Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.

1. The new‘A and B’of the
Finance Function: Analytics
and Big Data
-Evolution of the finance function from
bean counter to business partner
There’s 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businesses with data structures that are out of date or misaligned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 organization’s objectives. Lack of clarity on key business
metrics 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

2. 2
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.

3. 3
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 and
performance optimization, and has helped clients by developing
solutions specific to their business objectives and needs.

4. 4
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

5. Overview
1
The finance function of today is much more than the‘bean counter’ of the past. This evolution of the
finance function to a‘business partner’allows better focus in application of financial disciplines such as
managing for value, performance management, risk management or analytics in the decision making
process. Managing for value requires a cultural change in the way information is reported, with finance
teams providing much more than metrics and statistics. They share insights and assist in focusing the
business towards value creation.
Finance as a business partner is responsible for collaborating with the Chief Commercial Officer to plan and
implement strategies, challenge the operations with alternative strategies, thereby maximizing value and
aiding the Chief Technology Officer in planning and controlling budgets. Finance professionals bring more
than just financial measurements the business can relate to. They provide invaluable commercial insights
to strategy formulation and implementation based on both their‘big picture’view of the business, as well
as 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 Enterprise
Resource Planning (ERP) systems, Customer Relationship Management (CRM) tools, Point of Sale
applications and the internet. The ability of the organization to convert all of this to meaningful
information determines the quality of the decision making process. There are several analytical tools
available for predictive performance management, which provide in-built strategic planning, budgeting,
forecasting, consolidation and reporting. The current trend veers towards integrating reporting and
analysis. 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 can
unravel unseen opportunities for an organization and will help the finance function assist decision makers
in several ways.
This paper also draws upon the global trend study conducted by Tata Consultancy Services (TCS) on how
companies are investing in Big Data and deriving returns from it. TCS surveyed 1,217 companies in nine
countries in four regions of the world (the US, Europe, Asia-Pacific and Latin America) in late December
2012 and January 2013. As part of the study, TCS also conducted in-depth interviews with more than a
dozen executives across industries about their Big Data initiatives. The survey was conducted across all
industries and 13% of the total respondents were from the finance or accounting department across
industries.
Figures 1 and 2 give a breakup of the sample by industry and geography.
5
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 the
layman 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%
Consumer
Products, 5.4%
Banking/Financial
Services, 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

6. 6
Analytics – The Rising Storm
Analytics strategy is one of the many investment initiatives aimed at making a company more efficient, but it is a
significant one. Financial discipline integrated with analytics strategy can have a multiplier effect on overall returns on
investment. 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 to
inquiries about competing on analytics. As the competitive environment becomes more complex, company
stakeholders 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 of
strategic initiatives — make the implementation of a forward-looking analytics strategy even more critical to successful
enterprise planning and growth.
Data and Analytics must be at the core of fact-based decision-making. In its simplest form, business translates into
three 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 need
to innovate in terms of marketing and services while pricing products as smartly as possible and avoiding risk
assessment 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 and
market-savvy pricing. More accurate cost predictions can be achieved by several means, but some are more expensive
and less dependable than others.
The systematic implementation of analytics in conjunction with smart decision-making helps minimize risks for
companies while providing customers with quality products at optimal price points. Some companies are rapidly
maturing 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 cost
of one full-time employee per year. In equivalent terms, that one in-house resource must be able to build data
warehouses, be an expert in the business, create and validate predictive models, determine estimates and manage
those models in multiple systems.
A mid-size business can often achieve superior results using externally sourced analytics products that can provide
better value than the output from two or more staff equivalents. The larger the company, the more rapidly it can
leverage analytics solutions to reallocate resources to strategic implementations that cannot be efficiently licensed off
the 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. If
resources don’t support initiatives that promote ease of doing business, customers won’t even notice because they will
already have found another provider. All these factors are fertile ground for analytics to help increase performance and
growth. Ultimately, keeping a business grounded in sound financial reasoning — and leveraging the science of
predictive 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.

7. 7
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 accepted
definition of Big Data now comprises – Volume (amount), Velocity (speed of creation and utilization) and
Variety (type and sources of unstructured data). The TCS Big Data survey corroborates that while companies
are still using structured data (51%), as shown in figure 3, there is an increased need to use unstructured or
semi-structured data. The survey also reveals that companies that expect much bigger Returns on
Investments (ROI) on Big Data use more external and unstructured data than do companies expecting lower
or no ROI. In a generic sense, Big Data refers to large data sets that cannot be addressed using legacy data
management tools. Such data requires next generation techniques and technologies to aggregate, analyze
and visualize.
The 'Big' Impact
According to TCS' global trend study on Big Data initiatives, a little more than half of the respondents said
they had undertaken Big Data initiatives in 2012. When asked whether Big Data initiatives had improved
decision-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 and
improving 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-Structured
Data, 21%
Unstructured
Data, 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
3.6
3.6
3.2
3.2
3.0
3.1
2.9
Figure 4 - Potential Benefits of Big Data for Improving Finance/Accounting Activities
1 - No Benefits, 5 - Very High Benefits

8. However, there are also many challenges in making the most of Big Data and the value it holds in decision
making and bringing agility into the business. Most of all, finance managers need to determine what data they
need to make different business decisions. This is followed by getting other functions and units to share
information and putting the data into meaningful formats so as to facilitate decision making, as shown in
figure 5.
Big data can bring to the surface unnoticed prospects for a company and assist decision makers in several
ways:
Unravel : Readily available data can reduce search and processing time, adding significant value to an
organization. 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 and
use it to analyze variability in performance. This can aid in recognizing the source of the issues and
taking necessary remedial action.
Innovate: Big Data can provide insights to help improve existing products and create new products and
services.
The Finance Function and Predictive Analytics
The TCS Big Data 2013 Survey supports the view that the finance function today is warming up to leveraging
Big 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 function
including information relating to accounts, transactions, demographics and other business functions contain
vital intelligence that often goes unutilized. Analytics utilizes this Big Data and enables finance to prepare for
the future by learning from the past.
8
Source: TCS Big Data Survey Report
Figure 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
2.9
1 - Not a Challenge, 5 - Very High Challenge

9. 9
The ability to anticipate illegal or suspicious activities and transactions such as identity theft and money
laundering can be prevented. Predictive analytics can also be employed to combat newer tactics used by
fraudsters.
Unlike traditional reporting tools that only shed light on what happened in the past, leveraging analytics
empowers the finance function to accurately anticipate and cater to changing customer needs, business
demands, and other impending events and conditions.
Securing the Enterprise Finance – Risk Perspective
Effectively apportioning capital among the contending demands of an organization’s operational base goes a
long 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 of
longevity and success. Every enterprise of any appreciable size and with significant obligations will follow
similar guidelines when assigning capital offset to prepare for the outcome of an event or plan. Managers in a
variety of disciplines — finance, risk, audit — must deal with the risk associated with their capital allocation
decisions. 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 predictive
analytics on financial management contributing greatly in this regard.
Adverse consequences from operational risk can essentially translate into a sudden, unintended increase in
demand for capital. Such a situation can immediately drain capital from the company’s essential operations
and affect the credibility of the company’s financial position. With adverse consequences hanging in the
balance, the expense scenario takes the form of‘pay now or pay more later.’While efficiencies arise primarily
from a more effective allocation of capital, the absence of adverse‘surprises’directly resulting from good
business 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 contribute
significantly 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 function’s responsibility as the business partner and custodian to ensure the
business stays rooted in sound financial reasoning. And aiding the finance function in this task will be the
combined forces of predictive analytics and Big Data.
References
1. 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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