What Analytics And Big Data Can Learn From Lego

4/11/2013 @ 11:19AM |2,291 views

What Analytics And Big Data Can Learn From Lego

By Kurt Bilafer, APJ Regional Vice President, Analytics, SAP 

Lego recently became a hot topic of conversation with my son, a huge Star Wars Lego fan.  Lego is the fourth-largest toy company in the world, and here at SAP (and the Bilafer household), we have aspecial relationship with the brand.  In Singapore there is a Lego exhibit at theArtScience Museum where artist Nathan Sawaya uses Legos as his creative medium.  I asked around the office if anyone had been to the exhibit and one of my co-workers shared a more personal story – he lamented the fact that thousands of Lego blocks were gathering dust in his basement – the result of acquired sets for his three sons, culminating in dozens of Lego sets and completed projects.

As an analytics guy, this reminded me of Rebrickable, a Website for Lego enthusiasts.  Users input Lego set IDs and their current inventory of Lego pieces and voila!  – an analytics engine crunches the data and makes recommendations on new Lego creations they can build.  This means a second life for old, forgotten Lego sets and more importantly, saves me from having to rebuild the same sets with my son every weekend.

After the euphoria on finding this site wore off, I started to think about how this was a great example of Big Data.  Most consumers might not realize this is a real use case for big data – they simply use the Rebrickable solution to solve a problem.Legos are Data and Vice-versa

Legos allow you the flexibility to work with bricks of various sizes, shapes and colors and combine them to create something fun, unique, or even valuable (in the case of Nathan Sawaya). In a business context, the company’s data sits like Lego bricks just waiting to be used to create valuable insights. Lego builders must manage brick proliferation. CEOs, CMOs, CFOs and CIOs have data proliferation issues. Whether it’s data or Legos, the problem is similar and can be cured with the proper analytics.  The challenge is thinking differently about the solution. Typically we start with the end result in mind based on the data sets (or Lego sets) but it’s the unique combination of these sets that create the real value and competitive advantage.

The Art of the Possible

Can we bring the Rebrickable concept to the enterprise?  Yes, but let’s make it exponentially better. The approach requires the gathering of all data (not just transactional data) in order to rethink the solution to the problem.  It also helps to suspend disbelief; don’t get caught in the trap of doing business the “old way”, make a leap of faith and imagine a new approach.

Once the vision is understood, establish the right technology to enable it. Data quality is key. There’s no sense in making bad decisions faster, therefore a proper user interface needs to be available wherever and whenever users need it.

The result of laying the proper groundwork is an analytic engine capable of processing millions and millions of records with the intelligence to draw conclusions and insights on its own.  No need for queries, reports or dashboards. This tool recognizes patterns in the data and proactively creates the analyses.

Just imagine having answers to questions like the following:

What if you could predict the next star athlete?

What if you could develop a fuel pricing strategy down to the gas/petrol station level?

What if you could make targeted product up-sell recommendations “in the moment”?

With the proper analytic tool, there’s no need to worry about questions, it should be provide the information you need to make a decision and move to the next activity.

Sound far-fetched?  Maybe so, but we might be closer than you might think.