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INFOGRAPHIC: Big Data Alchemy

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This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able …

This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.

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  • 1. How can Banks Maximize the Value of their Customer Data? #digitaltransformation of banking executives worldwide say customer centricity is important to them But only analyze customers’ share of wallet of financial institutions in North America think that successful big data initiatives will define the winners in the future However, only Inflexible legacy systems A large European Bank embarked on a big data project to analyze a large amount of unstructured data Big data requires new technologies and processes to An average company sees a return of just of bankers are cautious in their use of big data due to privacy issues A recent security breach at a leading UK-based bank exposed databases of thousands of customer files Embed analytics into core business processes Strive towards full executive sponsorship for analytics initiatives Develop and promote a company-wide analytics strategy Establish a well-defined recruitment process to attract analytics talent Consolidate disparate analytics teams into an Analytics Center of Excellence Set up strategic partnerships for supplementary analytics skills Build a robust data management framework for the collection, storage and use of structured and unstructured data Graduate to predictive and prescriptive analytics that enable more precise modeling of customer behavior Sources: 1. SAP and Bloomberg Businessweek Research Services, “Banks Betting Big on Big Data and Real-Time Customer Insight”, September 2013 2. Capgemini and EFMA, Retail Banking Voice of the Customer Survey, 2013 3. Microsoft and Celent, “How Big is Big Data: Big Data Usage and Attitudes among North American Financial Services Firms”, March 2013 4. Capgemini and the Economist Intelligence Unit, The Deciding Factor: Big Data and Decision-making, 2012 5. Computerworld UK, Deutsche Bank: Big data plans held back by legacy systems, February 2013 6. Finextra Research, Clear2Pay, NGDATA, “Monetizing Payments: Exploiting Mobile Wallets and Big Data”, 2013 7. Wikibon, “Enterprises Struggling to Derive Maximum Value from Big Data”, September 2013 8. Mail Online, “Exposed: Barclays account details for sale as 'gold mine' of up to 27,000 files is leaked in worst breach of bank data EVER”, February 2014 of banks have hands-on experience with live big data implementations 3/4 of banks do not have the right resources to gain value from big data Banks face the challenge of training end-users of big data, who may not be data experts themselves How can Banks Scale-up to the Next Level of Customer Data Analytics? 70% 29% 90% 37% Silos of Data Block a Single Customer View The Skills and Development Gap Needs Closing Lack of Strategic Focus: Big Data Viewed as Just Another ‘IT Project’ Privacy Concerns Limit the Adoption of Customer Data Analytics Reach out: Interested in reading the full report? Head to http://www.capgemini-consulting.com/Big-Data-Customer-Analytics-in-Banks Follow us on Twitter @capgeminiconsul or email dtri.in@capgemini.com Faced difficulties in and their integration with big data systems the extraction of data from legacy systems Impede data integration Prevent a single view of the customer STORE ORGANIZE RETRIEVE 55 cents on every dollar that it spends on big data large volumes of structured and unstructured data Such incidents reinforce concerns about data privacy 62% Drive a Shift from a ‘Data as an IT-asset’ to a Establish a Strong Data Management Framework for Big Data Develop Analytics Talent with a Targeted Recruitment and Continual Training Programs ‘Data as a Key Asset for Decision-Making’ Culture Banks have not fully exploited customer data Why are Banks Unable to Exploit Big Data? th Big Data Alchemy