Demystifying Big Data and Analytics and the Way Forward
By: Khan Muhammad Saqiful Alam
Tags: Big Data, Tutorial, Analytics, Big Data Analytics

In recent times, Big Data and Analytics are two of the common terms that pop up on a regular basis, in blogs, vlogs, career advice, skill development sessions and in stories of organizations coming up with some nifty new products. But, despite the heavy chatter, questions such as “What exactly is the difference between the two?”, “How to learn these skills?” and last but not the least, “Is it really a business specialization? Why would a business graduate learn to code?” are yet to be properly answered, at least from the perspective of Bangladesh.

Suppose you are a managing a supermarket, with the likes of Meena Bazaar or Agora. You want to know how to locate the beef, poultry, and the egg aisles. One way to do that might be to rely on your experience in the other stores and design a layout like that. Another way might be that you collect some information. You already have the video footage of the customer movements and the sales data of your store. All that is left is to change different types of layout and see how the sales pattern and the sales amount change. If changing layout of a store might be unfeasible, try having 4/5 different stores with different layouts, and see the purchasing pattern. The layout that maximizes the sales for over a long period of time should be the answer to your question. When we are looking at a managerial decision-making problem where we are collecting the data and then using tools to analyze the data to come to the insights, we are going through the whole process of business analytics. If you collect the data over 6 months, across 10 different stores, you will have a very big repository of both numerical and visual data. Such a large amount of data is called Big Data (honestly, this would a very small Big Data file), and when you are using Big Data to answer the layout question, the whole process becomes Big Data Analytics (BDA). This is the simplest way to look in to the difference between the two terms.

Now, whether we need our business graduates to have such a skill or not, that’s another pressing question. Personally, I believe, the skill to answer questions based on evidence from data always helps, and although it does not replace experience or intuition, it supports them, in making a solid decision. In slightly more quantitative business functions like operations and supply chain, data is one of the keys in daily decision making. Data on demand, supply route timings, inventory levels are required every single day to help in production and help in keeping the market well supplied. In fact, operations and supply chain management are the functions that have been making use of BDA even before the term was coined and gained popularity. Enterprise Resource Planning (ERP) software have been the backbone of many large organizations, even in Bangladesh. Another data driven function is that of finance. Although classical financial models already bank heavily on data, BDA has had some very interesting impact Finance. For example, a stock investor knows that news regarding a company creates fluctuations in the stock market often. He can use web mining techniques to have a program that can collect the news headlines related to his stocks every single day, and see how the news is affecting the stock fluctuations. So, he can use the news to predict possible fluctuations and can make some quick bucks, if his decision making is quick enough. This is just one out of many examples of how analytics (in this case its Predictive Analytics) that are revolutionizing the field of finance.

From my experience as a lecturer, I have seen people relate Marketing and Human Resource Management functions of business as more qualitative and intuition-experience driven. But think of this scenario — you oversee a very large RMG factory and you want to see whether the employees are being efficiently deployed in the manufacturing process. In such a case, you can collect data on employee activities and investigate patterns of low productivity. But that is not the limit. Say you have a comment box for employees and supervisors and you receive many comments. The beauty of BDA is that it’s not only limited to numerical data but also spans over to audio-visual and textual data. You can bring all these together to monitor what is hindering your employee motivation, or decide on the employees to promote.

The field of marketing is where BDA is bringing on some interesting changes. Most marketing firms and agencies have been open about the fact that beside creativity, communication, and intuition, data is the next key to be successful. BDA can help companies find out the behavioral patterns of the customer, the way they interact with the promotions of a company, the way they interact with the ecommerce portals, how they purchase products, what design factors or product features lead to a successful sale and much more interesting insights. And the flow of information is not only limited to just purchase information and customer databases — tools such as social media analytics can give information what the social media is talking about the company or its campaigns right now and with proper planning, social media analytics can be paired with BDA to marketing communications that can go viral!

These are just a few of the examples of how BDA is involved with the regular functions of a business. But then comes a harder question — how should a business graduate develop the skills to BDA. Frankly, a level of programming knowledge is required, as two of the most powerful analytics tools are R (a data coding language) and Python (a versatile programming language). So far, our business graduates and managers are exposed to analysis using Excel and SPSS (two of the most common software being used for data analysis), but these have their limitations. Coding helps to develop logic and exposes a whole new area to work on data analysis. With the help of these codes, graduates can work with massive datasets and can mine and harvest data from the internet, and develop their own data analysis programs. And these are not that hard to learn, they are all English based codes, and most of the commands are in English. There are loads of tutorials available all over the net. The best places to learn BDA is to start to learn R and Python through guides from Codeacademy and Analytics Vidhya, and these are all free. You can also learn in a more structured way through courses online from Coursera and EdX, or from training courses that are being offered in many places in Dhaka. But besides doing tutorials and doing courses, they key to learning BDA is through the ‘getting your hands dirty’ approach. There are websites which give access to large datasets, such as Kaggle.com and DataWorld.com, and the ideal thing to do is to download a dataset and start playing with it! Skilled candidates in BDA are actually very rare in Bangladesh compared to our neighboring country and Asia, but the demand is out there, and it will increase very soon. So my advice as an educator, trainer and a researcher in BDA will be that we need to start taking steps towards learning BDA and implementing it in our work fields, and better now than later!