Technology for Finance and its importance
Over a cup of coffee from the comfort of her home, this finance professional chats with her colleague on the nuances of working from home. She has started to miss office and is hopeful that the Indian summer restricts the spread of the virus. As she chats, her phone buzzes and she has a budget approval request from the facilities cost center for sanitizing the workplace. She logs in to the company’s mobile application and approves the request as a finance manager. The workflow is then triggered and the funds released, for the much-needed sanitization process. She can also access her company’s budget dashboard real-time and take proactive steps to reach out to respective cost centers ensuring the actual expenditures remain within the acceptable variances. She is a seasoned finance professional and has seen her field evolve from approving requests through memos to approving them with a touch screen. Though technology has made her job easier and faster, technology is continuously evolving. Technology has evolved from being an enabler of business to technology being the business itself. Today, technology permeates every part of the business across all industries.
ERP (Enterprise Resource Planning)
At the dawn of the new millennium, it was the ERP (Enterprise Resource Planning) wave, where enterprises leveraged technology by establishing systems of records to run their business processes. All the business functions – HR, Finance, Vendor Management, Facilities Management got linked through an ERP software (SAP, Tally, Oracle, Siebel, PeopleSoft, etc…) to drive efficiency and faster decision making. In this wave, our finance professional moved from approving budget requests over the paper to a workflow-driven ERP system. As the technology evolved further through faster speeds of computing, smarter phones, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), businesses realized that actionable insights can be derived if they linked their systems of record (ERP) with their systems of engagement. The systems of engagement are customer-facing systems like mobile application, chatbots, etc., For example, a mobile application for a bank is the system of engagement, customers can log in to the app and do most of the banking transactions without having to visit the bank. As more and more customers are doing transactions online, gone are those days when banks used to lease large properties and hire a large workforce to serve their customers. Today’s banks are asset-light, thanks to technology they can offer more services at a much lesser cost. As the competition amongst banks increased, banks started to think about the next level for growth – either in terms of driving cost efficiency, grab market share from the competition, and last but not the least on how to add additional revenue channels. They realized “if I know my customers better, I will be able to custom stitch offerings for them” The challenge is how will they know the customer better? The answer is the digital footprint the customer leaves in her online transactions, can the data be fetched and analyzed to understand the customer spending and saving patterns? How much is the customer earnings (Credits) and how much is he spending (Debits) and how much is the saving (month end balances)? Where is the spending going? “Can the bank analyze the spending patterns by type – Is the customer spending more on consumer electronics or on travel or health care? Is the customer saving consistently month on month- Can the bank then advise investment-linked savings plans for the customer from its offerings” Any bank today will have a Data Analytics team which has in its charter to generate actionable insights for the business to drive growth in revenues and profitability. The Data Analytics team gathers and analyses streams of data from multiple systems of engagement and generates actionable insights for business users. Example: The relationship manager (business user) with a bank who has high net worth individuals (HNI) as his clients depend on the Data Analytics team to tell him the products that should be targeted to his clientele.
Technology for Consumer Goods and Services
Similarly, if we look at other industries like consumer goods – Often the biggest challenge the companies face is which of their products is in good demand and in what geography and more specifically in what store? Can I equip my salesman with intelligent information on what goods to sell to a retailer? There is more demand for coconut oil in the southern states than elsewhere is a no brainer, but can the data be analyzed more deeply to gain actionable insights for a salesman, when he goes and sells to the retailer? The answer is Yes, enter the data analyst who gathers reams of data on customer preferences, demographic spreads, and choices, geographic preferences, customer buying patterns at the point of sale (POS) terminals. They collect data from multiple sources like these, analyze them, and direct the salesman efforts to sell more wisely.
The scope of the Data Analytics
The scope of the data analytics is ever increasing as more and more enterprises across industries are linking their systems of engagement with their systems of records in what we call Digital. This Digital transformation has opened up a lot of opportunities for the finance professional, they no longer work in silos in a corporate finance team but are partnering with the business to drive growth and efficiency in the organization. The collaboration with the business has created more visibility for her in the organization and opened up an opportunity to understand the customer touchpoints through systems of engagement, She can now stitch her learnings from customer insights through systems of engagement to the systems of records to generate intelligent data and create compelling narratives of growth and opportunities for the business managers.
So, what does it take to reinvent herself to the new world of digital? Working in the age of Digital demands that she equips herself with key concepts of data analytics leveraging programming languages like Python, R. These programming languages will help her examine, extract and analyze large streams of data to fetch intelligent insights for the business. But what good is an intelligent data if not presented in a way the business understands? Ergo she also needs to devote time to learn visualization tools like Tableau, Microsoft Power BI. These tools transform intelligent data into compelling dashboards for businesses to act upon.
They say change is inevitable – Now is the time that she embraces the fact that she has to reinvent herself – may be unlearned and learn a few things. Fortunately for her, just like the banks and consumer goods companies that are embracing digital transformation, many top universities and business schools are imparting courses on data analytics specifically tailored for finance professionals through online mediums. It’s time she explores these opportunities to be a digital-ready finance professional.