Banks aren't doing enough to mine customer data in search of new sources of profit.
As they recover from years of crisis mode, banks are discovering that they're behind in customer-focused, profit-optimized services. The February issue of our sister publication Bank Systems Technology shares some of the challenges and opportunities banks face in normalizing their data and phasing in advanced analytics.
Associate editor Jon Camhi cites an SAP survey that illustrates how banks lag behind other industries, especially retail, in customer analytics. The survey reveals that:
Only 46% of banks can analyze external data about customers.
Only 32% can analyze social media activity.
Data volume and analytics complexity are the most common challenges cited by respondents.
Cloud and predictive analytics technologies will be "extremely valuable" to around 60% of respondents' strategies in the next 24 months.
Some of the experts Camhi spoke with said unstructured data is overwhelming for legacy systems in banks, and even internal data management protocols are often in conflict. The first step for banks today is to get their database structures normalized and standardized, before they introduce new data sources into the mix.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
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