When Does Big Data = Big $$?

When does Big Data lead to earning big $$? When the big data extracts relevant analytics, intuitively connects to a user, and adds value. Big Data is valuable, when it adds value. Period.

There is always a market pull to pay for optics that support a vision, rather than substance. The big winners in tech or data plays are sometimes the optics focused players, but most often the long term winners are companies that have connected the vision or advantage intuitively to their users. This connection of data to user has to happen in a way that is easy to integrate into what was already happening, or in a way that replaces a pre-existing function with something so much better and easier that it overwhelms and replaces an established standard way of doing things. There's a lot of money in disruptive technology, or in clearly taking things to another level.

Big Data value is not the data by itself, but in making something better, starting with intuitive data analytics, flowing through apps and interfaces to users who experience value from the improved analytics and/or connectivity.

Obviously, the cloud has a big role to play in the usefulness and therefore value of Big Data.

Our attention is best spent looking at big data plays that reach right down into the detail of how the data is used through an application. The application /user interface is where we can find Big Data turning into Big $$.

Here's an interesting video on big data from IBM:

https://www.youtube.com/watch?v=x2ORW9Eu7BQ&feature=youtu.be

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Earlier I wrote the above post and thought we should revisit and add some context and also a few examples of winners.

The development of user software that exploits big data is quite different from the owners of the big data infrastructure, such as Cloudera which is already worth several billion dollars, or Big Data vendors like DataStax and MongoDB which are worth in excess of $1 billion already.

The Big Data infrastructure higher up the deliver chain or stack, closer to the data (vs closer to the user) tend to be open source just to make the development and adoption economically feasible. It i so complex a development challenge to integrate to that degree to achieve widespread adoption, business structures based on open source development is the best way to thrive.

Down closer to the user, developers of the software are now focusing on cross platform integration using both open source but also proprietary development that communicates with many types of software at the same time, for the ultimate utility, simplicity, and compatibility for the user.

How fast is the money moving if you hit the right 'high utility' app? My favorite example lately is Sunrise Calendar. To quote the Sunrise Calendar web site, "Sunrise is a free calendar app made for Google Calendar, iCloud and Exchange. Available on Desktop, iPhone, iPad and Android."

Founded in 2012, on February 4th 2015 Calendar announced their sale to Microsoft for $100 million. For a calender app. Wow!

It's the start of a tidal wave of value for the tech companies that get it right.

In the exhibit below McKinsey & Co. give their opinion of how much economic pressure or advantages there are for different industries to develop Big Data, and their relative ability to do so:

I like this way of looking at things; a macro perspective should be part of the decision process for developers considering projects, and also for investors tackling valuation and opportunity.

Big Data is going to be the gravitation for substantial wealth movement, however the biggest winners will be the smallest projects, the ones closest to the users, where the developer uses the open source solutions for the highly complex part of the stack, and can retain ownership of and sell licenses for relatively inexpensive to develop end of the stack, the 'user end'.

Or as we know it, serving the 'end-user'.