“In God we trust, all others bring data.” — W Edwards Deming
Even before we start diving into the world of data analytics and become a pro, we need to understand the basics. Nobel laureate Ronald Harry Coase was absolutely on point when stated that “Torture the data, and it will confess to anything.” Information is power, and a set of unstructured data becomes solid information when viewed in context and post-analysis. This is why flawless and prompt analyses help in building a successful brand image and eventually enhance business performance.
Experts describe “Data Analytics” to be the process of analyzing raw information, using various qualitative and quantitative methods, to draw conclusions that would eventually enhance business intelligence. Quite a lot of data analytics processes have been automated to optimize the speed and efficiency of businesses. We can call it a “blanket term” because a lot of different methods are incorporated under ‘Data Analytics’.
“Data really powers everything that we do.” – Jeff Weiner
Why Data Analytics?
The answer is simple — well-timed and meticulous Data Analytics help analysis of past data, monitoring of present data in real-time and forecasts future trends across business processes. Below are a few examples.
Gathering Social Media intelligence to optimize marketing campaigns
The digital marketing team of Durex is creating waves when it comes to using social media to the fullest. When they came up with their marketing campaign with the hashtag #IndiaHatesCondoms, they received more than 50,000 replies, out of which 41% of respondents checked the “lack of feeling” box. A perfect example of a large set of data assimilated and analyzed before introducing a new product in the market.
Organizing workload for operational efficiency – Transportation industry worldwide is heavily dependent on data analytics. Transport of London uses data analytics to assess its rail industry alongside their road transport. Sensors provide onboard collects details about trains’ mechanisms and more. On average, information from 100 trains alone could rack up to 200 billion data points annually. So imagine the treasure-trove of data TOL is sitting on to use at their own discretion.
Understanding buyer trends – Retail industry (brick-and-mortar stores) is reliant on big data and analysis now more than ever, especially with serious competition from online stores and experiences. These stores have to one-step ahead to understand the customer’s likes and dislikes so that these customers become patrons of their stores. Lenskart.com adopted customer engagement to come up with a marketing strategy to sell spectacles on virtual stores. The brand has had huge success as now it is backed by Tata and is operational in countries like Australia, UK, and the US along with more than 500 offline stores.
Gaining a competitive edge over the competition – The thorough market study had proven to be rather fruitful for IKEA when they set up their first store in India (Hyderabad). They waited patiently for 12 years, studying the market, the competition, and the Government policies before their launch. To be conducive to the Indian market, they changed some of their strategies and products too. This not only gave them an edge over their competition, but also a solid footing in the Indian market.
And gaining financial wealth – This is self-explanatory. Once all aspects of business fall into place, the corporation is bound to be financially successful. A comprehensive market study and an intensive analysis will lead to appropriate marketing strategies, which in turn will lead to financial success.
“Without a systematic way to start and keep data clean, bad data will happen.” — Donato Diorio
A gamut of methods are involved under “Data Analytics”. Here are some of the most prevalent ones.
Business Intelligence (BI)
Some BI tools are OLAP (Online Analytical Processing), SaaS BI, LI (Location Intelligence), Open Source BI, and Data Visualization. BI is a tech-driven process for analyzing data and present actionable information. BI encompasses various apps that collate data from internal systems and the external sources to prepare a comprehensive report. It can also access historical data stored in the data warehouse to arrive at a more focussed result.
Machine Learning/Artificial intelligence (AI)
Some of the frequently used Machine Learning software are Python, SAS, rapidminer, and R Programming. ‘Machine Learning’ enables an automated analytical DA model based on the idea of artificial intelligence, wherein the program learns and adapts through experience.
Some software companies that pioneer in this field are Dynamics, Cynet Systems, LightCyber Ltd, and DTex Systems amongst others. Behavior analysis talks about user behavior and experience. It helps define the business goals and is designed to detect user preference.
Internet of Things (IoT)
IoT investigates large volumes of data generated by various connected IoT devices. These devices can be anything, right from a heart monitor to a GPS in the car. Basically, any device to which an IP address can be assigned and can transfer data on a network, become an IoT device. It is a complex system as the data collected is huge and heterogeneous in all possible ways.
It is a section of advanced analytics that uses historical and transactional data to create predicted intelligence based on patterns for both structured and unstructured data. Successful application of predictive analytics can give insight regarding the forthcoming risks and interpret the data for the organization’s benefits.
This includes both qualitative and quantitative forms of research. This need not be a machine-based analysis. Qualitative data are analyzed with data preparation, validation, editing & coding; while for quantitative data the first level of analysis is descriptive analysis, and the second and the more complex level is inferential analysis.
Driven by technology and modernization, almost every industry is at the edge of the next level of evolution. Like every other field, technology has crept into the field of finance and accounting too. Being aware of the fact that the market is full of competition and one of the sure-shot ways of getting ahead in the market is a detailed market study to create strategies. Ergo, it was only natural that the application of data analytics in the Finance & Accounts department will emerge and eventually become a necessity.
A successful finance team plays a crucial role in enabling the growth of any business and wealth generation in general. Data analytics has almost become synonymous to the accounting careers now plus it is also true that accountants make the best of data scientists, exclusively because of their technical skills. Tax accountants use analytics for calculating complex taxation related to investment scenarios and the share market. Auditors are slowly moving on from sample-based data to monitoring large volumes for higher accuracy, while accountants are becoming reliant on data analytics to uncover insights and predict and manage risks.
Having the technological know-how and being ‘future-ready’ only helps to increase the person’s market value. It is obviously note-worthy to understand that such skill sets often pay superior and offer larger growth opportunities for a professional.
At Miles Education, we believe that all students/professionals should be ready for this future and our data analytics certification courses are in tangent with our thoughts. So, build on your analytical skills and develop your acumen for technology. You will understand that being an accountant is so much more than just balancing sheets and filing returns. It is the basis on which organizations build their economic health.
Disclaimer: Miles Education is the official partner of IIML plus Wiley’s recommended Data Analytics program for accounting & financial professionals.
“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice” — Stephen Few