum policies, is optimal.

Methodology

By looking at the value chain of location-based applications, McKinsey examined both the amount of data generation and the potential to create value.  McKInsey estimated the amount of personal location data generated in each category of application orsource device and in different geographic regions using the installed base of devices, usage behavior, and the frequency with which data are generated. To assess the potential value that data can generate, McKinsey estimated the economic value of each application to end users—individual consumers, enterprises, and government organizations.

Location-enabled Society: Mapping tweets using real-time big data technology and privacy

In a very appropriately titled session Taking Society's Pulse in Real-time at the Creating the Policy and Legal Framework for a Location-enabled Society conference in Boston, one item really got people's attention focussed on the issues that arise from combining location with personal data. TweetMap is a technology that visualizes a large number of tweets on a map of the world. This is not the first time I have seen tweets mapped.  But what really got people's attention is that a speaker showed how to drill down to your neighbourhood and see what the people next door are tweeting about.  Of course you can do this without a map by simply following your neighbors on Twitter.  But somehow mapping it makes it seem more intrusive, even invasive to some people. In another demonstration, Tweetmap was used to show how a person sailing in the Caribbean could be tracked as they tweeted from their boat.

To be tracked geospatially on Twitter requires that you explicitly agree to geolocation tracking for Twitter on your iPhone or Android device.  For example, on an iPhone the geolocation option allows you to make your location known to others. Apps such as Facebook or Twitter for iPhone can report your location as well as the messages you post.  The iPhone requires that these applications obtain your permission before they can be enabled.  Apparently only about 2-4% of smart phone users, mostly younger folks, agree to this.  After seeing a Tweetmap drill down, I would expect that some people might have second thoughts about agreeing to the gelocation option. And in the not too distant future this will be possible in real-time on inexpensive hardware with open souce software.

TweetMap is an instance of MapD, a massively parallel database platform being developed through a collaboration between Todd Mostak, (a researcher at MIT), and the Harvard Center for Geographic Analysis (CGA).  Currently the tweet database starts on 12/10/2012 and ends on 12/31/2012 and contains 125 million tweets.  The database can be queried by time, space, and keyword.  The ultimate objective is to be able to query billions of tweets live in real-time.  In this case real-time means from tweet to tweet on a map in under a second.

MapD is a general purpose SQL database that can be used to provide real-time visualization and analysis of very large data sets.  MapD uses commodity Graphic Processing Units (GPUs) to parallelize compute intensive tasks such as querying and rendering very large data sets on-the-fly.  MapD runs on inexpensive hardware ( ~$1000) and is intended to be open source.

SPAR 2013: Integrated geospatially-enabled asset management at the Utah Department of Transportation

The Utah Department of Transportation (UDOT) is responsible for about $30 billion in assets including pavement, bridges, signs, culverts and signals.  At SPAR 2013  Stan Burns, Asset Management Director at UDOT gave an overview of a very ambitious program involving inventorying all assets and developing an integrated gespatially-enabled asset management  system. 

UDOT has about 6000 centre-line miles (10 000 km) of highways and has just completed an inventory of the entire system.  The inventory generatd a total of 30 terabytes of data, 6 terabytes of imagery and 24 terabytes of point cloud data captured by LiDAR.

UDOT is in the process of developing applications that are designed to provide a common interface for the dozen or so UDOT groups that provide a variety of services.  One of the most important features of the architecture of the integrated asset management solution that UDOT is developing is that location represents a fundamental way of indexing and integrating disparate data sources.  For example, it makes it possible to see critical maintenenance issues in different layers such as culverts, pavement, and bridges on a common landbase map, so that repairs can be coordinated for all of these assets at the same time. 

A critically important objective is no data duplication by implementing a data management stategy based on data stewardship,  In essence every piece of data has a single owner, which could be a group or department.

Interoperability is a critical issue because UDOT has many different software systems from different vendors including Oracle, Bentley, ESRI, Deighton, Mandli, Virtual Geomatics and others.

But perhaps the most important aspect of this project is the sharing of a common dataset among all the departments of the UDOT.  As Gene Roe of LiDAR News said in another session, sharing of data among different departments of DOTs is not the current situation in many DOTs.

