Tag Archives: Data Trust

Analytics Stories: A Banking Case Study

Right to winAs I have shared within other post within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. In banking, the right to win increasingly comes from improving two core sets of business capabilities—risk management and customer service.

Significant change has occurred in risk management over the last few years following theAnalytics subprime crisis and the subsequent credit crunch. These environmental changes have put increased regulatory pressure upon banks around the world. Among other things, banks need to comply with measures aimed at limiting the overvaluation of real estate assets and at preventing money laundering. A key element of handling these is to ensuring that go forward business decisions are made consistently using the most accurate business data available. It seems clear that data consistency can determine the quality of business operations especially business risk.

At the same time as banks need to strengthen their business capabilities around operations, and in particular risk management, they also need to use better data to improve the loyalty of their existing customer base.

Banco Popular launches itself into the banking vanguard

Banco Popular is an early responder regarding the need for better banking data consistency. Its leadership created a Quality of Information Office (the Office uniquely is not based within IT but instead with the Office of the President) with the mandate of delivering on two business objectives:

  1. Ensuring compliance with governmental regulations occurs
  2. Improving customer satisfaction based on accurate and up-to-date information

Part of the second objective is aimed at ensuring that each of Banco Popular’s customers was offered the ideal products for their specific circumstances. This is interesting because by its nature it assists in obtainment of the first objective. To validate it achieves both mandates, the Office started by creating an “Information Quality Index”. The Index is created using many different types of data relating to each of the bank’s six million customers–including addresses, contact details, socioeconomic data, occupation data, and banking activity data. The index is expressed in percentage terms, which reflects the quality of the information collected for each individual customer. The overarching target set for the organization is a score of 90 percent—presently, the figure sits at 75 percent. There is room to grow and improve!

Current data management systems limit obtainment of its business goals

Unfortunately, the millions of records needed by the Quality Information Office are spread across different tables in the organization’s central computing system and must be combined into one information file for each customer to be useful to business users. The problem is that they had depended on third parties to manually pull and clean up this data. This approach with the above mandates proved too slow to be executed in timely fashion. This, in turn, has impacted the quality of their business capabilities for risk and customer service. According to Banco Popular, their approach did not create the index and other analyses “with the frequency that we wanted and examining the variables of interest to us,” explains Federico Solana, an analyst at the Banco Popular Quality of Information Office.

Creating the Quality Index was just too time consuming and costly. But not improving data delivery performance had a direct impact on decision making.

Automation proves key to better business processes

TrustTo speed up delivery of its Quality Index, Banco Popular determined it needed to automate it’s creation of great data—data which is trustworthy and timely. According to Tom Davenport, “you can’t be analytical without data and you can’t be really good at analytics without really good data”. (Analytics at Work, 2010, Harvard Business Press, Page 23). Banco Popular felt that automating the tasks of analyzing and comparing variables would increase the value of data at lower cost and ensuring a faster return on data.

In addition to fixing the Quality Index, Banco Popular needed to improve its business capabilities around risk and customer service automation. This aimed at improving the analysis of mortgages while reducing the cost of data, accelerating the return on data, and boosting business and IT productivity.

Everything, however, needed to start with the Quality Index. After the Quality Index was created for individuals, Banco Popular created a Quality of Information Index for Legal Entities and is planning to extend the return on data by creating indexes for Products and Activities. For the Quality Index related to legal entities, the bank included variables that aimed at preventing the consumption of capital as well as other variables used to calculate the probability of underpayments and Basel models. Variables are classified as essential, required, and desirable. This evaluation of data quality allows for the subsequent definition of new policies and initiatives for transactions, the network of branches, and internal processes, among other aspects. In addition, the bank is also working on the in-depth analysis of quality variables for improving its critical business processes including mortgages.

Some Parting Remarks

In the end, Banco Popular has shown the way forward for analytics. In banking the measures of performance are often known, however, what is problematic is ensuring the consistency of decision making across braches and locations. By working first on data quality, Banco Popular ensured that the quality of data measures are consistent and therefore, it can now focus its attentions on improving underling business effectiveness and efficiency.

