Khan Muhammad Saqiful Alam, Lecturer, North South University Department of Management

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Khan Muhammad Saqiful Alam is a Lecturer at North South University in the Department of Management from 2013. He graduated from the Institute of Business Administration in 2011 and obtained a Masters from Alliance Manchester Business School, UK. He is also an advisor for Robi 10 Minute School, an online education platform. He has also been a Corporate Trainer for BD Jobs since April 2015 and takes great interest in educating people about Big Data Analytics.

In an in-depth interview with IBT, this educator unfolds many aspects of Big Data and its analytical implications. 

“In this scenario, companies use geolocation and geocoding to a set up digital parameter around the store. Whenever the cellphones of customers enter that zone, they get a pop-up that reminds them to do their monthly purchases.”

In recent times, Big Data has been a much talked about issue globally, what are the reasons behind such popularity?
During the 1990s and 2000s most of the organizations became automated and adapted Enterprise Resource Planning (ERP), centralized automation and centralized management systems. It was soon observed by them that automation was impossible without databases so they started collecting data. Approximately in the first half of 2010, people started realizing their data files are massive and weigh not just in gigabytes but terabytes and exabytes. They started looking at the large data sets that they have collected and examining the best possible ways to use them. One of the major areas where this Big Data became popular was specifically in search engines. Although Google entered the market after Yahoo became popular, it soon topped all the other search engines due to its spot on recommendations. They figured out that the data they collect from people’s searches or clicks can be used to predict patterns that would help them direct their content accordingly. Nowadays FIFA does not promote drug tests rather they take measures of the players height, weight, past performances, blood pressure and temperature. They then make use of the previous patterns of fluctuations in the pressure and temperature to figure out whether a player is on drugs or not. This is how big data slowly gained popularity.
Elaborate on the importance of Big Data Analytics in today’s world and its relevance in Bangladesh.
We are in the middle of a region which is thriving in big data. For instance in Malaysia, there is Petronas and a few more companies who are doing very well with big data. Singapore has almost connected the entire city using a single network. Then comes China, a country which produces massive amount of data and brilliant analysts.
There is some myth that exists in Bangladesh, which states that we do not have big data in Bangladesh. However, in reality, we have exabytes of data available in the country. Hospitals collect from the patients, which can act as a gold mine for future predictive health care. Similarly, common industries can use data analysis to maintain compliance and keep track of their production and human resources. It can also help us find out what kind of project development can result in better outcomes for effective analysis. Furthermore, it can be used to look into rural behavior and predict patterns that can be addressed for better rural development. On the other hand, the city has become heavily congested these days with the increasing population. Data is widely available on vehicles and traffic movement across the city, which can be accumulated together to find interesting patterns to solve congestion. Thus, Big Data has huge relevance when it comes to Bangladesh.

How can companies effectively use Data Analytics to drive significant values by harnessing the data that streams into their businesses?
Business experience and intuitions of a good manager cannot be replaced by anything. However, for many companies in Bangladesh when we do analysis and find a pattern, we do not give much importance to it. Therefore, right now, we are overdue on the revolution of big data in Bangladesh. As the next step, we need to figure out how big data can be used in our daily lives. In today’s world, big data has a major significance in marketing. Telecommunication and service companies like GrameenPhone and Pathao are all utilizing stacks of big data to attract new customers and investing in new markets. This data can come handy while differentiating between profitable and unprofitable ventures and tell us which patterns can be taken into account. Once a pattern is predicted properly, companies can make profitable use of the piles of data that flows into their streams. Similarly, much data also exists on defective products, raw materials, timeline, salary and bonuses of the human resources that can help organizations to ease out their processes and better manage their resources. This will help organizations to be more productive and efficient and serve the customers in a better way than before.

Considering the Data Analytics field is relatively new in Bangladesh, how can an individual build a career path in this field? What sort of resources and courses are useful in this regard?
There are 2 career paths in Data Analytics, 1 is a data scientist, and the other is a data analyst. The task of a data scientist is a quite complex one, which requires you to have both mathematical knowledge and a computer science background and if not that then a thorough understanding of how the programming function works. The job is often pretty time consuming and requires a person to undergo expensive training sessions to build a sound profile in this field. Therefore, if a person wants to build a career as a data scientist, the individual must start early and most importantly build upon the skills as mentioned earlier. As for subjects, one can start with introductory courses on programming and mathematics, then go on learning linear programming in calculus, AP calculus. One can even go for advanced courses like real analysis, nonlinear programming, calculative analysis and learn some programming languages like R and Python.
As a Data Analyst, one has to read the data, get inputs from it, understand the inputs, compute his or her analysis and present it to the user. Here one does not need to instruct the program about the type of analysis required; it knows what data an organization runs on thanks to automation on a different level. However, such automation comes with high level of skills, and I believe the recent graduates should go for smaller steps like being a data analyst first. This path requires certain basics of statistics. These are things that they can learn from courses available on websites like Coursera, edX, Udemy and using videos by Khan Academy. A certain level of programming skills is also required in this case. This skill I believe is a must-have for all sorts of graduates as it increases our cognitive skills by teaching us a logical way to instruct the computer. One can also learn from tutorials available on Codecademy and DataCamp.