This type of asset management represents a cultural shift for DOTs, because it makes it possible to maintain assets proactively, develop strategic directions, and create wisdom from the huge volume of asset and other data such as historical weather conditions.  For example, it makes it possible to predict future bridge and pavement conditions, so that funding requirements can be projected into the future.

IEEE PES Smart Grid Technologies: The next challenges in grid modernization

At the IEEE PES Conference on Innovative Smart Grid Technologies in Washington DC,  Patricia Hoffman, Asst Secretary Department of Energy, Office of Electricity Delivery and Energy Reliability, gave the  keynote address. 

The whole is greater than the sum of its parts

In the past the focus for the Department of Energy and for utiltiies has tended to be on individual technologies such as smart meters and AMI, self-healng networks, synchrophasars, substation automation, and so on.  Ms Hoffman's message is that we need to start focussing on the smart grid as a whole and in particular we need to focus on grid resiliency.  Things will happen, floods, tornadoes, hurricanes, derechos and other natural and man-made problems.  We need to ensure that the grid is resilient so that we can recover from disasters quickly.  The example she gacve was one that I have blogged about,  Southern Co.'s use of  smart meters to track outages caused by tornadoes in April, 2011 and then help with restoration. 

Ms Hoffman then discussed specific areas where there are opportunities to make the grid more resilient.

Transmission operations

There is a lot of data that is becoming available, primarily because about 1000 synchrophasars have been deployed over the national transmission grids.  What are needed are improved interoperability, analytic, and visualization to better manage the transmission grid.  For example, it took almost a year to analyze the data and determine the cause of the 2003 Northeast blackout.  The 2011 San Diego blackout  required about 3-4 months, so fault analysis is getting better, but we need to be able to use the available data to predict the state of the transmission grid in real-time.

Distribution operations

In the area of electricity distribution, Ms Hoffman singled out peak load reduction as the area where there are the greatest econimic and environmental benefits.  Reducing peak load means that the construction of new power plants (peakers) that may only be used a hundred hours a year can be avoided.  The other areas she identified include distribution automation, especially automated self-healing networks using devices such as intelliRupters,  improved outage management, using predictive analytics to replace equipment before it fails, and microgrids.

Customer empowerment

Areas that directly impact the customer are residential diagnostics to help the customer understand how power is being used in the home and ways that it can be used more effectively, at lower cost, and as much as possible moved to off-peak.  Photovoltaic cells and plug-in electric vehicles are becoming much more prevalent and utlities need to be prepared for these.  Improving reliability and resiliency directly impacts customers by reducing the number and duration of outages.  An area that is getting a lot attention is rate structures, for example, pricing electric power to motivate shifting load from peak to off peak.  A new technology that is being investigated by DoE is transactive control signals.  This is a  smart grid signaling approach that communicates prices and other information, such as expected future prices, to encourage the customer to change his/her electric power usage.

Energy infrastructure cyber protection

Ms Hoffman's message on cyber protection is that it needs to be dynamic and multi-dimensional.  Infrastructure is complex and changing and new vulnerabilities are detected every day.  In addition multiple organizations are responsible for the grid.  Effective cyber security management has to deal with a dynamic landscape of control and information systems.  There are a broad range of threats and motivations including unintentional, natural phenomena, and those with malicious intent including theft, revenge, and national security.  It is also important to recognize the interdependencies among critical infrastructure.  A gas explosion can affect the telecommunications and electric power networks.  Loss of electric power can affect many other networks including water and telecommunications.  Disruption of communications, for example, severing a fiber optic cable can directly affect the electric power grid.

To address these challenges cybersecurity solutions need to be mutli-dimensional.including cyber-physical, wide area monitoring and protection, cryptography, anti-tamper devices and advanced architectures. 

One of the most serious challenges that Ms Hoffman singled out specifically is developing the cybersecurity workforce that is going to be responsible for implementing these measures.

Where are we headed ?

The next major challenge is turning the smart grid into a driver of economic development.  The example Ms. Hoffman used was Chatanooga.  The Electric Power Board (EPB) saw the smart grid as not only benefiting the utility and improving the quality of life of its cusomers, but also as a driver of economic development.  The city’s decision to deploy one of the country’s highest capacity fiber-optic networks not only improved power quality, reliability, customer service and energy efficiency but also made Chananooga more attractive to business.  For instance, because bandwidth is virtually unlimited, EPB is able to offer its customers Internet upload and download speeds of up to one gigabit/sec, making Chattanooga very attractive for businesses that rely on the internet for critical aspects of their operations.

