30
Sep

Data Monetization – the untapped profit opportunity for legacy Financial Services Companies

The concept of data monetization is not new. Some 400 years ago insurance companies were formed on the basis of the monetization of shipping data. Actuarial science applied to longevity and health are the backbone of the life and health insurance industries and have been around for decades. The same is true for the linkage weather forecasting and commodity trading. There are lots of other familiar examples. So, what’s new? And what can legacy companies do to catch and ride the data monetization wave?

Firstly, the cost of computing and data storage has fallen dramatically and rapidly. The cost of computation is roughly one hundred-millionth what it was in the 1970s. and the cost per megabyte of data storage in 1956 has fallen from US$85,000 to $0.00002 today in constant dollars. Furthermore,connection speeds of hundreds of megabits per sec now cost only tens of dollars per month (Source: The Economist).The result of this, is that organizations installed a myriad of systems – computers and software – to enhance their services, resulting in the capture and storage of enormous amounts of data. Most of it is, in the best of cases, underutilized.

Secondly, very advanced analytical techniques are now available to process and analyze data. This has been accelerated by the emergence of open-source sharing, which has made very sophisticated tools widely and cheaply available. And every day, there are even better and faster tools being launched.

Companies can and should realice operationalized data monetization benefits as early as three months after embarking on an initiativeand ramping up to steady state within six to twelve months, depending on the initiative. However, they will need to make some adjustments:

  1. Decouple Data Monetization from Digital Transformation. The fact is digital transformation is not an essential precursor to data monetization. Most legacy institutions have embarked on substantial investments in digital transformation. However, very few have focused data monetization. Data Monetization does not require digital transformation to deliver significant impact. There is an erroneous misconception that although there is a lot of data, it is not easily accessible and therefore not able to be used for monetization. For sure, the more data collected and the more structured and organized the storage of that information, the better. However, with the tools available today, most if not all the data currently being captured by financial institutions, can be rapidly monetized.
  2. Top-down approach. Put a highly respected member in charge of the Data Monetization program. She or he should be empowered to drive change. And at the same time incentivize the ENTIRE EXCO for the success of the program to ensure cross divisional cooperation.
  3. Set aggressive but achievable financial targets with delivery deadlines in months not years.
  4. Reduce Data Monetization Complexity. Whilst Complexity due to Governance and Regulatory Controls are essential, empirical experience based on real case studies demonstrate that, with the right approach, people and skills data monetization initiatives can be completed in a fraction of a time currently taken. Complexity drivers interact exponentially – # of initiatives, # of stakeholders, #data sources, #monetization tools, #data generating platforms etc… As digital transformation, IOT – Internet of Things – … evolve, even more data is generated and tools turn even more sophisticated. Internal complexity is unlikely to decrease unless institutions undertake disruptive cultural transformations towards simplification, identifying and removing unnecessary complexity.

Data Monetization may be one of the biggest untapped Profit improvement opportunities available to legacy Financial Service companies. It is likely to be one of the fastest and surest ways to offset the coming economic headwinds. Are companies ready for the challenge?

Authors:

Amparo Marin de la Barcena. PhD Candidate. Quantitative models applied to the Financial and Strategic environments. Universidad Politécnica de Madrid. @amarinbg

Peter Cummings. CEO & Co-founder. Essex Lake Group, LLC

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