Social Banker

The Data Debate – 5 Big Challenges for Banks 

Many banks are positively salivating over the rich source of customer data that social media offers. Indeed, by carefully harvesting, analyzing and leveraging social media data, banks may be able to gain valuable insight into customer investment patterns, market trends and value propositions, to name just a few.

As a result, banks are increasingly starting to question exactly what can be accomplished with the information they gain from social media, and how they might use that insight to further their business objectives.


But the use and application of social media data also raises a number of important questions for banks that will need to be carefully considered. So before banks can take full advantage of the data they gather from social media channels, they may want to consider five key questions:



The use and application of social media data raises a number of important questions for banks that will need to be carefully considered.
  1. Can you be confident about the validity of the data? Social media data is largely ‘user generated’ and can therefore be very difficult to validate. As such, many are questioning whether this information represents a true reflection of customer trends, or if it might be biased by planted or augmented information. Banks will therefore need to consider what type of analysis might be needed to verify the actual value of the data and whether it is representative of their customers’ actual lives, demands and feedback.

  2. Who else has access to this data? Given the rising adoption of social media in the banking industry, it seems clear that the data gleaned from these channels will increasingly become commoditized and, over time, may diminish in value. As a result, banks will need to continuously adjust and adapt their analysis mechanisms to achieve increasing levels of depth that will – over the long-term – continue to provide competitive advantage and value to the bank.

  3. When does it become too much data? For many banks, the sheer volume of data flowing from social media can quickly become unwieldy and difficult to effectively manage. What’s more, by leveraging modern analytical techniques, it is increasingly possible to ‘cut’ the data in such a way as to support almost every possible scenario. Taken in its entirety, this could quickly result in reduced clarity and decision making for banks as the ‘messages’ being delivered by social media data become increasingly contradictory.

  4. Is your data being sabotaged? Whether intentional or not, social media data can easily be corrupted. In some cases, outside data can be ‘fed in’ to social media channels from other sources such as competitor websites and promotional campaigns. In extreme cases, banks’ social media sites may be hijacked by more nefarious parties seeking to wreak havoc or misinform decision making. This may lead banks to distrust the data they are receiving and ultimately rely more heavily on their own insights, thereby bringing them full cycle back to today’s status quo.  

  5. Who owns the data? Ownership of social media data is already a critical question for many industries. Indeed, given that the data resides in the public domain, banks may well wonder whether they need to own the data in order to properly conduct and utilize their analysis. And as banks migrate more and more of their communication onto social channels, the question of its ownership could quickly become a much more contentious issue, particularly when applied to banks’ own social media content.

So while social media data certainly promises to deliver massive dividends to banks that are able to capitalize on its insight in the near term, it seems increasingly likely that the issue of social media data will – over time – create a series of new complexities and challenges for banks.


By Harry Hughes and James Mckeogh, KPMG in China


Gary Richardson Gary Richardson (KPMG in the UK): ‘Big data’ has been a feature of the social web since Mark Zuckerburg popularized the term ‘The Social Graph’. For financial services, it is critically important to understand the hyper-connectivity of the customer base and its interconnection with its products and services. The rise of both social data and big data architectures has brought together a convergence point which, until recently, was out of reach of the majority of organizations. We are now at a point where leveraging the enterprise social graph can help banks uncover information they have never even dreamed of before.



Mitch Siegel Mitch Siegel (KPMG in the US): As Harry and James point out, the data gleaned from social media can be both a benefit and a challenge. Banks should determine what they are trying to get out of social media data and how it can inform their changing operating models. This should start through collecting, analyzing and leveraging data sets that are most core to their business. Creating this prioritization hierarchy will be critically important in helping banks avoid data paralysis.



Francisco Uria Francisco Uria (KPMG in Spain): Data is rapidly becoming the new currency of business, which may offer banks a new form of revenue by selling parts of their data collection to partners and outside industries. As we’ve started to see already from the mobile banking industry, access to data regarding shopping patterns, spending preferences and demographics can generate significant interest in the market. However, to retain their position as a trusted service provider to customers, banks considering this approach will need to exercise extreme caution and be very clear about how customer data will be used.



James Scott James Scott (KPMG in Australia): The ownership of data leads to interesting questions and business opportunities for third-party service providers that are able to aggregate this type of information. In particular, small and regional banks may quickly find that data aggregators or service providers are an absolute necessity if they want to enjoy the same level of benefits as their larger global peers who – arguably – have more resources to crunch value out of their data.

The views and opinions expressed herein are those of the authors and do not necessarily represent the views and opinions of KPMG International or any KPMG member firm.

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