NRECA TechAdvantage: Modeling is the key to grid resilience

The National Rural Electric Cooperative Association (NRECA) is comprised of more than 900 electric coops in the U.S.  This is more than just an organization representing electric coops at the national level.  It also supports a very active technology research arm called the Cooperative Research Network (CRN) that supports collaborative research with the Department of Energy, Department of Agriculture, National Labs and others.

At the NRECA TechAdvantage conderence in New Orleans today, Dr Craig Miller, Project Manager at CRN gave a scintillating and inspiring presentation on the future of the U.S. power grid over the next two decades.

Four years ago when DoE received a large amount of ARRA funding, the focus was on the smart grid and the technology required to support it.  Now every utility has done something using smart grid technology.  The new focus is on resiliency, how to make the grid agile.  This is a lot more complicated today with our full quiver of smart devices. 

The foundation of the smart grid are sensors.  All electrical devices these days are smart and are able to report their status ranging from several times a microsecond to every 15 minutes.  The result is that utilities are faced with 100'000 times as much data as they've dealt with in the past.  The challenge is the analytics to make sense of all this data.

In the old days, when you had a fault, you dispatched a crew and a few hours or days later, the fault was fixed and people got power again.  Now the network can be reconfigured in subseconds.  But there are a lot more things to worry about.  When you reconfigure the network, you have to worry about whether the new configuration can support the load.  If it can't there a number of options you have that weren't available with the old grid.  You may be able to tap battery storage, autpmatically startup backup generators at industrial customers and take them off the grid, disconnect less critical customers, automatically shut down some devices for customers on demand response programs, reduce voltage, or even isolate some parts of the grid, so-called microgrids.  With the new grid, there are tools to manage load, generation, and storage dynamically, and since the grid is a balancing act, all of this has to happen at subsecond speeds.

Modeiing the power grid is the key to the engineering challenge of the 21st Century

Dr Miller's most important point is that the key to all of this is models of the power grid.  Being able to model the  network and predict what it will do faster than the network itself is seen as the key to the next generation "agile" grid, which Dr Miller predicts will be the greatest engineering achievement of the 21st C, much as the old grid was called the greatest engineering achievement of the 20th C.  Dr Miller pulled no punches when he said that our current models of the grid are all bad.  They are only able to be used for planning purposes, not for operations. 

Modeling the grid is a very complex problem.  Even our existing "simple" models are incredibly complex.  This problem will require the best of our current super computers, and will probably require another decade for this level of computing power to become affordable for utilities.  For example, a project that CRN is working on with DoE to improve modeling of the electric grid will use Titan, at 21 petaflops, the fastest super computer in the world today.  This is indeed a "challenge to light up our lives", especially the lives of the next generation of electric power engineers.

Why are utilities implementing analytics programs and how are they doing it ?

At  the Data Analytics for Utilities conference in Toronto yesterday, several speakers explained what is driving utilities to invest in analytics programs, and gave some real world examples of how utilities are implementing analytics programs.

Why analytics ?

In a nutshell, as a number of speakers emphasized, the goal is to enable utilities to make decisions based on information derived from the increasing volume of reliable data that is available to utilities as a result of their smart grid activities, rather than "gut" intuition, which is what utilities had to rely on in the past because they had limited and out-of-date data.

Lloyd Tokerud of Hudson Energy referenced a seminal paper, an MIT Sloan Management Review "Big Data, Analytics, and the Path from Insight to Value" that found that top performing organizations are twice as likely to apply analytics to activities. 

The study also found that the biggest challenges in adopting analytics are managerial and cultural.  Lloyd pointed out that to enable an analytics prorgam to be successful, it is necessary to educate people on the use of data and to promote a data-driven culture rather than intuition.  The important benefit is that an anlytics program can enable the utility to make better decisions faster.

Lloyd also said that analytics requires people with different skills than traditional IT.   He called them  "purple people".  Purple people understand both IT (red) and the business (blue).

Guidelines for implementing an analytics program

Mary Rich, who was responsible for smart grid and analytics initiatives at Centerpoint Energy in Texas gave a very insightful overview of how Centerpoint implemented their first anaytics program subsequent to their rollout of smart meters and AMI.

