McKinsey1 in one of its very recent article on analytics states “Today, banks have the rare opportunity to reinvent themselves again- with data & analytics”. But the challenge is that many banks are currently saddled with data overdose while being anemic on the analytics. Said in the same breadth ‘data & analytics’, but it will need a real transformation for banks to make the journey from ‘data’ to ‘analytics’.
Why is it so important to have this change NOW?
- Digitalization of banking means minimal ‘human’ interface between bank & customer. Hence knowledge must be derived from data/ customer footprints to deliver effective customer experience
- Changing customer behavior where they are no longer happy with ‘one shoe fits all’ and is always looking for differentiated offerings. Unfortunately for banks, loyalty is no longer driven by tenure of relationship but by specific instances of ‘Wow!’ delivered at ‘moment of truth’
- Digitalization has also resulted in scrupulous customers/ spammers trying to game the system resulting in multi-million frauds to banks and the genuine customers.
- Slow but steady proliferation of challenger* / quasi banks (e.g. PAYTM, Airtel, P2P lenders etc.) who have made fintech their cornerstone to build their banks. Ignoring their competition, will mean them taking over the customer facetime leaving the established ones as transaction processors
- Significant competition, economic headwinds resulting in pressure both on operational margin & capital of the banks. This has necessitated them to look for newer revenue streams, optimization of costs, better monitoring of assets etc. and this can only be driven from insights drawn from data
While banks/ financial institutions are mostly aware of some/ all of the above, what they are missing currently is the appreciation of the magnanimity of the problem at hand. They are not sure whether it is worth the significant investments that it warrants and at time also lacks the understanding of the scale of transformation that is needed to drive this journey from ‘data to analytics’. This creates a destructive cycle as returns on such point investments falls below the bank expectations which then raises questions on efficacy of such transformational journey.
Hence today, we see investments by banks are primarily limited, cautious investments for a certain analytics use case rather than a more holistic approach. No wonder, in a recent joint report released by BCG & Morgan Stanley2, financial services organizations rank towards the lower end of the Digitalization Index (much beyond the Media, Hotels, Retail, Utilities).
The process of transformation of data generally happens in stages (exceptions are there where analytics is transaction/ event triggered through a rule based system) and organizations need to ensure that every stage of such transformation of data is executed properly.
The struggle starts from the very start:
- Stage 1: Generally, volume, variety & sources of data leads to potential latency issues
- Stage 2: Generally, multiple sources or data definition issues arises here leading to data integrity challenges
- Stage 3: Delays, inconsistency, starts showing up here because of the challenges mentioned in 1 & 2. This results in adoption & consumption challenges
- Stage 4: Due to lack of systematic approach, this remains a domain of few driven with no mass consumption and low utilization
- Stage 5: This mostly is a pipe dream, at best, achieved as small pilots in an experimental/ pilot area
The established large banks face multiple issues while trying to resolve the ‘breaks’ in the above chain. These issues arise mostly due to lack of nuanced reasoning while deciding the way forward and the correct approach to adopt. While it is easy to blame these large banks, but the choices are not that easy to make. The 2 most common fallacy in approaches are:
1. Start from ‘data’ and not from ‘users/ consumers’ leading to ending up on the wrong side of balance, spending lot of effort with no real improvement in experience of business users/ consumers of analytics/ BI
2. ‘Sunk cost’ becoming a point in consideration. While the discussion starts with ‘sweating of assets’ to optimize IT spend, it ends up on compromising the outcome. In a BCG survey2, 23% of respondents pointed to Legacy IT as a key challenge to effectively monetize data. Not surprisingly, Talent scarcity and lack of D&A strategy also figures in the top challenges in the same survey. A biased view based only on ‘Cost/ Investment’ can take the eyes off from the higher long term ROI expected from “Innovate”
The key to really resolve this effectively is to develop a more holistic & strategic approach to build the BI & analytics layer for the bank and this may need a few hard decisions to be made for the sake of a better tomorrow & a better ROI as well While right set of technology will be key, data governance & right people skills will need to go together.
1: McKinsey, April 2017, Analytics in banking: Time to realize the value
2: BCG, Morgan Stanely, October 2016, Data Analytics for FIs: The Journey from Insight to Value
*: Terminology used for UK banks primarily not falling into the main category of banks. Loosely used in reference to banks by supermarket chains, ecommerce, digital banks etc