Value-Driven Lessons From the LEGO Effect

There is value in understanding the complexity of all possible permutations that exist amongst data elements. But, the reality is that most business problems require an incredibly narrow focus to achieve optimal business value. So, the critical question is: how do you determine the focus? Using a LEGO analogy, I present a frame of reference to guide deciding what to focus on to maximize value.

Durhuus and Eilers (2005), in the famous paper titled “On the entropy of LEGO,'' addresses questions around the decent upper and lower bounds for the number of configurations with N bricks. According to Lego statisticians, two 2x4 (eighth-button) bricks render 24 possible shape combinations. Add a third brick and that number increases to 1060. Add three more and the LEGO magic really kicks in: just six (2x4) bricks make the lucky recipient into a near billionaire of playtime possibility, with an incredible 915,103,765 combinations at their fingertips.

While the debate on the most accurate estimate of possible combinations continues, there is consensus that the number is exceptionally high and that the almost-one-billion billion estimate is acceptable. This number can even be higher if you consider that the user can select r items from the total n blocks where order doesn't matter (combinations) and similarly where order matters (permutations).

What can data practitioners learn from the simple yet powerful LEGO effect for applications in parts of data management to use "data bricks" to unlock limitless possibilities for business players? The fine-tuned simplicity of the LEGO brick is precisely what ensures the dizzying potential of its applications. Through this article, I share insights on an unconventional perspective to optimal exploitation of data assets for maximum value that data leaders should consider.

The analogy's core premise is that all possible combinations are valuable and relevant. But, this is not the case in most real-world implementation scenarios. The reality is that only a few of these combinations will create maximum value. Many redundant combinations will not add to the overall value but will deter the designer, user and other roles from seeing potential. Big data is the phrase created by scientists to explain the explosion in accessible and usable data. It refers to the absolute scale of data points being recorded and made accessible by the second. If your business does not generate data at this velocity, you can still attempt to understand the ultimate purpose of combining data elements in ways never thought possible. With scale comes technology complexity as we attempt to aggregate, extract and enrich data points at levels never seen before.

Why Value?  A Perspective on Advanced Analytics

Data in and of itself is meaningless. Using data management practices such as meta-data, we give the data context and meaning. The transformation through business intelligence and analytics techniques allows us to turn information into knowledge, insights, and wisdom.

In my organization, Medscheme & AfroCentric Health, a diversified company substantially invested in health care assets, with almost 3.8 million customers generating approximately 25 million interaction touch points monthly (300 million annually); this implies the nearly billion possibilities per customer every eight months. For all our 3.8 million clients, this represents trillions of possibilities for each customer and group of customers. These possibilities are much higher if you consider demographic and product artifacts. The big question then becomes, what are the opportunities that lie in these trillions of opportunities to transform our business? Thus, this necessitates a unique responsibility of the advanced analytics function.

As with the LEGO combinations, most of the shape combinations are meaningless. The actual value lies in identifying shapes that tell some form of a story. This is even more complex for organizations as our data bricks have different forms, shapes and sizes (structured, semi-structured and unstructured data). Therefore,  the role of the advanced analytics team is even more critical in helping the organization filter out the junk combinations to remain with what is meaningful. The filtering requires high computing capability and organizational capability to apply both supervised and unsupervised machine learning techniques to do this with speed and a high degree of precision. Thus, the role of the advanced analytics team is primarily one that helps the organization filter meaningful combinations of their data bricks and collaborate to present the meaningful combinations through compelling storytelling and visualization. These combinations range from identifying personalized opportunities to intervening with the right solutions at the right time in a most cost-effective way to enhance health and wellness for our customers and equally service them in a way that delivers the best customer experience. The effectiveness of this function ensures maximum extraction of value from the use of data assets.

Conclusion: Lessons on Data Management and Leadership

The value-driven approach to big data rooted in combinatorics and permutations is arguably the most comprehensive and effective strategy. With this approach, organizations increase their chances of capturing data assets for business model transformation and enhancing their products and services. The LEGO effect provides an unconventional approach to how organizations can embark on this journey.

One other unique value proposition is that LEGO is renowned for its quality, hence, the willingness of its customers to pay a premium relative to competing products. Data is no different; durability and integrity are critical components to practical big data strategy realization and will often require that organizations invest in ensuring a sound data quality program. Furthermore, we can learn a valuable lesson about enabling a data-driven culture using this concept as data leaders. Anyone can play with the blocks, from a 2-year-old to an elderly, more astute individual. The more astute are likely to build more meaningful and complex structures than a toddler, but everyone gets to play and have fun. It’s insightful for data leaders to consider how they can simplify their data bricks to enable greater participation by business users. This is the greatest opportunity to achieve data democratization and drive effective data culture. Data monetization literature well documents the implied impact of this on improved decision-making capability for organizations. So, have fun with your data bricks and create some data effects!

Author Bio: Vukosi Sambo is a Global Top 100 data visionary, Global Top 40 Under Forty leader and Judge of Global Top 100 data activators. He is currently the General Manager and Executive for Business Data Insights at Medscheme and Afrocentric Group. He serves as an advisory board member for data and technology groups. The views expressed in this article are his own and not on behalf of the organization.

References

Crompton, Andrew. (2012). The entropy of LEGO. Environment and Planning B: Planning and Design. 39. 174-182. 10.1068/b37063.

Durhuus, Bergfinnur & Eilers, Soren. (2005). On the entropy of LEGO r. Journal of Applied Mathematics and Computing. 45. 10.1007/s12190-013-0730-9.