Based on her experience at Centrepoint she made several points that are important for any utility planning to embark on an analytics program, 

First of all, implementing an analytics initiaive is not a departmental exercise, it has to be company-wide.  It needs top-level support and strong governance.  Without these it will either fail or drag on for years.  Seondly it is not cheap.  It requires new tools and new people.  The big payback will not be short term, but long term.  Creating a data-driven culture is a cultural transformation that requires breaking down traditional data silos in engineering, operations, GIS, and across the company. 

One of the most important objectives is ensuring that the data is accurate and comprehensive.  Most importantly there can be only one authoritative source for each piece of data.  Conflicting reports are not going to help management make decisions rapidly and even worse they will raise red flags with the regulator.

How did Centerpoint Energy implement an analytics program

At Centerpoint they created a team comprised of subject matter experts (SMEs) from all divisions of the company.  It was ensured that these people were relieved of some of their normal committments so they could devote quality time to the analytics initiative.

The team spent about a month and a half identifying areas which potentially could benefit significantly from the application of analytics.  They then winnowed these down to the top 25.

For each of these opportunities they wrote detailed use cases,  Mary emphasized that this was not easy.  It was time-consuming and often required external support people to help SMEs who were not familiar with writing use cases.

As a practical example of implementing a use case, ons of the uses cases related to what Centerpoint refers to as "diversion" and what others call non-revenue-generating, non-technical losses.  With analog meters Centerpoint had to rely on the meter readers to note signs of tamperring with a meter and to report a possible diversion problem to Centerpoint.  A crew would roll a truck and investigate,but in many cases it would turn out not to be a diversion probem.  Wth smart meters meter tampering can be detected auomatically., but it is an involved process because it is necessary to make sure that there isn't an outage or a crew working in the area, because these can trigger events that look like meter tampering.  The SME team working on the use case estimated that by using autmated techniques they could increase detection by 20%.

The reality was quite different. They found that their autmated algorithms were much more effective than expected, because they were able to identify real diversion events with 80% accuracy.  They found that the big business benefit was fewer truck rolls.  Mary strongly recommended that every utility should know the cost of a truck roll because reducing truck rolls is one of the most important areas where utiltiies can reduce costs, often dramatically.  Mary said that in Houston alone, they were able to eliminate 4.5 million truck rolls.

Big data and trends in data analytics for utilities

I had the opportunity to catch up on current trends in analytics in the utility indusrty at  the Data Analytics for Utilities conference in Toronto today.

Jane Allen and Tom Peters from Deloitte gave a high level overview of what utilities are experiencing as they move toward the smart grid and decarbonizing energy generation through intermittent renewable energy sources.  

Big data

The smart grid means intelligent devices that can report their status sometimes as frequently as many times per second.  Smart meters, which are many utilties' first experience with intelligent devices, typically capture electricity usage in 15 minute intervals, which for a utility wih a million meters translates into 3.2 terabytes of data. They may be required by law or by the regulator to maintain historical usage data on-line for a significant period of time.  The result is that the data volumes that utilities need to manage are going up exponentially.  For example, Oncor, the largest distribution and transmission utility in Texas said that their operational database which used to be 30 terabytes is now 300 terabytes.

Analytics

To make sense of the huge volumes of data that utilities are collecting requires specialized analytical tools that are able to handle large data volumes.  The first wave of analytical tools were not designed to do this in real time or even near real-time, but the next wave will be able to.

Apparently Germany was the first country to use analytics to help sculpt electric power demand to the supply of intermittent energy from wind and solar.  This is critical in Germnay because renewables contribute a greater proportion of the electric power in Germany than any other major country.

Deloitte sees five main areas where analytics are being applied in the utility industry, customer, supply chain, financial, workforce and risk. 

Tom Peters oulined some of the trending application areas where analytics are being used by utilities. 

Pricing analytics

Segmenting demand using demographcs and looking at dynamic pricing to manage peak demand (demand response) in different sectors.

Marketing anlaytics

Monitoring the response to web-based marketing campaigns in real time.

Customer service analytics

Analyzing unstructured text data in social media looking for "bags of words" that identify topics in on-line discussions to monitor and assess how the on-line community is responding to utility programs and initiaitves.

