Big Data Analytics for the retail industry

Retail is the process of selling products and services through multiple channels. Retailers identify the buyer’s need and deliver the product or services through a medium. The medium could be the traditional physical shops or e-commerce websites, or multi-channel. The present landscape of retail is very different from what it was some years ago. For instance, the number of touchpoints for a consumer has increased more than ever before. While visiting a store, a consumer does not make the purchase decision by evaluating the product or talking to a retail salesperson. They use multiple information sources like browsing the web on their phones to compare prices, reading product reviews, checking in with an immediate network like friends and family, visiting social media pages to gather more information about particular products or services. In this process, the consumers generate a lot of data for future consumption or use the data to make their current decisions. Thus from the customer point of view, the purchase journey, including researching product, quality, price, convenience, availability, etc. leaves a lot of data traces

On the seller side and in the retail ecosystem, there are several strategic decisions to be made. Some of these include analysis of the current market scenario, understanding the customer, benchmarking of competition, supply chain process efficiency, channel & distribution strategy, pricing and placement of products strategy, etc. All of this relies on some data. Thus, both seller and customer use several data points at their respective levels in their buying/selling journey. The number of sources available and used has also increased manifold at both ends.

The large amount of customer data collected through the point of sale and other sources can help in producing valuable insights for retailers. Earlier, the retailers provided the product to the customer basis on the requirement mentioned at the point of sale. But the scenario is different now – retailers can use the previously collected data and behaviour of a cohort of consumers and predict the customer choices well in advance, even before buying the product. As a result, the data is growing exponentially in terms of volume, velocity, veracity, and value. These insights give them understanding and an edge over their competitors.

Therefore, big data analytics techniques, Artificial Intelligence and machine learning algorithms are now used more than ever. Big data is helping retailers in inventory management, packaging, cost-effectiveness, fast transportation, forecasting the demand and customer experience. With these analytics techniques, consumer cohorts and profiling are done to a certain level of accuracy and help sellers understand the interests, choices, and lifetime value of the consumer.

In the years 2020 and 2021, due to the Covid19 pandemic, this was further amplified by the digital-first behaviour of consumers. It further enables understanding and generating valuable information to serve the consumer better. Now, the brands know more about the consumers through their online behaviour, which benefits both the buyer and seller. Due to this deep knowledge about consumer likes/dislikes, choices and behaviour – offerings or even communication pre and post-offer can be customized, leading to increased customer loyalty and helping retailers return as repeat purchases and referrals.  While the pandemic situation is improving and the economy is on the recovery route, some shifts in product demand and consumer behaviour are long-term changes and therefore termed as new normal for the retail industry.

The present paper promises to provide a framework for using the various big data methodologies and data science algorithms, which can help retailers make better decisions. We will review the latest Machine learning techniques that can help solve core business needs of expanding the customer base. The paper will deliver some helpful methodology for big data analytics techniques for growth and sustainability in retail.

Disclaimer: All views, thoughts and opinions expressed belong solely to the author and don’t represent any organization that he is/has been a part of.

(The above article was published as an Abstract at the ICMIT2021 conference)

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Cloud Computing: An Overview

The computing power is overgrowing every day. We hear so many new terms in the industry, and if we are not updated, we may get confused. One of the very well sought terms is “Cloud computing” this article aims to provide a brief overview of cloud computing. Many people think that cloud computing is different from traditional computer architecture. But in reality, it is not that different. Cloud computing is still based on the same physical server hardware that is present in any computer network. The only difference is that cloud computing architecture makes the processing power and storage capacity of this hardware available over the internet.

What is cloud computing?

In simple terms, It is the on-demand availability of platform, infrastructure and applications. Cloud computing aims to offer businesses a cost-effective solution to increase their IT capacity and functionality.

Top Cloud Providers

Amazon Web Services (AWS)

Microsoft Azure

Google Cloud Platform

Alibaba Cloud


IBM Cloud

Oracle Cloud

Digital Ocean



Type of cloud computing:

There are mainly three types of cloud computing:

  1. Public Cloud – The public cloud is provided by third-party service providers, which offers their computing resources such as server, services, storage, etc. on the pay-per-use model. It is ideal for small and medium-sized businesses. The benefits of public cloud platforms are easy & quick scalability, cost-effective, easy to manage, reliability and without any geographical restriction.
  2. Private cloud – Private clouds are architecture owned by a single business entity. It is a more controlled environment where the resources are shared amongst different entities of the same business. The architecture can be managed externally as well as in-house. The private cloud provides a high level of security and customization as per the requirement of the business.
  3. Hybrid Cloud – The hybrid cloud is a combination of both public and private clouds. The deployment usually happens using a virtual private network. These days most businesses are using this model. It helps in the scalability of the in-house capability of storage or services.

