BPO Whitepaper Bigdata Analytics Finance Evolution 0613 1

8/12/2019 BPO Whitepaper Bigdata Analytics Finance Evolution 0613 1

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The new A and B of the

Finance Function: Analytics

and Big Data

-Evolution of the finance function from

bean counter to business partner

Theres no escaping the fact that finance, and the very nature of its

transactions have changed enormously in less than a decade. The

finance function has grown beyond simply supporting the business

by providing robust forecasting, squeezing liquidity without

affecting the business and refinancing.

Business acquisitions have brought in multiple legacy systems across

businesses with data structures that are out of date or misaligned

with the organizations objectives. Lack of clarity on key businessmetrics lead to data structures not being aligned appropriately.

The finance function is now seen as the custodian of business wide

information, and is pivotal in structuring data sets that align robust

information to the key business metrics. As custodian, the finance

function becomes a conduit between the businesses and external

stakeholders.

Influenced by the rise of global businesses, leaps in technology and

the changing investment scenario, the finance function of today is

proceeding towards a much more significant role.

While organizations have improved the back office operations, the

potential of the finance function to create value by supporting

decision making remains a challenge. The key enablers which can

take the finance function to the desired state of being a business

partner are Big Data and Analytics.

Finance needs to effectively communicate the results of its analysis

of financial information to the operational decision makers.

Operations can help finance implement a more analytical and

decision based approach to complement its traditional quantitative

analysis.

White Paper


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While customary reporting tools can only reveal what happened in

the past, leveraging analytics enables the finance function to

precisely foresee and cater to changing customer needs, business

demands, and other impending events and conditions.

This paper discusses how the finance function will evolve with the

combined forces of Big Data and Analytics and the levers that can

help catalyze this change.


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About the Author

Balaji Venkat Chellam Iyer

Balaji is a member of the Transformation - Finance and

Accounting team within the TCS Business Process Services unit.

He has 17 years of experience across global markets with domain

expertise in Finance and Accounting. He has been part of global

finance transformation and re-engineering projects over the

lifecycle from due diligence - feasibility to implementation andperformance optimization, and has helped clients by developing

solutions specific to their business objectives and needs.


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Table of Contents

1. Overview 05

2. Analytics The Rising Storm 06

3. The Emerging Big 06

4. The Big Impact 07

5. The Finance Function and Predictive Analytics 08

6. Securing The Enterprise Finance Risk Perspective 09

7. Conclusion 09

8. References 09


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Overview1

The finance function of today is much more than the bean counter of the past. This evolution of thefinance function to a business partner allows better focus in application of financial disciplines such asmanaging for value, performance management, risk management or analytics in the decision makingprocess. Managing for value requires a cultural change in the way information is reported, with financeteams providing much more than metrics and statistics. They share insights and assist in focusing thebusiness towards value creation.

Finance as a business partner is responsible for collaborating with the Chief Commercial Officer to plan andimplement strategies, challenge the operations with alternative strategies, thereby maximizing value andaiding the Chief Technology Officer in planning and controlling budgets. Finance professionals bring morethan just financial measurements the business can relate to. They provide invaluable commercial insightsto strategy formulation and implementation based on both their big picture view of the business, as wellas their ability to delve deep and understand the granular detail of every business transaction.

Organizations now have access to unprecedented amounts of data captured through their EnterpriseResource Planning (ERP) systems, Customer Relationship Management (CRM) tools, Point of Saleapplications and the internet. The ability of the organization to convert all of this to meaningfulinformation determines the quality of the decision making process. There are several analytical toolsavailable for predictive performance management, which provide in-built strategic planning, budgeting,forecasting, consolidation and reporting. The current trend veers towards integrating reporting andanalysis. Data and Analytics must be at the core of fact-based decision-making.

In the evolution of the finance function, Big Data and Analytics are the game-changers ahead. Big Data canunravel unseen opportunities for an organization and will help the finance function assist decision makersin several ways.

This paper also draws upon the global trend study conducted by Tata Consultancy Services (TCS) on howcompanies are investing in Big Data and deriving returns from it. TCS surveyed 1,217 companies in ninecountries in four regions of the world (the US, Europe, Asia-Pacific and Latin America) in late December2012 and January 2013. As part of the study, TCS also conducted in-depth interviews with more than adozen executives across industries about their Big Data initiatives. The survey was conducted across allindustries and 13% of the total respondents were from the finance or accounting department acrossindustries.

Figures 1 and 2 give a breakup of the sample by industry and geography.

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1 bean counter' - the term has several different meanings. The common usage these days is as a name for a rather pedantic accountant, the implication being that, while thelayman is content to buy beans by the bag, fussy accountants want to know exactly how many they are paying for.