Workplace injury analytics

Using analytics to identify high-cost work-related injuries that are preventable.

Fraud analytics

For example, analyzing a company's general ledger for an entire year looking for duplicate invoices, invoices sent to the wrong company, billings for products and services never delivered, and so on.  Tom Peters said that in the case of one large company this saved $11 million in a year.

Geospatial analytics

Using location information to look at big data in a different way from a traditional spreadsheet makes it possible to see things from a much more interesting and enlightening perspective.

In summary utilities are having to deal with a lot more data.  They need analytics to winnow the wheat from the chaff, to turn huge volumes of data into actionable information.  The good news is that the software analytical tools available now that are designed specifically for big data are getting better and more affordable.

Data volumes in the utility sector projected to exceed telecom

At Gridweek last year, a speaker from Alabama Power, which among other intelligent devices has deployed over a million smart meters, said that he is seeing a 10,000X growth in the volume of data they have to deal with.

The new grid will be part of the "internet of things".  At the Soft Grid Conference this week, it was reported that a speaker from Proximetry estimated that there are currently 10 billion assets connected to the grid.  Many of these assets are smart devices that can report every microsecond, like a synchrophasor, or very 15 minutes like a smart meter. This adds up to a lot of data, much of it real-time.

Although the volume of data that utilities are currently handling is not very large compared to the data volumes in retail, finance and telecom companies, It was reported that Oracle's utility group has estimated that the data flow in utilities will be "1.5 to 2 times higher than the traditional communications industry".  That includes data coming from intelligent devices such as smart meters, synchrophasors, smart transformers, many devices in smart substations, and other intelligent devices and sensors.  Reportedly PG&E with millions of customers is planning on 4 petabytes data growth per year.

Not only do utility smart grid systems have to manage vastly increased volumes. but also have to process much of it real-time.  For example, not only monitoring transformer status (load and temperature) in real-time, but also provide analytics to the operator of the impact of the overloading and allow the operator to reconfigure the grid to reduce the load on overloaded transformers by redistributing load to others with available capacity.

Burlington Hydro's real-time smart grid for operations and analytics: Managing big data real-time

At the Oracle Spatial User Conference in Washington DC this week, Don Guatto, COO and VP Engineering at Burlington Hydro and Geoff Cameron, EVP Intelligent Networks at AGSI gave a rivteing presentation on Burlington Hydro's (BHI) smart grid implementaton for the utility's GridSmartCity initiatives.

BHI is a utility near Toronto with 65000 meters, 32 substations and 93 employees.  The GridSmartCity initiatives include

• self healing networks - this initiative has reduced average outage times in BHI's service area to 20 seconds per year
• smart meters - Burlington is in Ontario which has deployed smart meters and time-of-use pricing province wide
• distributed generation - Ontario has a Feed-in-Tariff program which has led to the deployment of over 7000 micro-generation projects, mostly solar
• electric vehicle (EV) charging stations
• factory ride-through systems - enabling factories to continue functioning through outages
• battery-based electric storage

Smart grid management system for operations and analytics

An independent consultant hired by BHI made a recommendation with far reaching implications for BHI. "Two of the main factors affecting GIS today are the move towards using internet technology to be able  to serve GIS data to a much broader user base and efforts by the Open Geospatial Consortium (OGC) to promote interoperability.  BHI needs to implement a new GIS that will fit its technology vision and provide the benefits of today's GIS to BHI operations."

Based on this recommendation BHI determined that the foundation for their smart grid management system would be an open architecture based on open standards.  This would allow them to integrate their SCADA system, 65 000 smart meters reporting power use every 15 minutes, the supporting automated meter infrastructure (AMI), intelligent devices, power line sensors, a customer information system (CIS), their ERP system, engineering analysis, and other systems.  It would utilize bidirectional communications to both receive information from and control smart devices. The system would have handle much larger data volumes than BHI had ever experienced before.  For example, some devices were reporting 60 times per second.  And perhaps the most challenging requirement is that it also had to operate real-time, which as Geoff emphasized, is beyond the capability of today's traditional enterprise GIS systems.