Apart from the above, there is one more type emerging named “Community cloud”. The community cloud is like a private cloud that operates as a public cloud. It is used in a collaboration of different companies.

Types of Cloud Computing Services

There are mainly three types of cloud computing services:

  1. Infrastructure as a service (IaaS) – IaaS provides virtualized computing infrastructure to the user, which can be used over the internet. The providers of IaaS manage the physical end of the server, storage etc., in a data centre. Still, at the same time, it allows the customer to customize those virtualized resources as per their need. Under this service, a customer can purchase, install, configure, and manage any software they want to use. Examples of IaaS: Microsoft Azure Virtual Machine, Amazon Web Services (AWS) EC2 Instance, Cisco Metacloud, Google Compute Engine (GCE)
  2. Platform as a Service (PaaS) – Unlike IaaS, which provide tools through the cloud, the PaaS offers the framework to build, test, deploy, manage and update software applications. Like IaaS, it uses the basic infrastructure and includes the operating system, development tools, database, and middleware required for Software development. Examples of PaaS: AWS Elastic Beanstalk, Microsoft Azure Web Apps, Google Cloud SQL, Google App Engine, Apache Stratos
  3. Software as a service (SaaS) – in SaaS, the cloud service providers provide ready to purchase software that can be used over the internet. The service providers manage the infrastructure, Operating System, middleware and storage. The user gets access to software anywhere and whenever required. Examples of SaaS: Microsoft Office365, Google GSuite, Salesforce, Slack, DocuSign, MailChimp, Dropbox, Cisco WebEx

 Would you please share your view and let me know if this article helps you understand cloud computing in brief?

Disclaimer: All views, thoughts and opinions expressed belong solely to the author and don’t represent any organization that he is/has been a part of.

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Understanding Blockchain

Blockchain is a revolutionary concept because it reduces the risk of fraud for any transaction. The basic structure of any system is a transaction. Whether it is finance, economics, legal, political or even moral, some types of transactions occur when two entities interact. Therefore, three crucial parts or players of any interaction first are the details of both parties; second is the actual transaction, and third is the records containing the data point of any transaction. If we note the fact of any interaction, then the core will always be these three concepts. The rise in the transactional power of computers has resulted in the growth of the digital economy. There are many problems in maintaining the transparency for a transaction. The concept of blockchains emerged as a solution to these problems, and it promises transparency in every transaction.

As per the concept, this is the core of any virtual currencies such as bitcoin, ethereum, xrp etc. It is an open distributed ledger technology, which can record the transaction between two parties that cannot be altered and remains verifiable at any point of time. It operates through a distributed database on the decentralized network. So in short, the process is as follows, whenever a transaction takes place, a digital asset is created, distributed to the decentralized network with full real-time access. A transparent ledger which preserves all the details about the digital asses and transaction is maintained. It creates trust in the digital asset and removes many obstacles caused by intermediaries. It also gives freedom to individual, organizations or machines to transact and interact with each other.

There are three components of Blockchain: Blocks, miners and nodes. The blocks are present in every chain in multiple numbers. Each block has data, a 32-bit whole number known as nounce which is randomly generated at block-creation. And then there is a hash which is a 256-bit number with many zeroes in the beginning. At the time of first block creation, a nounce produced a cryptographic hash and data is signed and ties to the nounce and hash until it is mined. The second component is the miners. The miners follow a mining process for creating the new blocks on the chain, which is a complex process as every block has unique nonce and hash. It also has a reference of the previous block in the chain. All the miners work on unique software for finding the nonce, which can generate an accepted hash. Also, since nounce contains only 32 bits, the hash has 256 bits so there can be colossal combination possible, so many combinations are to be mined before one reaches the right one. It is an advantage of the Blockchain as it doesn’t allow any manipulation. Once the block is successfully mined, all the nodes accept the change and the miner gets the reward. The third component is the nodes. It is an electronic device which maintains the copies of the Blockchain. Every node has its copy, and the network approves any newly mined block with the help of an algorithm. Because of the transparency in the foundation, every action in the ledger can be easily checked and verified.

