There is a need to provide industry-specific, relevant solutions which meet the needs of the business user as opposed to simply placing generic technical capabilities on the table and hoping some of them will be useful. Location analytics as a general topic has increased in maturity over the past decade especially as GIS has come out of the backroom, and has increasingly taken its place in the overall analytics story. It is easy to suggest that GIS is just another dataset but that fails to take into account the criticality of understanding ‘place’ in many different contexts.
Which are the industries that can benefit from location analytics?
There are obvious industries which can benefit, such as, retail, banking, insurance, agriculture and the like. In fact, it is more difficult to think of industries which are unlikely to benefit — even dentistry has a location component as it relates to lifestyle, diet and tooth decay.
Perhaps a different approach is to examine the topic from a higher level viewpoint, for example which industries are most likely to be impacted by the ‘connected home’. The ‘connected home’ will inevitably have a location component which will be of interest to retailers, utilities, insurers, insurers and others. It is also tempting to think about the ‘connected person’ and the ‘connected factory’, and of course the idea of the ‘connected car’ is increasingly becoming mainstream thinking. Perhaps the location component is the ‘golden data’ that unites all these different industries around single issues.
What sort of data do you tap to get to insights from location analytics?
Data is categorized into structured data and unstructured or ‘dark’ data. Dark data is the data that comes from devices, video, voice — and almost all of these have a potential location component. The increased availability of open data adds yet more information to the mix. Our ‘Analytical Exchange’ provides an open data exchange that includes a catalogue of more than 150 publicly available datasets that can be used for analysis or integrated into applications, including geospatial data.
How does location analytics fit into the Big Data movement?
Although we think of this as a ‘movement’, neither data nor analytics are the destination but rather they provides insight which helps organizations ensure that they are on track to meet their business objectives, and to make adjustments if needed. By the same measure, having effective location analytics is not the destination, but is a capability which helps organizations (and individuals) meet their objectives.
The Big Data movement has the potential to be enormously helpful to the location industry by allowing GIS experts to come out of the backroom and take the main stage. That requires those GIS experts to understand and be able to talk to the business issues, and not hide behind technical capabilities and jargon.
How do you plan to leverage analytics in the area of connected cars?
Many organizations started with niche vendors on proof-of-concepts. Increasingly, we are seeing a trend towards more strategic and substantial relationships with robust partners who have cutting-edge technology focused on the Internet of Things. We are also seeing the need for modular design architecture which allows a holistic view of the technical requirements yet allows scope for differentiation. Although telematics is of considerable interest to the insurance sector, it goes far beyond this. Ford recently described themselves as ‘no longer an automotive company’ and have recently joined forces with IBM to accelerate how Big Data is used with cars.
We also recently announced Germany as the global headquarters for our new IoT unit, as well as its first European cognitive innovation center. The campus environment brings together a thousand IBM developers, consultants, researchers and designers to drive deeper engagement with clients and partners. It will also serve as an innovation lab for data scientists, engineers and programers building a new class of connected solutions at the intersection of cognitive computing and the IoT. Overall it represents IBM’s largest investment in Europe in more than two decades.
How has IBM’s analytics architecture changed over the past few years?
Our analytics capabilities are now available ‘as a service’ or in the Cloud which helps organizations get up and running more quickly, as well as reducing the cost of development, deployment and maintenance. The level of interest in this may also be a by-product of the shortage of client-side analytical skills in the industry. We are also seeing a lot of excitement around the ‘hybrid Cloud’ where companies maintain their own data centres as well as using the Cloud. Some market researchers suggest that this will be an $88-billion market by 2019. We do not see hybrid Cloud as a transitional stage but rather that it is likely to be the approach that many companies choose to adopt going forward for the forseeable future
How does the recent acquisition of the Weather Company fit into IBM’s scheme of things?
In the same way that location is infused into many business decisions, so too is the topic of weather. The ability to better understand weather conditions in the context of operational decisions, supply chain, risk, and customer demand all have validity and of course have a location element. Beyond this, Weather Company provides us with an operational platform which allows us to digest and process data from thousands of sources, resulting in 2.2 billion unique forecast points and on an average day 15 billion forecasts. Additionally it represents an interesting shift beyond IBM only analyzing third party data into being the owners of data ourselves.
What do you think will come next for location analytics as an industry?
Analysts anticipate that the IoT market will be about $400 billion by 2019. Data from many, if not all, of these sensors will have a location component, so it is pretty clear that location analytics is here to stay in one form or another. One thing that the location analytics industry is great at is in the creation of effective visualizations — we used to call them ‘maps’ — but the visual presentation nowadays is so much more important. I am waiting for a breakthrough in the visualization of location analytics.
I am also curious to see how the location analytics industry will cope with the changes being brought upon it by the cognitive era. Being able to apply analytics to unstructured data in a ‘cognitive’ manner will make the geospatial environment very different to what we know today. The next generation will grow up with cognitive analytics as ‘the norm’, and how will this change the skills of a future geospatial specialist? The geospatial industry has to think hard about in the future, and that thought process needs to have started already.