Related links

Related Blogs

Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

 

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The CFO Viewpoint upon Data

According to the Financial Executives Institute, CFOs say their second highest priority this year is to harness business intelligence and big data. Their highest priority is to improve cash flow and working capital efficiency and effectiveness. This means CFOs highest two priorities are centered around data. At roughly the same time, KPMG has found in their survey of CFOs that 91% want to improve the quality of their financial and performance insight obtained from the data that they produce. Even more amazing 51% of CFO admitted that “collecting, storing, and retrieving financial and performance data at their company is primarily accomplished through a manual and/or spreadsheet-based exercise”. From our interviews of CFOs, we believe this number is much higher.

automationDigitization increasing but automation is limited for the finance department

Your question at this point—if you are not a CFO—should be how can this be the case? After all strategy consultants like Booz and Company, actively measure the degree of digitization and automation taking place in businesses by industry and these numbers year after year have shown a strong upward bias. How can the finance organization be digitized for data collection but still largely manual in its processes for putting together the figures that management and the market needs?
 
 
TrustCFOs do not trust their data

In our interviews of CFOs, one CFO answered this question bluntly by saying “If the systems suck, then you cannot trust the numbers when you get them.” And this reality truly limits CFOs in how they respond to their top priorities. Things like management of the P&L, Expense Management, Compliance, and Regulatory all are impacted by the CFOs data problem. Instead of doing a better job at these issues, CFOs and their teams remain largely focused on “getting the numbers right”. And even worse, the answering of business questions like how much revenue is this customer providing or how profitable this customer is, involves manual pulls of data today from more than one system. And yes, similar data issues exist in financial services organizations which close the books nightly.

ProblemCFOs share openly their data problem

The CFOs, that I have talked to, admit without hesitation that data is a big issue for them. These CFOs say that they worry about data from the source and the ability to do meaningful financial or managerial analysis. They say they need to rely on data in order to report but as important they need it to help drive synergies across businesses. This matters because CFOs say they want to move from being just “bean counters” to being participants in the strategy of their enterprises.

To succeed, CFOs say that they need timely, accurate data. However, they are the first to discuss how disparate systems get in their way. CFOs believe that making their lives easier starts with the systems that support them. What they believe is needed is real integration and consolidation of data. One CFO said what is needed this way, “we need the integration of the right systems to provide the right information so we can manage and make decisions at the right time”. CFOs clearly want to know that the accounting systems are working and reliable. At the same time, CFOs want, for example, a holistic view of customer. When asked why this isn’t a marketing activity, they say this is business issue that CFOs need to help manage. “We want to understand the customer across business units.  It is a finance objective because finance is responsible for business metrics and there are gaps in business metrics around customer. How much cross sell opportunities is the business as a whole pursuing?”

Chief Profitability Officers?                                               

Jonathan Brynes at the MIT Sloan School confirms this viewpoint is becoming a larger trend when he suggests that CFOs need to take on the function of “Chief Profitability Officers”. With this hat, CFOs, in his view, need to determine which product lines, customers, segments, and channels are the most and the least profitable. Once again, this requires that CFOs tackle their data problem to have relevant, holistic information.

CIOs remain responsible for data delivery

Data DeliveryCFOs believe that CIOs remain responsible for how data is delivered. CFOs, say that they need to lead in creating validated data and reports. Clearly, if data delivery remains a manual process, then the CFO will be severely limited in their ability to adequately support their new and strategic charter. Yet CFOs when asked if they see data as a competitive advantage say that “every CFO would view data done well as a competitive advantage”. Some CFOs even suggest that data is the last competitive advantage. This fits really well with the view of Davenport in “Competing on Analytics”. The question is how soon will CIOs and CFOs work together to get the finance organization out of its mess of manually massaging and consolidating financial and business data.

Related links

Solution Brief: The Intelligent Data Platform

Related Blogs

How CFOs can change the conversation with their CIO?

New type of CFO represents a potent CIO ally

Competing on Analytics

The Business Case for Better Data Connectivity

Is Big Data Destined To Become Small And Vertical?

What is big data and why should your business care?

Twitter: @MylesSuer

 

 

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Posted in Business/IT Collaboration, Enterprise Data Management, Governance, Risk and Compliance | Tagged , , , , , | 2 Comments