Hi-Tech, 18.0%

Utilities, 3.4%

Energy &Resources, 3.4%

Life Sciences, 5.1%

Travel/Hospitality/Airlines, 3.0%

Media &Entertainment,

3.0%Manufacturing,

14.0% Insurance, 7.5%

Telecom, 7.8%

Retail, 9.8%

ConsumerProducts, 5.4%

Banking/FinancialServices, 19.8%

North America,48%

Latin America,11%

Asia Pacific, 16%

Europe, 25%

Figure 2 Distribution of Respondents by GeographiesFigure 1 Distribution of Respondents by Industry

Source: TCS Big Data Survey Report Source: TCS Big Data Survey Report


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Analytics The Rising Storm

Analytics strategy is one of the many investment initiatives aimed at making a company more efficient, but it is asignificant one. Financial discipline integrated with analytics strategy can have a multiplier effect on overall returns oninvestment. Further, the resulting insights and smarter decisions also help drive the company forward faster.

Organized data and analytics across the enterprise can create a virtuous cycle of continual, synergistic improvement.Similar to other areas of the business, financial leadership must now be able to respond cogently and convincingly toinquiries about competing on analytics. As the competitive environment becomes more complex, companystakeholders demand in-depth intelligence for more refined risk assessment and to help navigate their responsibilities.Effectively analyzing the market and meeting stakeholder expectations along with sound financial oversight ofstrategic initiatives make the implementation of a forward-looking analytics strategy even more critical to successfulenterprise planning and growth.

Data and Analytics must be at the core of fact-based decision-making. In its simplest form, business translates intothree basic activities: products and promises, selling those products and promises, and servicing the customer.

Creating more value for customers does not mean simply producing and promoting products. Most businesses needto innovate in terms of marketing and services while pricing products as smartly as possible and avoiding riskassessment mistakes. Be it manufacturing dry goods or offering mortgage, insurance or health care services,integrating large data sets with advanced analytics is becoming critical for sophisticated market segmentation andmarket-savvy pricing. More accurate cost predictions can be achieved by several means, but some are more expensiveand less dependable than others.

The systematic implementation of analytics in conjunction with smart decision-making helps minimize risks forcompanies while providing customers with quality products at optimal price points. Some companies are rapidlymaturing in their analytics expertise, replacing internal efforts for similar or better external analytics data and products.For smaller outfits, a highly accurate predictive modeling initiative can be licensed as a product for less than the costof one full-time employee per year. In equivalent terms, that one in-house resource must be able to build datawarehouses, be an expert in the business, create and validate predictive models, determine estimates and managethose models in multiple systems.

A mid-size business can often achieve superior results using externally sourced analytics products that can providebetter value than the output from two or more staff equivalents. The larger the company, the more rapidly it canleverage analytics solutions to reallocate resources to strategic implementations that cannot be efficiently licensed offthe shelf. Simultaneously, such companies gain speed-to-market benefits for their products and build competitive

advantage with their customers as innovators. These same analytics acquisition processes apply to the operations,logistics, maintenance and supply chain functions.

Lack of attention to operating costs translates into higher costs and customers will quickly notice and react. Ifresources dont support initiatives that promote ease of doing business, customers wont even notice because they willalready have found another provider. All these factors are fertile ground for analytics to help increase performance andgrowth. Ultimately, keeping a business grounded in sound financial reasoning and leveraging the science ofpredictive analytics to do so is how companies will thrive or fail.

The Emerging 'Big

The game changer is Big Data and harnessing it using the right analytical tools.

Why has data become such an issue? There are many reasons.


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As market dynamics continue to evolve, expectations continue to shift about what should be disclosed,when and to whom and in this scenario, Big Data is dominating the strategy discussions. The accepteddefinition of Big Data now comprises Volume (amount), Velocity (speed of creation and utilization) andVariety (type and sources of unstructured data). The TCS Big Data survey corroborates that while companiesare still using structured data (51%), as shown in figure 3, there is an increased need to use unstructured orsemi-structured data. The survey also reveals that companies that expect much bigger Returns onInvestments (ROI) on Big Data use more external and unstructured data than do companies expecting loweror no ROI. In a generic sense, Big Data refers to large data sets that cannot be addressed using legacy datamanagement tools. Such data requires next generation techniques and technologies to aggregate, analyzeand visualize.

The 'Big' Impact

According to TCS' global trend study on Big Data initiatives, a little more than half of the respondents saidthey had undertaken Big Data initiatives in 2012. When asked whether Big Data initiatives had improveddecision-making, an overwhelming majority (81%) said they had.

Finance and accounting managers see the most value in Big Data for the two activities: measuring risk andimproving budgeting and forecasting, as shown in figure 4.

