Stop Press : Check out my latest article on Big Data as the new digital currency that has been published in the prestigous CIO magazine …
Big Data has been with us for many, many years. Arguably, Big Data can trace its roots back to the 1880 US census.
This survey of 50 million people generated 2.5 gigabytes of data with results being calculated using punch cards in just six weeks.
The technology boom since 1880 has created the explosive growth of data within organisations.
IBM states that data growth is running at a torrent of 2.5 exabytes per day (where an exabyte is a billion gigabytes).
Of this 90 per cent of the information has only been created in the last two years.
Since 1880, technology has created a global economy where transactions have evolved from cash to credit cards through to electronic payments. The next evolution is a digital economy where data is the new currency.
To some Big Data describes vast data sets that are too large and too complex to be processed by conventional means. Big Data has five constituent parts:
– Volume: From hundreds of terabytes to petabytes, where a petabyte is equivalent to 500 billion pages of standard printed text.
– Velocity: Up to near real time sub second delivery
– Variety: Including both structured and unstructured data
– Volatility: Hundreds of new data sources coming online from new apps, web services and social networks
– Value: The opportunity to drive new competitive insights, operating models and products based on customer insight and intelligence.
Financial organisations are of course thought of as being in the money business, but in reality they are in the data business.
The information banks hold is staggering; from credit card, ATM, and online transaction data. Banks are just realising that data is their biggest asset in the new digital economy.
The Big Data benefits for financial services organisations are immense, from identifying the profitability of customers, creating new products and new market channels, through to mining customer sentiment and understanding customer needs and desires.
The more intelligence you have on these customers, the greater insight you can apply to new products and services, driving greater impact on revenues, margin and market share.
Lighting up Dark Data
Most companies only capitalise on 5 per cent of their in-house data, Forrester Research found in a study.
The challenge for banks is that the vast majority of information available to them is what is known as dark data.
Dark data is data that is either under-utilised or mothballed.
In today’s world organisations are over run by dark data, from machine and transactional data through to social media and location data.
A new form of competitive advantage will be those organisations that can extract value, by lighting up dark data, to develop new products and operating models based on deep customer insight and intelligence.
Google and PayPal are pioneering the collection, processing and analysing of Big Data. In banking new financial aggregators such as Intuit’s Mint are appearing.
These organisations are in a space that many financial organisations struggled to exploit the value of Big Data.
As new entrants they have defined new operating models and architectures based on customer information, intelligence and insights.
By leveraging data as a new currency, these organisations can better understand customer sentiment and desires and are able to offer innovative new products such as real time offer management and pricing for loan agreements based on customer intelligence.
As a result Google a non-banking organisation and PayPal a popular entrant are challenging the stalwarts of the financial services community.
The 720 degree customer
Historically banks have struggled to determine the true financial value of data as a raw material.
To capitalise on the value of data that exists within the organisation, companies need to extend the internal customer view to include an external perspective.
Specifically, organisations need to build a 720 degree view of their customers to unify and aggregate the internal view with the client’s external social and influencing network including Facebook and Twitter.
With a 720 degree view of the customer CIOs and banks have new opportunities to develop fresh operating and delivery models.
Credit card provider American Express (Amex) has recently partnered with Foursquare, a location-based social networking website, to offer targeted promotions based on customer transactions, social media and location data.
Based on a customer’s previous transaction history, Amex can uncover new customer insights, such as identifying the type of restaurants preferred and offer targeted real time promotions the next time they walk past their favourite French bistro.
Financial organisations are heavily reliant on overnight processing, the ability to build a 720 degree view of their customer will drive new opportunities for revenue increases, such as being able to predict market swings based on news sentiment right through to improved risk models and fraud detection based on predicted patterns and behaviours .
Payments have shifted from cash to cards and then online; the risk of fraud has significantly increased. The ability to look for unusual patterns and connections coupled with increased financial regulation has dramatically driven up the cost of fighting fraud.
A recent report from Deloitte identified that European financial institutions are subject to at least 24 distinct regulatory directives.
Of these, the US Frank-Dodd legislation covers 22 individual regulators, 16 broad topics and 445 different subject areas.
This regulatory burden has dramatically changed the retail banking landscape with a number of banks selling off their credit card business to organisations such as MBNA and Capital One.
PayPal came under sustained attack in its first year, which resulted in several of its competitors failing by the wayside.
Cleverly PayPal’s solution was a Big Data initiative called Igor, named after a Russian thief and hacker that attacked it.
Project Igor looked for patterns such as payment concentrations, transactions at payment limits and destinations. The result of Project Igor was that PayPal were able to extract increased value from its Big Data assets and drive down its online fraud rate to less than 0.5 per cent.
Data Management Strategy
The true value of Big Data for organisations is the opportunity to form deeper relationships with customers, pulling data together from different structured and unstructured sources alongside new ways of processing, such as Dark Data and the 720 degree customer.
The vast levels of data involved is staggering. To sift; mine and identify the right information will require a fully integrated approach to managing both the product and data lifecycles in an organisation.
To benefit from Big Data CIO’s will need a cohesive data management strategy focused on the business outcome. In much the same way that you would visit a hardware store to purchase an 8mm drill bit, the outcome desired is not the drill bit, but an 8mm hole.
Similarly, with Big Data there needs to be clarity on the actual business outcome required. Taking a Big Data approach of: “I want to store all data” will result in a data tsunami that CIO’s will be unable to visualise, let alone analyse and process.
The Spanish Bank BBVA have consciously avoided an ‘all you can eat approach’ to Big Data and have targeted four distinct business themes for their Big Data Strategy:
1. Cross selling and selling up:Target the right offer to the right customer based on increased customer insight and intelligence
2. Customer profitability: Have the right rates and pricing based on real time intelligence
3. Predictive Analysis: Analyse customer life events and predict their future needs, wants an desires
4. New products, and delivery models: Deliver new innovative insights to differentiate products and increase customer loyalty
There needs to be a discerning data management strategy that underpins Big Data, focused on the specific KPIs and outcomes including increased customer insights, to develop new innovative products that the business deserves and warrants.
An exciting by product of Big Data is the Data Exhaust. This is the unstructured information that is a by-product of the online activities.
Google applies the principle of data exhaust to many of its services. Microsoft spent several million dollars developing a spell checker; Google on the other hand developed a spell checker, in every language, based on the misspellings typed into its search engine.
The Google spell checker was developed on the by-product of data exhaust from its online search engine.
Collecting and analysing a data exhaust can provide valuable commercial insight into the behaviour, actions and aspirations of your customers.
Re-using data exhaust to improve a service or create a new product will create a new form of competitive advantage for those organisations embracing Big Data.
Banks need to know their customers better than anyone else and be able to extract the true financial value of data based on deep customer insight.
Banks are well placed to lead the development of the new economy by utilising their biggest asset: data.
By using new techniques such as Dark Data, Data Exhaust and building a 720 degree view of the customer, banks will have the opportunity to develop new innovative products and inspired services, based on a new level of intelligence and insight, to drive product differentiation and increased customer loyalty.
Ian Alderton was CIO for RBS Corporate Banking until April 2012 and European CIO at Wachovia Corporate & Investment Banking Technology for six years having been Prudential Financial and NM Rothschilds & Sons previously in his career
The article was first published in CIO Magazine on 21st September 2020.