Big Data Challenges, Value and Fallacies

When it comes to big data, I have discovered many challenges over the past three years, such as:  the extreme complexity of building digital representations of the physical world and understanding the underlying business value of location data.  

Digital mapping used to be quite basic in the 1990s and applied to limited use cases, primarily getting users from A to B without a paper map.  In the last 30 years, enabled by fast technology development, it all got a lot more sophisticated.  A typical logistics use case today sounds more like this: "give me the best route across four stops considering truck restrictions, minimize my fuel consumption based on road geometry and traffic, respect timing constraints for each one of the stop-overs. Give me also the cheapest lunch options no more than five minutes away from my second stop and inform the transportation manager when I enter the geofence of my final destination."

It is a whole new world.  This “new basic" usage involves huge amounts of data that need to be collected, kept accurate and fresh to remain meaningful.  Behind the fancier name of Location Intelligence,  mapping has become another "Big Data Business" with phenomenal opportunities to create value. 

Like many other big data businesses, there are still a lot of companies sitting on large amounts of data and not knowing what to do with it, even less what its value might be.  Many things get in the way of data monetization projects.  Some are quite basic misconceptions. Here are my top 3 fallacies from numerous discussions with customers and partners. 

1.  "Data need to be free "

There are two types of promoters of free data: the Ideologists and the Free Riders. The Ideologists are the ones with a strong belief that the world can only be a better place when things are in the public domain. I can partially side with the Ideologists. Some data have huge value to society. Anonymized health data, for instance, when aggregated, given a time- and a location-stamp, have the power to improve public health by accelerating research in disease prevention, treatment and emergency response. 

The Free Riders, on the other hand, are usually the ones selling services on top of someone else’s data and largely avoiding the costs to acquire, handle, store and make it accessible.  The economics do not hold. There has to be an incentive for the ones investing in high-quality data production to continue doing so. 

2.   "We need increasing amounts of data to be competitive"

Not always. There’s a critical threshold for any given type of data, but there are other reasons for data monetization projects to get stuck, lack of sufficient data is rarely the main issue: 

• lack of skills: besides the well-known scarcity for data scientists, there’s also lack of business acumen - what problems I want to solve, best business models to monetize - communication skills and other “soft” skills;
• tendency for complex models;
• lack of infrastructure and budget.

Also, in many instances, it’s not about data quantity, but about quality, diversity, and availability. Today, many business problems can be improved with the right data, instead of simply aiming to get more of the same. 

One example I love is the Traffic Gaaye ("Cow" in Hindi) application, developed by Videocon and Mobiiworld.  Cows cause traffic jams on Delhi streets. Realizing that the cows themselves had unique ways to avoid congestion, they decided to track them and ingest the data to provide for alternative routes. This reduced the average travel time by 15 minutes and turned the cows from part of the problem to part of the solution. Not more of the same data probes, but the right data to make a difference. 

3.  "Data is our most valuable asset: we need to protect it and keep it for ourselves"

Best case, this is only a half-truth. Data is surely an asset, but increasingly a liability as well. Regulation such as GDPR is making it clear that companies can no longer only look at ways to monetize their data, but also need to have the right policies and processes to ensure compliance. 

As for the need to keep everything for oneself to gain a competitive edge, that’s another myth.  The 2016 HBR article on Breaking Data Silos explains how the difficulty of getting access to data, even within a corporation, is a barrier acting against the interests of the organization as a whole.   Another example: just last month, Microsoft, SAP and Adobe announced their Open Data Initiative to start tackling the issue from a customer data perspective.  

Breaking data silos should also reach beyond the corporation walls.  In 2015, three competing automotive companies – BMW, Audi, Daimler – jointly acquired HERE Technologies and decided to share their vehicles sensor data as part of a common platform benefitting everyone.  In this example, aggregating originally competing sources of data enabled more reliable services than any single player could create on its own.  

Breaking data silos enables services that were not possible before and can unlock insight and innovation.  

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So, the next time your data monetization initiative seems to be going nowhere, look under the hood and study the potential misconceptions your company may be facing!