Data in today’s world
Today’s enterprises capture a lot more information about their customers, employees, budgets, spends, market and competition. There is data from the point of sell (like order size, shipment volume, price, revenue, margin details etc.), before sell data (e.g. sales pipeline line, marketing spend across channels etc.) and after the sell data (e.g. customer service information, product usage patterns etc.) All business functions like sales, marketing, pricing, supply chain, finance, risk management, information technology, human resources, etc. have a lot more access to various forms of structured and unstructured data available inside the enterprise and from sources outside. Access to social media and the internet allows enterprises to understand market, social and customer trends, needs and behaviour. There is also an increasing awareness on the emergence of ‘big data’ in today’s world and how it can be leveraged as the new frontier for innovation, competition and productivity. Thanks to the evolution of better storage and computing power in today’s world, data and information is becoming a key asset to enterprises in taking better decisions.
Business Analytics is the science of using these information assets to build a single version of truth of what is happening in the business, identify why it is happening, find controllable drivers to change future strategies and identify extent to which business can change the future results based on the strategy changes. This allows business managers to take more informed and better decisions than just rely on their gut and experience. This also makes the business more predictable and helps realize incremental top-line and bottom-line impact.
Given the challenges that CFOs face to grow revenues, increase profitability and manage compliance issues, the focus today is on getting relevant and timely financial information to make better decisions and take proactive actions to improve the company’s performance. Finance managers have always been savvy with data and numbers. Hence analytics is increasingly gaining attention in the minds of finance managers. Financial analytics is increasingly finding popularity among companies that are looking to improve their financial value proposition and optimise business performance.
Some examples of financial analytics applications
A. Budget and Resource Optimization – Analytics is like a tool that allows finance managers to motivate their peers in marketing, sales, procurement and others to start thinking in a more objective manner and drive strategies to enable better financials for the business. They can get all functions to better understand the available resources, their current utilization, their performance metrics and their relations to business outcome.
- Forecasting – Analytics have bought in the ability for finance managers to use past trends and their drivers to do better forecasting of future scenarios.
- Optimization –Optimization allows finance managers to increase efficiency and effectiveness of available resources based on optimal allocation and utilization given the business constraints.
- Simulation– Simulation allows them to understand likely future outcomes from their decisions without real occurrences and stress test their strategies.
All these analytics techniques allow Finance managers to also become more effective in the organizations. The applications are numerous. Following few will give a flavour of some decisions where finance managers use analytics for better resource planning & optimization.
- Financial budgeting and investment decisions
- Demand, sales and revenue forecasting
- Price setting and discount management
- Capacity planning – e.g. factories, customer service, workforce planning etc.
- Inventory & logistics planning
- Procurement spend reduction
- Portfolio risk management
B. Forensic Analytics – Finance managers are increasing using analytics for identification of non- compliance and potential fraud by independently checking and validating entire collection of transaction data. This allows them to identify underlying fraud patterns by mining large volumes of data, be more precise and accurate in the decisions and be more predictive and prescriptive on potential fraudulent transactions and their impact. The following are areas of analytical capabilities in forensics audit –
- Data augmentation – Identify and integrate structured and unstructured data from different internal and external sources.
- Trend identification – Use data mining techniques to identify drivers of fraud and underlying trends in the data.
- Analytical accounting – Applying data based forensic accounting methods like Benford’s Law, relative size factor test, abnormal duplication test etc.
- Predictive modeling – Build prescriptive solutions to detect potential fraud behaviour.
Such solutions can be deployed in multiple scenarios of forensics audit and accounting across industries and business functions.
Illustrative application in some industries –
- Credit card fraud detection
- Insurance claims fraud
- Cyber risk identification
- Anti-money laundering solutions
- Pharmaceuticals statutory pricing compliance
Illustrative applications in some business functions –
- Tax & accounting fraud
- Vendor risk scoring
- Purchase card compliance
- Channel collusion & sales compensation fraud
- Insider trading
- Misuse of special pricing schemes
C. Internal Audit Analytics –Analytics can effectively optimize the deployment of field audit resources by identifying and prioritizing risk segments, thereby multiplying coverage. The solutions can help drive a significant transformation in the way audit happens today –
- Risk segmentation – Helps to identify, estimate and manage risk rather than just safe guard assets based on physical audits.
- Predictive modeling & pattern recognition – Helps auditors not only identify deviations but also the drivers of the same, such that they can effectively prevent and deter deviations.
- Automated data extraction & analysis – Helps transform manual audit of selected items to end to end business performance assessment.
- Pan-enterprise dashboards– Allows continuous monitoring of the key risk indicators for compliance and controls.
Analytics allows finance function to increase sophistication in more data driven internal audit execution and increase coverage of audit with existing pool of auditors.
Thus analytics is becoming a powerful tool for CFOs to bring in incremental business value to their function and for the business in general. This requires them to invest into the right information assets, talent with expertize to analyse big volumes of data, build capabilities in using more analytical tools and techniques while building on a culture of more data driven decisions across the enterprise. The results are bound to be visible in form of significant and sustained multiplication of business impact based on the information assets.
By Prithvijit Roy. The author is the CEO & Co-founder of BRIDGEi2i Analytics Solutions and can reached at email@example.com.
About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com
The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.
Reprinted after initial publication in Core-2011 by West Bengal State Electricity Finance & Accounts Managers’ Association.