How can the large organizations benefit from the transition of the traditional databases into big data lakes?
As the first step any organization can initially start with assigning small data roles to their employees and can then train their data analysts, give them the strategic importance and listen to their outcomes regarding strategy on a regular basis. When this is done, they can slowly move towards collecting data, developing big data departments in the company and assigning strategic roles to each of the department managers. Once the time and resources are being used into building a department with strong data analysts, companies can get better insights and utilize existing data in a better way.
Organizations then must allow the analysts to make the best use of resources and experiment with the data and the organization itself. This might all go in vain at first but more than 75% of the time, you can gain the desired result through the valuable insights obtained in the process. The analysts can further use the data to predict significant patterns in the consumer behavior and directly related content related to their responses to benefit from better consumer satisfaction. Many big data companies like Google, Facebook, and LinkedIn, are taking up this approach now, by incorporating the data insights into their main strategy.

As an educator, what are your take on the university education in Bangladesh and its scopes of improvement? What can be done on both the parts- students and institutions to make education a more effective phenomenon than just limiting it to basic learning?
I have been an educator since 2013, and in my 5 years of tenure, I have observed that Bangladesh is very regressive concerning business studies. We have a lack of advance courses in our curriculum, which are being taught to the students in the first world countries. This is something we really need to work on. Thankfully, North South University has already revised many of its course curriculums and is constantly stressing on improving the curriculum regularly. We are also offering new courses that are based on the demand of the new era, like project management, digital marketing, and covering courses like digital analytics.
A large gap also exists between the business environment and the university. There is very less interaction between the corporate world and the university, which needs to be improved drastically. One good way of doing so is to offer projects or establish offices in collaboration through which the companies in the market can place their problems into the universities. The companies may do so regarding day-to-day problems, which can then be addressed in the university offices through teacher-student consultation. Students can also reach out to companies and build a network that will help them in the future.


This is one of the key areas where people have been working for the past few years and is one of the most interesting outgoing trends right now. In this trend, we make use of machine learning, and deep learning to identify measures, videos, patterns and to automatically suggest scenarios, outcomes, and results. Predictive analytics have now gone over from just two-dimensional data like numeric data to three-dimensional data like videos, sounds, pictures and has started to identify recurring patterns.

Humans beings are very rational and often swayed by different sentiments and emotions. Analysts are trying to understand user command and figure out what sort of sentiments have an effect on them. Once this is done, they can use data on emotional responses to predict the decision of a customer and how it influences a customer to buy something. The next step after the decision bias is identified, is to introduce the idea of a social organization that can make better use of sentimental idea to predict the collective decision making of a community. For example, Barack Obama’s government heavily relied on sentiment analysis for the second election and tried to find out what kind of sentiments were working for the President.

The trend focuses on finding out different patterns of behavior; for instance, there is a study going on in China where they are trying to figure out whether traffic congestion affects the hourly productivity of workers or not. It is a fact that sitting in congestion surely hampers one’s punctuality. However, the theory suggests it has an impact on productivity too, as people often become cranky in the congestion which hampers their efficiency in the workplace.

This refers to the ongoing use of data to integrate websites, physical visits, and mobile sites to get insights in a way that can be used to draw consumer attention. This allows customers to look at things based on their previous purchases when they walk into a store. In this scenario, companies use geolocation and geocoding to set up digital parameters around the store. Whenever the cellphones of customers enter that zone, they get a pop-up that reminds them to do their monthly purchases. It also informs them about the discounts available on the products they generally buy, which helps companies retain customers in a better way.

This trend by Gmail is actually in its pirate phrase where they are trying to predict through emotions and sentiments whether an email is offensive or not. If an email has an offensive content, then Google can choose not to prompt it or just not allow the person to send that email. It can also set a countdown or a one day block off time where that user will not be allowed to use Gmail for a day or at least send that email to anyone.


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