Breaking Big Data Down To Dollars And Cents: Why The CFO Should Care

moneyIf the conversations I’m having with finance leaders and their IT peers are any indication, the days when “Big Data” – be it text, unstructured, or web-generated – was treated as the exclusive domain of data scientists is drawing to a close. As CIOs and operating executives debate how best to collect, process, and analyze these proliferating new data types, leading finance executives are looking to break out of a spectator role.

Big Data and the office of the CFO

Often consumed by the challenge of managing traditional structured financial and related operational data, many finance professionals have questioned the applicability of big data—the debits and credits they handle on most days may not seem “big” enough to mesh with the unending streams of unstructured data. For progressive organizations, however,

the time is now for CFOs to get their feet wet in big data and start exploiting it for their companies’ larger financial advantage.

Eric Simonson, managing partner for research at Everest Group, has said that the big data opportunity for finance lies in identifying the business processes where operations and finance intersect and generating insights into what drives the financial results. This is what “data-driven finance” is all about.

Big Data in Finance—Real-World Examples

Leading enterprises are already applying big data and advanced analytical tools to leverage the integration of financial information and big data to drive significant cost savings and increase efficiency by reducing reliance on manual processes, generate revenue growth, and improve competitive advantage. For those who have invested intelligently in data and analytics, the related initiatives often do not require new, highly skilled resources or high-cost tools and applications.

Looking at companies who are pushing boldly into the big-data-for-finance-arena shows that they are following a similar blueprint based on financial systems architectures that enable three primary analytic capabilities:

  1. Agility: CFOs are increasingly expected to proactively inform day-to-day decision making in both finance and operations, which requires agility to respond to and promote change. To be agile, finance must have its own analytic hub, enabling more in-depth, near real-time analysis.
  2. Extensibility: Sustainable financial analytics are built on a decision-making environment that can be continuously updated and evolved with minimum effort, leveraging previous investment to build new projects.
  3. Predictability: CFOs need detailed profitability and operational insight based on actual behaviors, as opposed to managing on averages, to identify and take action on priority activities that can improve future profitability and help avoid unnecessary costs. Data-driven CFOs spend more time understanding trends and planning for future events rather than merely reporting historical results.

Early adopter companies across industries show these capabilities in action as they use financial, operational, and big data integration to generate critical new insights. Consider a couple of examples.

Wireless Provider

At a leading provider of wireless telecommunications services, finance and operational groups collaborate for better insight into drivers of call center costs by capturing and analyzing customer behavior, web activity, and call center and customer detail record data. The new insights are helping finance identify ways to minimize call center costs, while improving customer satisfaction and reducing churn.

Health Care Equipment Manufacturer

A leading health care industry manufacturer needed more post-sales insight into the status of its equipment. By loading usage log and sensor data into Hadoop and leveraging both big data analytic tools and its existing data and analytic architecture, the manufacturer better monitors customer equipment and provides more proactive and preventative service. This ability is helping maximize equipment uptime and minimize component replacement costs, while easing finance’s ability to work with business management to maximize service contract profitability.

These companies are using analytic data platforms that can support future initiatives at a fraction of the cost as they can leverage pre-integrated data for extended use with new data types and subject areas. This empowers their CFOs to take a critical first step in the big data journey by incrementally improving their understanding of the key linkages between financial results and the operational and behavioral drivers behind them.

The Time for Big Data Is Now

John F. Kennedy once noted that “Change is the law of life” and those who look only to the past or present “are certain to miss the future.” By building and leveraging financial systems architectures that support the use of big data and advanced analytics, forward-looking CFO organizations aren’t missing the future; they are helping their companies seize it.


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Shri Ranganathan

Practice Partner, Data Driven Finance Americas Center of Expertise Prior to joining Teradata, I spent five years leading IBM’s Financial Performance Management practice for the East region. As part of their advanced analytics group, I worked across industry verticals to train teams focused on helping CFOs address analytic challenges. I exercised similar expertise as Director of Consulting Services for Infor and also Wipro. A former PWC audit manager, I hold a BS & MS in Accounting, along with a MBA in Finance.

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