Implementation

AGSI's solution uses their Go360 products which provide a scalable, geospatially-enabled solution built on an open service oriented web architecture. It uses Oracle Spatial for a shared, geospatia data repository and AutoCAD Map 3D for design and geospatial data maintenance.  A web browser, either on the desktop or on a mobile device, is all that's required to access tools for asset maintenance, cable locate, asset managemet, operations, financials and drill downs, automating network pinning and work protection tagging, SCADA, schematic views, automated CAIDI, SAIDI and SAIFI reporting, an outage management system (OMS), mobile workforce automation, automated as-built management, real-time asset monitoring and analytics, and an executive dashboard.

One of the most impressive Go360 applications that Geoff demonstrated was a transformer status monitoring dashboard that not only showed a map with transformer loading in real- in the form of a heat map, but could also report on historical loading and even estimate, based on the history of oveloading on a particuler transformer, how much the lifetime of the transformer had been shortened as a result of the overloading.  It also allowed the operator to reconfigure the grid in real time to reduce the load on overlloaded transformers and redistribute it to others with available capacity.

What impressed me most was to see how an open IT architecture based on open standards together with industry standard off-the-shelf components, a bidirectional communications network and real-time smart devices and sensors has enabled BHI to develop a state of the art real-time geospatially-enabled smart grid operations and management system that integrates with their existing enterprise systems and provides a common point of access to all their operational data. 

BHI has plans to contnue to develop smart grud applications in the future, but I think that they have built a strong IT foundation for their future smart grid development.

Interesting predictions for the geospatial sector in 2012

There are some interesting predictions for the geospatial sector that I've come across that identify some important trends for 2012 (not in any particular order). 

• The next great GIS isn’t a “GIS”–but rather...and here Brian Timoney goes on to talk about analytic and visualization tools that we will need in 2012 to deal with the "tides of Big Data, the sensor web, and the Internet of Things”.

Brian's predictions reminded me of the talk that Robert W. Burkhardt, the Army Geospatial Information Officer (GIO), gave at a DLT event in June of 2010 in which he said that for the Army, at that time with access to more and better data sources than ther rest of us, in the past the lack of data was a problem, now the challenge is increasingly being able to integrate and analyze in order to extract information from the huge volumes of data that are now available.  His words were "extracting actionable information by exposing patterns from the huge, chaotic volume of data pouring out of many stovepipes." 

• Open source geospatial - Joe Francica predicts that open source gets its act together, specifically, Joe says that the "Open Source Geospatial Foundation (OSGeo) needs to transition itself from a movement of programmers to a cohesive group of businesses that can leverage the movement."  Joe predicts that "open source companies [will] step out of the shadows and become a marketing force. Either OSGeo takes the lead in supporting the group or individual companies will seek other professional support."

Adena Schutzberg in her predictions goes a step further and predicts that "OpenGeo will do the hard work of building credibility (and support) for enterprise use of open source. It will do for open source GIS what Esri did for GIS. The company has and will continue to take the baton from OSGeo to get the software into wider spread use. (And it will make money doing so.)"

By way of background the open source geospatial movement started coalescing around 2005, the OSGeo was founded in 2006, and the movement and OSGeo have grown rapidly since then.  The annual OSGeo event, FOSS4G has been happening in various parts of the world since then.  Last year in Denver FOSS4G attracted about 900 attendees.

• Enterprise Geospatial - Joe Francica mentions three specific areas, cloud, big data analytics (echoing Brian Timoney's prediction), and social business and mobile computing.

Cloud: Joe says that geospatial companies need to offer solution-based cloud services.  "Google has Google Earth Builder; Esri has ArcGIS.com and the others better get on the band wagon or die." He also sees geospatial data and software being offered by integrated companies, for example, "GeoEye buying a company like PCI Geomatics or DigitalGlobe buying or merging with Exelis VIS."  Adena Schutzberg predicts that in 2012 the biggest impact that cloud computing will have will be on education.  "Educators can and will teach introductory courses using 100% Web resources by the end of 2012."

Big data analytics – Joe predicts that "location intelligence will move in this direction and lead big data analytics."

Social Business & Mobile Computing – Joe sees "a movement toward location-based business (LBB): taking all the aspects of Internet socialization and combining this with the mobile ecosphere.  Salesforce.com is doing it and you see just about every enterprise software company coming out with some form of mobile business application. The element of location-based business will be huge."