In the retail space, 2018 has seen many local banks firing shots across the bow, with innovations like ‘switch with a selfie’, biometric ATMs, Bitcoin trading, WhatsApp banking, programmable cards for developers and other headline-grabbing features.
Without the right data strategy at the heart of the business, these innovation efforts are nothing more than a gimmick which will quickly run out of steam, and banks will struggle to generate sustainable competitive advantage.
With increased investment in information technology (IT), many wonder why local banks struggle to clean and coordinate their data structures and turn them into the kind of insights that enable truly personalised customer services.
In the past, data for particular channels (such as online banking or ATMs), or different products (such as home loans or credit cards) would be stored independently in different silos.
Over time, more and more silos sprung up, leaving chief information officers and chief digital officers with the unenviable task of trying to coordinate data sets composed of different structures, owned by different areas of the organisation, and with different compliance and regulatory considerations.
Concurrently, the concept of unstructured data rose to the fore – where banks realised that every customer touchpoint, such as a call centre transcript for instance – could contain valuable insights into customer needs, frustrations and desires.
This became a perfect storm of data issues that has left banks reeling ever since.
But just how does data quality influence an organisation’s innovation capabilities?
The adept use of big data enables smoother and faster customer onboarding, which is becoming a vital weapon as banks look to ‘switch’ customers over from their competitors as easily as possible.
By smartly using data from credit bureaux and other official sources, banks can pre-populate, simplify and even completely eliminate the need for those lengthy application forms.
Remember that as the younger generation gets older, they are comparing the banking experience with the ease of digital services such as those offered by Facebook or Uber.
It is possible to ensure strong security and credit scoring controls, while also eliminating pain points for customers in areas such as signing-up, opening new accounts and products, and transacting.
We can draw on illustrations from the automotive industry – which also operates under very strict compliance and safety regulations – where in the past few years we’ve seen incredible innovation in areas like self-driving cars and even prototype flying cars.
For the financial services industry, having confidence in one’s data (just like Tesla has confidence in its mapping data, for instance) is essential for ground-breaking new innovations to emerge.
An organisation must first gain a sense of control over where all of its data is lying, and understand who owns each dataset, what format it’s in, and what are the rules and laws around its usage. From there, it becomes possible to start parsing the data into advanced analysis engines and pulling in artificial intelligence and cognitive computing capabilities.
While it’s never going to be possible to imagine, and work towards, a perfect end-state with your data journey, you can start exploring and using your data in new ways, sparking new ideas for innovation and competitive advantage.