Figure 3 - Structured, Unstructured and Semi-Structured Dataacross Industries for Big Data Initiatives

Source: TCS Big Data Survey Report

Structured Data,51%

Semi-StructuredData, 21%

UnstructuredData, 27%

Source: TCS Big Data Survey Report

Measuring risk

Budgeting/forecasting/planning

Determining financing amounts for customers

Identifying amounts for customers

Identifying bad credit risks

Identifying areas of external theft

Identifying accounting irregularities

Identifying areas of internal theft

1 2 3 4 5

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3.6

3.2

3.2

3.0

3.1

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Figure 4 - Potential Benefits of Big Data for Improving Finance/Accounting Activities

1 - No Benefits, 5 - Very High Benefits


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However, there are also many challenges in making the most of Big Data and the value it holds in decisionmaking and bringing agility into the business. Most of all, finance managers need to determine what data theyneed to make different business decisions. This is followed by getting other functions and units to shareinformation and putting the data into meaningful formats so as to facilitate decision making, as shown infigure 5.

Big data can bring to the surface unnoticed prospects for a company and assist decision makers in severalways:

Unravel : Readily available data can reduce search and processing time, adding significant value to anorganization. Making data available to relevant stakeholders in a timely manner can reduce time-to-market and improve quality.

Analyze : Organizations can collect and manage increasingly detailed performance data on every aspect anduse it to analyze variability in performance. This can aid in recognizing the source of the issues andtaking necessary remedial action.

Innovate: Big Data can provide insights to help improve existing products and create new products andservices.

The Finance Function and Predictive Analytics

The TCS Big Data 2013 Survey supports the view that the finance function today is warming up to leveragingBig Data.

Predictive analytics is leveraged to assist the finance function in taking proactive decisions relying on the

patterns and trends they uncover from historical data. The plethora of data gathered by the finance functionincluding information relating to accounts, transactions, demographics and other business functions containvital intelligence that often goes unutilized. Analytics utilizes this Big Data and enables finance to prepare forthe future by learning from the past.

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Source: TCS Big Data Survey ReportFigure 5 - Top Five Challenges for Finance for Getting Value from Big Data

Getting business units to share information across organization silos

Determining what data to use for different business decisions

Putting our analysis of Big Data in a presentable form for making decisions

Being able to handle the large volume, velocity and variety of Big Data

Determining which Big Data technologies to use

1 2 3 4 5

3.3

3.4

3.2

3

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1 - Not a Challenge, 5 - Very High Challenge


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The ability to anticipate illegal or suspicious activities and transactions such as identity theft and moneylaundering can be prevented. Predictive analytics can also be employed to combat newer tactics used byfraudsters.

Unlike traditional reporting tools that only shed light on what happened in the past, leveraging analyticsempowers the finance function to accurately anticipate and cater to changing customer needs, businessdemands, and other impending events and conditions.

Securing the Enterprise Finance Risk PerspectiveEffectively apportioning capital among the contending demands of an organizations operational base goes along way in determining the extent to which the enterprise can compete and fulfill its mission.

How well an organization manages its capital and financial resources is one of the strongest indicators oflongevity and success. Every enterprise of any appreciable size and with significant obligations will followsimilar guidelines when assigning capital offset to prepare for the outcome of an event or plan. Managers in avariety of disciplines finance, risk, audit must deal with the risk associated with their capital allocationdecisions. Clearly, there is a distinct relationship between capital and risk and the analysis that joins them.Improving the use of capital within the organization is crucial, with the use of state-of-the-art predictiveanalytics on financial management contributing greatly in this regard.

Adverse consequences from operational risk can essentially translate into a sudden, unintended increase indemand for capital. Such a situation can immediately drain capital from the companys essential operationsand affect the credibility of the companys financial position. With adverse consequences hanging in thebalance, the expense scenario takes the form of pay now or pay more later. While efficiencies arise primarilyfrom a more effective allocation of capital, the absence of adverse surprises directly resulting from goodbusiness intelligence and a proficient risk management regimen are also critical.

Conclusion

It is evident from the TCS Big Data Survey results that predictive analytics and Big Data are now considered

integral to accomplishing the financial goals within an organization. Together, they can contributesignificantly to reducing cost and risk, while improving business performance.

As market dynamics continue to evolve, the role of the finance function keeps evolving to new levels.However, it is the finance functions responsibility as the business partner and custodian to ensure thebusiness stays rooted in sound financial reasoning. And aiding the finance function in this task will be thecombined forces of predictive analytics and Big Data.

References1. Tata Consultancy Services, The Emerging Big Returns From Big Data A TCS 2013 Global Trend Study (March 2013), accessed May 19, 2013, http://sites.tcs.com/big-data-

study/introduction-key-findings/


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other applicable laws, and could result in criminal or civil penalties. Copyright 2013 Tata Consultancy Services Limited TCSDesignServices

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About Tata Consultancy Services Ltd (TCS)

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BPO Whitepaper Bigdata Analytics Finance Evolution 0613 1