If the C-suite were to shape a rock band focused on the standard positions, the guitarist would be the ambitious CEO, and the resourceful COO would play lead guitar. The level-headed CFO will possibly be positioned as the guitarist, a significant band member, but put in the background and tasked primarily withholding the band on track to help the other members shine.
This perception of the CFO as a back-office number cruncher who controls schedules monitors costs and maintains the lights on might have been accurate in the past, but the new CFO is squarely at the heart of the corporate strategy. Data’s core position in today’s business climate is the impetus for this transition.
Today the CFO is the company’s co-pilot, finding the most successful clients, evaluating risk through scenario preparation, measuring customer loyalty through data collection, and designing new KPIs. Corporate boards are continually considering a future CFO in terms of whether he or she will take over as CEO eventually.
CFOs should have global and diverse experience, be up-to-date on technology, be able to recognize and recruit the right talent and, most importantly, know how to lead, as per one of the KPMG Global CEO Survey.
The study also found that 85 percent of CEOs agree that the most significant strategic advantage a CFO can bring to a company is to use financial data to achieve sustainable growth.
CFOs need new enterprise performance management (EPM) tools to serve this strategic role— and many see the cloud’s ability to unleash the power of their data and turn their business into an analytics powerhouse. CFOs and the finance department will need a live view into all business areas, with resources that allow them to provide real-time analyzes of changing situations, suggest actions, and offer effective strategic planning and forecasting.
In a recent CFOs survey of Oracle as well as other business leaders, 90 percent of the executives said that the ability to create data-based insights is very crucial to the success of their organization. Still, more than half questioned the strength of their organization to handle large data inflows.
So the more data an organization uses, the more reliable the research will be. So, almost half of the financial decision-makers in Europe and the Middle East, for example, expanded the number of data sources they evaluate to better understand the effect of this surprising change after the Brexit vote.
In business, where you always stand depends on where you are seated, and the finance department is well placed to offer a holistic view of the company. The CFO’s ability to link key areas around the enterprise— marketing, supply chain, manufacturing, services, and human capital management — to build a holistic, real-time business image is vital to risk management and value creation. That calls for the right resources.
The ubiquitous spreadsheet is one adversary of such real-time analytics. Consider how an annual budget is produced by the finance department of the business or any department within the organization. The budget process is mostly done through a series of spreadsheets that are sent to various stakeholders, with the usual concerns: Is this the latest version? Who made the most recent alterations? Was the data correct, or have the consolidation process made mistakes? Usually, the finance department spends most of its time tracking down and checking the data— and not enough time evaluating it.
Due to the many data systems and reporting tools acquired over the years, organizations rely heavily on spreadsheets and Data Analytics in Finance to organize the information. Because data is siloed in their respective units, to build budgets and strategies, LOB members must first dig into the data. Finance then spends massive amounts of time testing and rolling this unconnected data into more detailed predictions and plans.
Finance teams with Data Analytics in Finance need to build better models for financial and organizational improvements if businesses are to stay ahead of the market. Today’s digital finance team is moving from simple, traditional transaction analysis to more sophisticated predictive analysis, such as statistical-based modeling, dynamic market management, and risk-adjusted business simulations. To do so, they need access to a centralized data system that drills both intensely across transactional data, and broadly through core functional divisions of the organization.
Finance companies need to use analytics that interacts with cross-functional drivers such as customer loyalty, process management, and business decision-making. And, unlike in the past, these observations are obtained in real-time, not just at daily reporting times— providing a continuous view of the company’s birds.
In addition to having a profound impact on existing business models, digitization and globalization have also changed the way we evaluate business performance. Today, intangible assets like brands, customer relations, intellectual property, and expertise have become the primary drivers of the overall success of a business. Measuring the success of a company in all of these fields involves data from around the organization.
It is a challenge for finance to track these non-financial key performance indicators (KPIs) with the same degree of methodological rigor it gives to financial metrics— like productivity and return on investment.
A new report by the American Institute of CPAs and the Chartered Institute of Management Accountants on financial leaders found that the most forward-thinking CFOs are more likely to monitor non-financial KPIs such as talent pool, customer experience, business process performance, brand credibility, and competitive intelligence; Therefore, sustainability and social responsibility are also increasingly relevant for consumers, workers, and the result, and are steps that CFOs will recognize
What’s unique in monitoring this information is not just that the data is non-financial; it’s unstructured too. Many of the data regarding brand credibility and consumer loyalty may come from social media, for example.