Blockchain came in existence as part of a proposal for bitcoin in 2008. Since then, there is a transformation going on in several industries to adopt the blockchain methodology. One of the main reason for implementing the Blockchain is that it slashes the cost of the transaction. There is an immense possibility in the Blockchain. The transformation may take some time because of the complexity, but this will revolutionize the organization if implemented well.

Disclaimer: All views, thoughts and opinions expressed belong solely to the author and don’t represent any organization that he is/has been a part of.

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In a Big Data world, do not ignore Small Data

Everyone is talking about implementing Artificial intelligence, Machine learning and big data in the organization. The future lies in these technologies, so there is a massive rush towards implementing advanced data science techniques and tools and using big data methodologies. In this race, the organization often ignores the importance of small data. No one can disagree that big data is essential and will help organization growth in the end. However, in this article, I want to highlight the benefits of small data projects and how it can help make fruitful business decisions.

Nevertheless, before demystifying the benefits of small data projects, let us first focus on big data projects. The big data is often characterized based on three Vs which is Volume, Variety and Velocity. Volume means the amount of the data; Variety means the types of data and Velocity means the speed. Big data gives extensive insights such as finding the hidden pattern about consumer behaviour or predicting sales using colossal data files. However, to reach those high-level insights, we need massive investment in the storage, tools, and skill set. The three Vs make big data complex and challenging to manage.

On the other hand, small data is usable, comfortable to obtain and leads to quick insights. Using small data strategically, the business can get frequent, actionable insights without investing in great tools and platforms. Small data is present everywhere. For example, an organization’s CRM system accessible to everyone has so much relevant information about customers, their segments, competitors, contact details, etc. If one can combine CRM’s insights, they can get multiple benefits for strategic business development. Small data projects require a handful of employees using small data sets and simple analytic techniques and methods to get the required results. The project duration is short, and as stated above, it does not require high-end tools.

So how an organization can get the best out of small data projects? Here is some suggestive approach:

1.      Awareness – This is the first step. One needs to identify the problems, which can be solved using small data sets. For this, involvement is critical. One should keep eyes on every tiny detail. Start small and build confidence in the team. Once the team sees value, they will also look for opportunities to find problems, which can be, solve using small data.

2.      Encouragement – Encourage the employees to join the data initiative. Award and reward them, who solve business problems using small data in their unique ways. It will motivate others to find the hidden data factories, and gradually, everyone will join the data-driven decision-making community within the organization

3.      Discipline approach – Usually, the small data projects are simple, and sometimes it directly leads to the solution. Because of this, people ignore the usual process of a data project. Therefore, it is advisable to follow a disciplined approach, even if the answer is visible. The necessary steps of any data projects include identifying the business problem, collecting the required data, performing the analysis and finding the suggestion and recommendation.

4.      Training to the employee – Training is another critical area. Small data projects are doable in every department and do not require high-end skills, but the basic training on data practices is still necessary. By providing essential training, one can ensure that everyone is on the same page while executing small data projects.

5.      Quality measurement – Even if the small data projects use a smaller data set, the quality measurement is essential. From obtaining the data to implementing the results, every step should follow the standard quality control process and guidelines. It will ensure the authenticity of the outcomes.

The data-driven decision making is becoming the necessity of upcoming time. It is essential for every organization, whether big or small, to think using the data. Small data projects can be beneficial in this journey. Even if it does not lead to high outcomes, it will help achieve a smaller milestone in a quick turnaround time.

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Technology is affecting the way we learn

No one can deny the impact of technology on human lives. Technology is driving every aspect of our life in one way or another. Whether we say it is for better or worse, we all live in the world shaped by technology. Technology is guiding our inception, growth, belief, mindset, emotion, communication, or even our existence. The Internet, who is the core source of communication in the technology-driven world, dominates our lives. We cannot imagine human life without the Internet anymore. When we want to know anything, even before asking ourselves, we ask the Internet.

