Overview of Data Analytics for Finance Professionals: ICAI

Overview of Data Analytics for Finance Professionals

Why Data Analytics

In today’s information age, data is everywhere and ever-increasing. We are leaving digital footprints when we use the internet right from desktops, laptops, mobiles to wristbands!!

Traditionally businesses have been capturing data from internal accounting, and operations like CRM, payroll, inventory. But in the digital age external data like website traffic, social media feeds, etc. have become equally critical.

Increased data is not just limited to businesses; all government offices, defence offices, scientific research organisations, and other arms of modern society are also looking at ever-increasing data.

Data analytics can be defined as a science that analyses raw data to draw conclusions about the information contained therein.

For a chartered accountant, data analytics is a relatively new concept. To put it simply, data analytics enables an auditor to work on, assess, conclude, manipulate 100% of data, rather than just a sample of the population. However, the use is not just limited to audit and assurance, as we see in the following chapters.

Some Important Terms

A. Database (DB): Data stored in a database is in the form of tables, consisting of rows and columns.

B. Management Information Systems (MIS): Management Information Systems are essentially Reports, designed to address standard and repetitive information required periodically by management for decision making. MIS typically execute predefined queries on the database.

C. Data Warehouse (DW): Data stored in a database in the form of tables, may then be moved to a Staging Area for transformation, and then to a Data Warehouse. Data stored in a Data Warehouse is in the form of virtual multidimensional cubes (not tables). Typically, data in a Data Warehouse is never purged, as the more the data, the better will be the understanding of hidden trends, etc.

D. Artificial Intelligence (AI): Artificial intelligence is a technology which enables a machine to simulate human behaviour. It is based on the experience of the tool, rather than being clearly programmed. So, unlike MIS, AI is expected to give answers to random questions on data.

E. Machine Learning (ML): ML is also a subset of AI. Unlike BI, ML may be used for non-business purposes.

F. Deep learning (DL): DL is a subset of ML, which imitates the working of the human brain without human supervision. It can process data and create patterns similar to the human brain and use them in decision making. It is also known as neural learning, used for detecting objects, recognizing speech and translating language.

G. Business Intelligence (BI): BI is essentially a subset of AI. BI are the tools and methodologies that make use of large business data to make intelligent business decisions. BI uses past and present data to make better decisions for current business operations.

H. Business Analytics (BA): Business analytics uses past and present data to predict future trends. BA tools are used to help make strategic decisions regarding new market opportunities, improving customer relationships etc. Unlike BI which takes a thorough look at past, present data, BA engages human intelligence and individual perspective to arrive at conclusions about the next plan of action. BA has to do with viewpoints and foresight, and that can be very subjective.

I. Advanced Analytics: This data science uses scientific methods and processes to extract insights and knowledge from structured and unstructured data, generally large data. Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond BI and BA, to discover deeper insights, make predictions, or generate recommendations.

Example: COMET Neowise was discovered by analysing data acquired by a space telescope.

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