CFOs need to rapidly track, analyze, and evaluate unstructured data and collaborate with organization-wide subject matter experts to develop new performance metrics that incorporate this data. As a result, KPI goals will more accurately reflect actual performance metrics that drive the company, and appraisal evaluations will more accurately gauge the employees who operate the company. Working closely with HR, marketing, sales, and other core departments, financial leaders can help move the company from silo-based performance metrics to more objective assessments that take into account a variety of factors. These factors, in effect, can change the strategy and emphasis for those lobsters using big data analytics.
The need for real-time access to these data has led to the growing adoption of cloud-based analytics apps. But with increasing demands for finance to help formulate plans for LOBs and the business as a whole, finance professionals need more than data access; they need to be able to check business models and scenarios rapidly. And LOB managers need resources to make more efficient real-time decisions and to ensure that their operating plans are connected to the financial plans of the business.
Companies with cross-functional experience and linked networks would have a tremendous competitive advantage in the digital era.
Oracle EPM Cloud offers finance departments the power to track and improve efficiency in organizations of all sizes, report reliably, prepare efficiently, and link the entire company.
Data analytics is analyzing the results. It’s the science of taking raw data and formulating it understandably, allowing the knowledge to conclude. The amount of information that can be gathered by technology is overwhelming; the information will mean nothing without data analytics and data science. Data analytics–whether by human-driven or automated processes–helps to collect and process this mass of information for human consumption.
Ideally, data analytics should be used to draw hypotheses and help a firm identify patterns and indicators which would otherwise be lost in the mass of knowledge. Businesses may then use this knowledge to develop in several fields. Data analytics can be used both inside the enterprise and outside it; organizations use it to enhance internal processes, identify consumer dynamics and customer desires, and enhance products and services.
Let’s discuss CPA Exam scores and how candidates arrive at those scores to get a better understanding of big data analytics. There are four parts of the CPA Exam, and each section is taken by thousands of applicants each year. Each occurrence has its unique score associated with particular applicants and a specific test. At a high stage, we have thousands of lines of unstructured data from different sources of data, which does not mean anything. Nevertheless, if we take this data and break it into average scores per test period, we get concrete results that can help us make CPA Exam decisions.
We may also add more variables to increase the amount of data we obtain, such as the total time spent learning or the course used for analysis. It raises our data points exponentially, but if we use data mining to analyze the data to generate useful information, we can easily see the average research time per candidate per test, or what evaluation course seems to help more candidates pass the examination.
If the raw data is translated via data analytics into a readable and accessible format, conclusions can be drawn, and action items can be generated. Nevertheless, merely looking at the data through a text-based lens isn’t always the best way to understand patterns.
Software visualization represents software through charts, graphs, or other visual aids, allowing easy identification of trends in the data. Adequate visualization of data uses the best form of visible support for the data, as well as colors and patterns that attract the viewer. When big data continues to become a more significant part of the business environment, and millions of rows of data are generated daily, it is becoming increasingly necessary to communicate the most valuable information.
If we look at our example of the CPA Exam score from above, we can see it in two ways. The first approach is a dataset or data point chart. For instance, let’s assume we have a list of condensed data showing the average score for each exam over the last five years in each test window. The listing will undoubtedly provide us with information, but it may not allow us to see quickly which quarters have the highest scores on each test. When we bring this information into a line graph, we may notice that the Q2 and Q3 test windows have higher pass levels than the Q1 and Q4 test windows.
All sectors are powered by big data in the current business climate, like accounting. All public accounting and company accountants need to learn how to deal with data to make sound business decisions and meet customer demands. Here are many ways for clients within an enterprise to use big data analytics.
Accountants operate from health care and entertainment and non-profits in every sector. Each of these industries needs to evaluate business performance on an ongoing basis if they want to remain safe and profitable. While analyzing financial reports, accountants may use data analysis to ensure the business is working smoothly, meeting targets, and sustaining or enhancing performance. This awareness is necessary for profitability as well as the survival of a company.
CPAs, CMAs, and anyone who works on a company’s financial or accounting side need to learn how to deal with risk. Danger may come from several areas within and outside the organization. Customer retention, company asset security, and internal controls are only a few examples of places where risk is measurable and manageable. In having comprehensible data points to work with, accountants may analyze a company’s various risk areas and use predictive analytics to make strategic decisions about particular risks, leaving a better future for the company.
Public accountants, trade, or a small business capability both have clients. When looking at variables such as the time it takes to conduct an audit, the turnover of tax returns, or general customer satisfaction surveys, data analytics may be used to enhance the customer experience. It helps accountants and businesses attract and bring in new clients.
