Big Data’s Three Big Trends in 2016
24 Aug 2016 Marcel 0
Andy Hirst – SAP – Last year at Sibos, I talked in depth about some of the key learnings from Big Data projects. At the time, the industry was still getting to grips with the era of data and what that meant for the business of banks. A year on, we are still in a state of transformation, as industry-wide Big Data implications continue to unfold around us. I’d like to use this blog to talk to you about three of the most pervasive trends.
1. The Rise of Hadoop and Unstructured Data
With the rise of low cost shareware databases, such as Hadoop, unstructured data has now well and truly entered the knowledge mix. This has dramatically reduced the cost for banks to store unstructured and machine data alongside its transaction data. The opportunity now is for banks to see how much more insight they can tangibly gain as a result. For example, sentiment analysis can alert you to critical factors to better understand customer needs, risk exposure and patterns. And because both the volume and variety of available data has increased, while the cost of storing it has decreased, banks can assign an information lifecycle to the way different data is stored. ‘Hot data’ for example – that’s new, relevant and actionable – may be kept in near line storage; ‘Warm data’ may be archived, and ‘Cold data’ that’s less relevant or timely may be put into a data swamp.
I recently saw a presentation by one of the major banks that likened their Hadoop data lake to a flea market. The analogy was that there are some absolute gems in there, but you need the in memory SQL tools to find and extract them. A 360-degree customer view used to comprise whatever information a bank held on customers in its internal systems. Today, it’s a much more rounded view of the customer that could include their last ten tweets, their blog or voice comments on last call centre conversations. This needs to be combined with machine learning analysis of transaction data to create the complete view of customer requirements. I think we will look back on 2016 as the year low cost shareware came into its own.
2. Data Gets A New Boss
Another prevalent trend this year is the rise of the Chief Data Officer (CDO) in banks. It’s a testament to how seriously banks are treating data to ensure all requirements around security, privacy, regulations and other areas, even including international payments, are met and adhered to. Some banks now have multiple CDOs, appointing one for each area of the business. This is a good thing for all concerned, particularly as CDOs will most often bring a cohesive data strategy to a largely fractured data landscape. They can look at what data is clean, how it can be connected to relevant parts of the bank, and how it can be best exploited to drive value for customers and the bank itself.
This new C-level position can make a huge different to the business. At SAP, for example, we have our own Chief Data Officer, whose data management strategy delivered $75 million in cost savings, and fulfils an active leadership role on the board.
3. Open APIs Open The Door To New Revenue Streams
No conversation on data trends would be complete without mentioning APIs. Many industry watchers (and banks themselves) predict a more open approach to banking as more and more financial service providers open up their APIs. This opens the door for banks to innovate quicker, swap data with other institutions and partner with Fintechs on new Solutions.
For example, a large Spanish bank recently gave fintechs access to its anonymised credit card data – something it was not actively monetising but rather passively storing. The fintechs came up with 30 different business cases, such as visualizations to show spend patterns in different outlets over time and applications showing restaurants where demographic profiles on single people (age, gender, income) go out to eat on a Saturday night. Others included apps based on specific social lifestyles – all built within two weeks. The bank had considered this data to be completely valueless. Instead, it proved that data can shift from being a stored overhead to a profit generator.
In just 12 months, we’ve seen some major implications around data storage, management and innovative ways of monetising it. I’d urge you to consider capitalising on at least one of these trends if you’re not already doing so.
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