What Communication Service Providers need from Location Intelligence - Alex MacKay, CEO

As I thought about Twitter Places in last week's post and what it signalled, I immediately began to think about the concept of "big data" which David Sonnen, IDC Senior Analyst, raised in our blog post of June 2nd. The use of a lot of data to help make decisions is at the heart of location intelligence (LI). LI by its very nature, creates extremely large datasets. When you think about Twitter Places, it's the beginning of a form of the digital data steam rising off the planet that David referred to on June 2nd. When you also realize that all Twitter data effective back to 2006 is going to be digitally stored in the USA Library of Congress you realize just how important this information is and what the knowledge/intelligence is that will be available. The ability to analyze this type of big data and make smart business decisions from seeing the patterns and gaining the intelligence is going to be yet another race for those who want to compete in location intelligence. At DMTI we see this big data knowledge being served up within the context of different vertical industries. What's important to insurance companies (property specific information) may be quite different from the information required for a Communications Service Provider (CSP). For CSP's they use location intelligence for two main purposes:

1. Marketing & Sales - Knowing where your customers and prospects are relative to demographics and your network assets and coverage areas to maximize effectiveness and....
2. Engineering - More efficient network planning....using LI to make more cost effective decisions on where to deploy network assets at minimal cost and maximum effect.

The amount of data CSP's thirst for to help make these decisions is huge and while its different than the specific data insurance is after, they share one common characteristic: both want as precise and as localized information as they can get with the only restriction being privacy legislation. The thirst to utilize much more intelligence around location specific data is growing rapidly and the tools to analyze it are even more important. Twitter Places does provide a really simple example of how location context can add special value at the same time driving the awareness and therefore the advocacy of utilizing location intelligence.

Enterprise Business and Social Networking are getting closer through Location Intelligence - Alex MacKay, CEO DMTI

It was interesting to see the details of the much anticipated release of Twitter Places this week. It's another simple use of location in an ever growing awareness of Location Intelligence in ways that are similar to what Google Earth and Microsoft Bing did a few years ago. It has fascinating long term implications = you can now know the location context of where a tweet is coming from. The example Twitter uses in their announcement (is the tweet about the World Cup coming from inside the stadium or in front of a TV?) points out that the value of the location context can be significant.

What does that mean on an enterprise business level? Well in the enterprise usage of location the precise attributes of the information are always far more critical than in social networks. In recent posts we have talked about the need in the insurance vertical to know exact information about a specific property. As those needs get fulfilled (and they will) the quality of the information becomes even more important. Therefore information obtained while at the property site would have extra value (versus utilizing satellite or outdated government sources for example). What Twitter Places does in the social network game is essentially adding a similar context value. If I'm actually "there", my location and its associated information has special value. It's not surprising to see another example of how social/consumer use and enterprise use are slowing getting closer. As these types of examples grow, what will also become very apparent is that the combination of many sources of data (some real time, some from expert sources, some from trend sources) will become big time business, especially as the need for accuracy is paramount in the enterprise world. Visit our blog next week as we discuss how Twitter Places really represents the concept of "big data", and how that concept is translating into Location Intelligence for Communications Service Providers.

How Insurance Companies see a Good Payback from Location Intelligence - Alex MacKay, CEO

In recent posts we have heard from a futurist, an editor and an analyst about why Location Intelligence is important to the insurance industry. This week I decided to take the question to the actual "street" and talk with an insurance company. Speaking with Peter Silk, SVP at Lombard Insurance in Toronto, Canada we chatted about this question.....how important is location in insurance? Peter's experience says it's all about more and more data coming into use for decisions. He figures the importance of location information has grown at least threefold in the past couple of years. And while Peter agrees a main driving factor is risk mitigation, he also sees a number of business processes in insurance growing due to the need for more precise location information. He talked about these five in particular:

• Risk management around knowing specific property detail vs offering insurance in ways such as blanket policies where the specific risk on individual properties is not detailed
• Reporting to the various regulating bodies who are inquiring at a more individual property level (ex. Asking questions such as, are you drilling down to the location property level? OR do you have property policy specific exposure vs. aggregate?)
• CAT management and knowing where your risk locations exist. It simply means knowing more, such as your estimated probable maximum loss, your TIV, and being able to react promptly when events such as high wind storms cause damage
• Underwriters have many needs for this improved detail and right at the top of the list is to ensure that there is a managed balance level of exposure in any one geographic area
• And, having risk location information and location risk profiles for the re-insurance market makes the pricing of that business a whole lot better

These are all solid examples of just why insurance companies are seeing a good payback from investing in the capabilities of Location Intelligence.

I then asked Peter what trends he saw growing in importance in the upcoming years. He basically sees the need to get more information at the specific property level as the core need and that the data will just keep getting added. He says this is why getting the address correct is so critical. If you have the address nailed, then you can begin to overlay more and more data/knowledge on the exact location. He made a point of saying that if you can't be confident that two data sources are speaking to the same address then this greatly affects the ability to make an informed decision (knowing a property is mid street vs a corner property is a simple example with different decisions). The address trust is critical. With accurate confidence in the address, Peter then sees that insurance want the latitude and longitude to be precise and rooftop is what is ultimately needed. With rooftop accuracy he foresees that a lot of other maps and imagery can be overlaid for the decision needs. Next comes many other data sources such as loss information, credit information, and statistical history as examples.

As Peter said right in the beginning of our chat, it's all about more and more data. Location Intelligence is simply providing valuable information to help insurance companies make better business decisions.

It's not about Location, it's about Risk Management - Alex MacKay, CEO DMTI

We continue our focus on the insurance industry this week striving to further clarify why Location Intelligence is making such an impact in that vertical. And on that topic I was talking with David Sonnen, Senior Analyst of IDC earlier in the week and asked him for his thoughts. David reflected that with the economic recovery kicking in the insurance risk management teams are really digging into the details to make sure the overall system break down and recent meltdown does not occur again. In doing so they are seeing that knowing where stuff is, at a much more granular level is one of the key factors. Instead of doing risk profiles at state or county levels, many of the insurance companies are targeting right down to the property level. This is ambitious, but it drives the need for a lot of what Location Intelligence has to offer in terms of knowing property specific info, neighbourhood info and boundary or risk scores at that level of detail. Bottom line for David is "it's not about location really; it's about risk management. Location Intelligence, because of what it offers, is along for the ride."

As we chatted, David also brought up another real interesting point as he noted that this thirst for detail is spreading in many other ways. One of the strategic view areas is what David called "big data". What is big data? "It's the digital steam rising off of the planet" he stated. (I love that vision!) Big data refers to the growing realization among strategic thinkers, including the insurance companies, that there is a lot of digital data out there that can be found unobtrusively and used in good business decisioning. All of this type of data David refers to has geographic or location attributions. He is referring to cell phone data, GPS tracks, what is concerning a community info, crime rates, police info, and news info. It's about a situational awareness of what's going on at more granular levels like communities or neighbourhoods. David predicts that this big data phenomenon is going to help companies understand the relationship between people and their locations based on the trends and patterns in ubiquitous digital information, i.e. the digital steam rising.

I concur with David on both points. It really is about risk management in insurance. The clients we work with would say the exact same thing. And David is not alone in the outlook that more and more data is going to be analyzed (big data) and at significantly more granular levels. All this will lead to better business decisions.