One of the most important things that a CPA candidate or potential accountant should do now to become more trained in data analytics is to get to know the methods used to evaluate and interpret the data. Excel is one of the most popular data processing software, and getting a good understanding of how the tool currently operates will give you an excellent foundation for your future as an accountant.
Financial technology, popularly known as FinTech, is one of technology innovation’s fastest-growing fields and is a favorite among venture capitalists. Fintech refers to a group of innovations that concentrate on innovative ways in which customers can access banking and financial services. If you make a purchase online using PayPal, Amazon Pay, or your credit card, you, the user, the eCommerce company, and the bank are all using Fintech to make the transaction possible. Throughout time, almost all facets of financial services, including payments, deposits, consumer finance, insurance, securities settlement, and cryptocurrencies, have developed and challenged Fintech.
Fintech firms rely heavily on machine learning, artificial intelligence, predictive analytics, and data science to improve financial decision-making and offer superior solutions. Let’s take a few examples of fintech and how one uses data science in each.
Robo-advisors are digital platforms which provide investors with an algorithm-driven, automated financial planning, and investment services. Most of the process is technology-oriented, and algorithms arrive at the investment decisions. Human interaction is limited or not in the process. Usually, the process starts with the collection of client information via an online survey. Here, the client’s profile, such as financial status, risk ability, potential financial objectives, etc., is collected. Then the data is used to provide financial advice or to automatically invest client assets in instruments and asset classes that are better suited to their needs and goals.
The methods used in data science can be used to detect financial transaction fraud. Fraud detection has historically been based on a statute, and the rules for flagging a transaction had to be set manually. We can now exploit Big Data and Data Mining techniques where massive volumes of fraudulent online transactions can be used and modeled in a manner that will allow us to detect or foresee fraud in the future. It can be achieved using techniques of data science and machine learning, including Deep Neural Networks (DNNs).
Banks, as well as financial institutions, can use external and internal customer data to create detailed customer profiles. It can be used to tailor customer experience and provide highly personalized services. For instance, an algorithm could be constructed to predict what additional goods or services the consumer would want to buy based on their historical purchasing behavior. Or, for example, what kind of product a particular age group of people should be promoting.
Another major consumer of data science is the insurance industry—nearly every time the insurer uses data science to control its risk and keep its company profitable. As an example, in an insurance company, the claims department uses data science algorithms to distinguish fraudulent transactions from non-fraudulent ones. Insurance firms are now using big data analytics and big data for other uses, such as credit ratings, customer acquisition, marketing, customer retention, and new insurance product design.
Although these are some examples of how data science can be used at Fintech, its implementation has intact -free possibilities.
The overwhelming abundance of data and the rising complexity of technology continues to change the way businesses work and compete. In the last few years, the processing of 2.5 quintillion bytes of data regularly has created 90 percent of the world’s data. Commonly known as big data, this exponential growth and storage generate possibilities for organized and unstructured data collection, processing, and analysis.
After the big data, 4 Vs., companies are using data and analytics to gain valuable insight and make better business decisions. Industries that have embraced the use of big data include, to name a few, financial services, telecommunications, marketing, and health care. Big data usage is beginning to redefine the dynamic market environment. An estimated 84 percent of businesses agree that those without a plan for analytics run the risk of losing a competitive advantage in the industry.
In particular, the financial markets have widely embraced big data analytics to make better investment decisions with predictable returns.
One may categorize big data as unstructured or structured data. Unstructured data is unorganized knowledge that doesn’t fall into a predetermined pattern. It includes data collected from social media sources that help institutions gather customer needs information. Structured data is knowledge already maintained in relational databases and spreadsheets by the organization. Consequently, the different types of data need to be actively handled to make informed business decisions.
Institutions will more efficiently curb algorithms for integrating vast quantities of data, using vast volumes of historical data to backtest strategies, thereby making less risky investments. It allows users to recognize keeping valuable data as well as discarding low-value data. Since algorithms can be built with structured and unstructured data, better trading decisions can be made by combining real-time news, social media, and stock data in one algorithmic engine. Unlike decision-making, which can be affected by different knowledge sources, human experience, and bias, algorithmic trades are conducted on financial models and data alone.
Especially inside financial services, Data Analytics in Finance, most criticism relies on data analysis. To get reliable results, the sheer volume of data demands greater sophistication of statistical techniques. In particular, critics overrate the signal to the noise as patterns of false associations, solely by chance reflecting statistically reliable outcomes. Similarly, theory-based economic algorithms usually point to long-term investment opportunities due to historical data patterns. The underlying difficulties in predictive models are the efficient generation of outcomes promoting a short-term investment approach.
In this piece, we have discussed how Data Science In Accounting is helping accountants become better and smarter.