Big Data is here to stay, but it's massiveness means some businesses are having a hard time taking it from mere buzzword to useful tool. The thinking that more data means more insight simply isn't true: adding context to all datapoints is key to deriving useable insights that are accurate. It's this new endeavor that needs consideration as the tech industry wants to continue its rocketship ride to the next level of growth.
Skyhook has long been a quiet member of the billion transaction club. For years, we’ve been handling billions of location requests around the globe, augmenting and accelerating end-device location capabilities since before the original iPhone came to market. Hundreds of millions of devices have relied on Skyhook to ensure that location is not only accurate, but that it is responsive and helps provide optimal user experience to the users of those devices.
Looking ahead to the next trillion transactions, I asked our Chief Technology Evangelist Kipp Jones a few questions around where Big Data is headed.
Do you think there is a demand for location data in the world of Big Data? What's location's role in Big Data?
Absolutely! So much of big data includes (or should include) location attributes. Location is one of the most important pieces of information - the other being time - to providing linkage to externally relevant context. Location can provide what I call a key to the entire world (literally) of additional context and information related to an external event.
How can location data improve Big Data analysis? (Or vice versa)
In my world, I tend to think of big data with a bent towards ‘events’ -- something happened and we have this small sample of what that ‘event’ was. Somebody visited here, an alarm went off there...something was triggered for some reason and we have a data point about that event.
In other words, if you wanted to match weather information to a particular event, you need time and location. Whenever you're layering additional information on an event you need these 2 data points to anchor the rest of the context.
With location, you can infer and make correlations about the environment in which any event took place. There are so many geo layers that you can reference -- weather, buildings, population density, historic activities, air quality, crime, etc. Given time and location, so much context can be added to increase the value of the original information.
In which areas/industries have you seen the biggest/most surprising location data overlap with Big Data?
Clearly, location and advertising is seeing a surge. But it is a major sea change for advertisers and technology to adequately harness the power of location. There's so much historical baggage with traditional web-based IP geolocation. The new age of app based advertising and in-app interactions will continue to leverage precise location in new and innovative ways to increase the value to both the consumer and to businesses.
The other area that is largely unexploited at this point is in the developing world of IoT. Without a doubt, I expect nearly everybody will want to know where their ‘things’ are. It’s a natural fit -- you deploy a bunch of things, you want to know where they are, where they are going and what they are doing. Regardless of the type of thing, there will be many, many uses for having location associated with your things and the activities that your things do. For any kind of enterprise or sensor network, you simply need location to be an attribute to that data. It's fundamental, like time.
- Industrial IoT: tracking assets whether they're small sensors or large equipment over large areas
- Outdoor IoT: weather stations can be easily spun up and contribute to the global weather measurement, but they need location!
- Consumer devices: where do Smart TVs and appliances live once they leave the store?
- Enterprise: if you have devices reporting tempurature in a building, you'd need to know the locations of all those devices to monitor the area.
What does the future of Big Data and location look like?
It looks like a lot of data! Whether you are a consumer, a business, an enterprise, an analyst, location attributed data will continue to provide value. The world continues to become more connected and location is a foundational element to this connectivity and how you will be able to better leverage the massive amounts of data. You can filter data based on location, you can add information via location, you can visualize and map your data using location.
What is Skyhook doing in regards to Big Data?
Lots. On one hand, we actually produce vast hordes of data via our deployed API and SDK customers. We leverage this data to continuously build a view of the Wi-Fi, Cell and IP landscape around the globe. This ‘Big Data’ (and it is quite large) allows us to maintain an active database of billions of elements to provide location services for our customers.
We are processing and analyzing billions of signals from location service APIs, and even more with our adtech clients on a daily basis. And those numbers are only accelerating. The 'volume, velocity, variety' that make up Big Data is all there. On the other hand, we provide services for customers that have their own ‘Big Data’ sets and are looking to leverage their location-based data, primarily within the advertising arena. We do a lot of work to isolate and filter out ‘bad location’ from this data. This, by the way, is probably the single biggest problem facing the use of location data today!
What industry could benefit most from Big Data and location services?
That's a tough one - there are so many options -- apps and devices can use location for better user experiences, smart cities, logistics, security, advertising, retail, gaming, etc. However, I think the following 3 areas are where I expect some major changes in the near term:
- Advertising: there are so many ways to leverage location here. Given the overall size of the market and the relative dearth of high quality location data there’s a lot of upside as the technology and the overall advertising ecosystem learns how to maximize the value.
- Logistics: anybody with assets and/or people that move around can leverage location services to improve end user value as well as overall enterprise value. The growth in this industry is going to be driven largely by the proliferation of both personal devices, as well as the overall IoT trends.
- Sensors: this area is becoming more and more prominent. Already there's a lot being done in the smart city area using sensors to monitor air pollution and light sensing. There's even work being done to correlate dark and light areas to crime data - imagine if there was a way to easily reduce crime by deploying more light? We're just scratching the surface of how to use disparate datasets to improve entire systems.
Are there any particular challenges Big Data and location services face?
Location data quality! Knowing the quality of the location data associated with big data is a critical component and still misunderstood by everybody. False security in the idea that all location is created equal can lead systems and people astray. It is critical that we continue to ensure that quality location is and must be a component within any system using location.
Are there any examples of successful integration of Big Data and location services?
Sure, take a look at Pokemon Go!