Data Analytics, Data Science, Big Data. These are not just buzzwords, but catalysts in growing businesses.

Social networking has become an important part of our lives, where an increasing number of users shop, post messages, tweet and surf. Their online activity logs contain huge information. Similar is the case with sensor-based monitoring, satellite-based observations and Sensex-based logs. All these generate data at a high rate and extracting information from these logs is not an easy task. Those who can do so are called ‘data scientists’ or ‘business analysts’. These data scientists not only analyse existing data, but can also predict and forecast. The biggest challenge lies not in analyzing existing data but in data cleaning.

There is a huge dearth and thus demand of data scientists. Keeping this demand in view, the top universities have come up with specialized program to create such professionals. Even the government has identified this need and has started increasing the awareness among academia through the Department of Science & Technology, which recently sponsored faculty development program on Big Data Analytics across India. Recently there was a report that data scientists earn more than chartered accountants and engineers.

Clearly, the field of Data Analytics has huge potential!

Finance leaders are dealing with a significant shortage of accounting and finance professionals who possess the technical and non-technical skills required for data analytics initiatives. But finding such professionals has proved to be difficult, making it challenging for leaders to recruit, develop and retain people who possess these attributes.

As technology continues to evolve, it promotes changes to business models and surprises those who are un-prepared. Businesses change their strategies and the way they operate. New threats and opportunities arise. Thus, in such an increasingly data-driven world, CAs need to be able to adapt to these technological disruptions. Some tasks that require skills in data analytics:

• Financial planning and analysis in hopes of discovering the best course of action for Companies.
• Finding patterns in customer behaviour and market trends to drive Company’s strategy.

Meanwhile, mastery of data analytics can help businesses generate a higher profit margin and gain a meaningful competitive advantage. Some experts even predict that companies ignoring data analytics may be forced ‘out of business’ in the long run.

As the price of computer hardware and cloud services has been ever-decreasing, the element that is blocking the way of companies in becoming more data-driven is the ‘human element’.

Data analytics is often misunderstood as descriptive analysis (“what is”) only. The real value, however, lies in predictive (“what will be”) and prescriptive analysis (“What should we do?”). Data analytics is highly relevant as companies and industries transform to take advantage of technological innovations and as expectations of regulators and investors with regard to data availability and analysis are increasing.

Many accountants already use descriptive analytics in their daily work. They compute sums, averages, and percent changes to report sales results, customer credit risk, cost per customer and availability of inventory. Accountants also are generally familiar with diagnostic analytics because they perform variance analyses and use analytic dashboards to explain historical results. This however is not sufficient.

Predictive analytics and prescriptive analytics are now required because they provide actionable insights for companies. Accountants need to increase their competence in these areas to provide value to their organizations. Predictive analytics integrates data from various sources (such as enterprise resource planning, point-of-sale, and customer relationship management systems) to predict future outcomes based on statistical relationships found in historical data using regression-based modeling. One of the most common applications of predictive analytics is the computation of a credit score to indicate the likelihood of timely future credit payments. Prescriptive analytics uses a combination of sophisticated optimization techniques (self-optimizing algorithms) to suggest the most favorable courses of action to be taken.

Skills for a Data-Driven Practice

Producing analytics starts with understanding the business objective (“What are the key questions that you expect the analysis to answer?”) and identifying and obtaining relevant internal and external data sources to

support the analysis. Producing analytics often occurs at the junior level. The ideal “analytically skilled” employee has these three characteristics:

• Good technical skills: Understands the data and knows how to deal with it.
• Understanding of the business context: Can distill a business problem or opportunity into key questions to be answered and understands the business data flow and the relationship between objects within the business context.
• Analytical mindset: Possesses an inquiring nature and intellectual curiosity.

As this ideal employee is a rare find, companies adapt by building teams of various specialties and technical skills. At larger accounting firms, analytics is used regularly in tax, auditing, consulting, and risk management.

Data analytics is a skill that can be applied to many scenarios across all service lines. Employees who have this skill are therefore both very versatile and valuable to the organization.

The work of CAs will advance in the future to provide more data analysis, consulting, and decisionmaking support services. The audit function in particular will undergo a significant change with the incorporation of data analytics techniques. Data analytics can thus provide an important business opportunity for CAs and accounting firms.

Challenges and Opportunities

The advent of data analytics offers both challenges and opportunities for CAs. The challenges include undertaking appropriate training to develop the skills needed to initiate and support data analytics activities as well as altering the present audit model to include appropriate audit analytics techniques. The opportunities include a technology-rich audit model that provides for greater thoroughness, efficiency, and accuracy, as well as new business opportunities to provide data analytics expertise to CAs clients and organizations. CAs, whether working in public practice or industry, will enhance their career opportunities through the acquisition of additional data analytics expertise.

That’s where Big Data and data analytics could come in—concepts that have the potential to change your practice and the services you provide to clients in a positive way.A quick Google search will show you that there are many books, articles and videos that describe Big Data and its impact today (and tomorrow) on businesses. I won’t try to repeat all that information here, but instead, I’ll distill down some information on Big Data as it applies to data analytics, and give you some things to think about as a tax and accounting professional.

What is Big Data?

There are many definitions of Big Data, but the generally accepted view is that Big Data is a set of data that’s too large to use standard data analysis tools or methods to analyze.

Use of Big Data by large companies like Amazon, Target and Netflix for consumer purposes is common today. It helps these companies provide better service to their customers, target new offerings to the right person at the right time, make recommendations for new or complementary products and much more.

“Which is all fine, but what does that mean to me and my firm?” you ask.

Well, in order to understand the relevance, it’s important to understand the connection between Big Data and the data analytics that come from it — and ultimately, the insights and actions derived from those analytics.

What are Data Analytics?

Simply stated, data analytics are what you learn from an analysis of data or data sets, whether you “crunch the numbers” manually or use data analytics tools.

When combined with Big Data, we see data analytics as technology tools that review those huge sets of data in order to gain insight.

Some Important Data Analytic in Microsoft Excel tools are:-

Sorting – It is the process of arranging data in ascending or descending order. Example – finding blank sales in the amount column of your worksheet.

Filter – It displays data as per required parameters. Example – applying filter to display transactions above a particular amount.

Pivot Table – It is used to arrange and summarize complex data into a table. Example – finding the Minimum and Maximum sale price for each stock item.

Lookup – It looks at a value in one column and find its corresponding value on the same row in another column. Example – finding the corresponding sales limit for each employee from another sheet.

CAAT Tools

Gaps & Sequence Check – Detect gaps in a numeric / date / alphanumeric sequence. Example – finding gaps in Invoice Numbers.

Identify Duplicates – It finds duplicate values in the selected columns. Example – Finding duplicate Invoice Numbers.

Top / Last X – It is used to extract the topmost / bottommost ‘x’ number of records. Example – extract top 10 sales data from selected data.

The advent of Big Data doesn’t change the fact that analytics have been used by firms in the profession for years. Practitioners have provided information to their business clients with benchmark data that compares them to other businesses or industries that range from similar to “best in class.”

One of the key aspects of the advisor role that firms play is to help their clients make sense of financial data in order to improve their financial performance, look for opportunities to improve efficiencies or add a new line of business.

In the near future, the availability of significantly more data — and analysis of that data — will help differentiate your firm from the firm that doesn’t embrace data analytics in their practice.