As technology is affecting every aspect of human life, then how learning can be unaffected. Yes! Technology has transformed the way we learn more exceptionally then we can think. There are many real examples in which one can argue that education has not changed, and the foundational system still exists. I agree, but who has ever imagined that the blackboard will move from the classroom to the mobile screen or we can keep thousands of books in our pocket on a smartphone device. So let us look at some of the ways by which we can conclude that technology has transformed the way we learn:

Accessibility and reach – With the help of technology, education’s accessibility and reach have grown many folds. The Internet is providing access to education to everyone at any given point in time. The barrier of geographies does not exist anymore. One can learn from different parts of the world by sitting at their own home.

Customized learning – Where in the past, there was a concept of one size fits all. Learning used to happen in-group, where everyone was reading the same book at the same pace. However, it is not valid, not anymore. With the help of technology, one can learn the way he/she wants. There is an immense option available to choose, be it a course or instructor.

Latest and on time – Previously, a book or study materials used to come at least on a year gap as it used to take time to get it printed and make it available to the students or library for reference. Now, the latest information is available immediately in every possible way. The pace of sharing has become too fast.

Interactivity – Online education is becoming interactive by every passing day. The latest product of educational technology companies has made learning so much fun. There are so many new teaching methods for kids and adults to teach complex concepts quickly.

Collaboration – Earlier, the partnership was limited to the classroom or a group. However, now the collaboration is happening across schools, colleges, universities, or even countries in the virtual environment. It is not limited to the students only, but it is happening with teachers too.

We all agree that technology is a powerful tool that is transforming education and our life. It is helping to make learning easier worldwide. Anywhere anytime, knowledge is becoming a reality with the help of the Internet and smartphones’ accessibility. We all should be hopeful about the bright future ahead for every student and teacher.

Data-Driven Culture in an organization

Building a data-driven culture in the organization is a need of the hour. The sooner you do, the faster you will grow. Before fixing the customer’s concern with data, its significant to improve your very own situation with data. The data is available in many forms within the organization. To ensure your offering’s growth and better positioning to the customer, start investing time and resources to get insights from existing data within the organization. Following are some approaches which can help build the data culture:

Data is of the organization and not of the department – The first and foremost important point is to believe in the data as a whole. Whether the data is of Human Resource, Marketing, Finance, Sales or Operation, the idea is to make the employee feel that all data belongs to the organization and not of the particular department.

Cross-team communication – Communication within between teams is another critical aspect. Many of the time, there is a lack of communication between different departments of the organization. For example, due to non-shareable data culture, data might not be available for other departments for the use, which affects their decision making.

Democratize the data – Let everyone use the available data to build their success strategy for the organization. The data empowers a lot of small and weighty decision. For example, data from the marketing team can help the finance team to build their case. Therefore it’s essential to democratize the data.

Invest in the right tools and platform – The tools and platform are the backbones for a data-centric organization. Right tools help bring efficiency in the process of retrieval of the required information at any given point of time. Identify and evaluate the tools based on your requirement and invest in the same.

Identity and support right talent: There will always be some employees who will be proficient in using the data platform and tools. Let them be the support system for those who are not experienced enough. Identify those talented employees and ask them to train and motivate others.

This suggestion can help you build the data-driven culture and help you grow more in your area of business.

Talent Management with the help of people analytics

The most valuable and essential resource for any organization is its people. They are the heart and drivers of success in any organization. An organization needs to invest in its people by providing them the right environment to grow professionally and personally. Talent development is one area that is well sought. Now and again, the human resource department comes with innovative ideas for talent management and development. The process of talent development starts right from selecting suitable people for a particular profile to their retention. The global work environment is changing every day. More and more companies are changing the way they were looking at talent development. Companies used to think that if they recruit highly skilled people, they need not worry about their progress for several years. They were expecting highly skilled people to build a skilled workforce on their own. But not now, since the skill set required to perform a job is changing rapidly.

Talent development and management are also going through a period of change. In the organization, an employee continuously looks for a flexible work environment and diverse profile. They have to be reskilled many times during their span. An innovative work environment has become a basic need. The creative person will look for challenging work and expect their employers to provide the same. Data-driven decision-making has become essential to cater to this need, and using Analytics is the best way. Data Analytics is already helping organizations for their customer experience and product management, so using their internal workforce is not a new concept. Using HR Analytics, which specializes in people’s data, is the fresh hot cake for human resource and talent management. In the present time, if you are not utilizing your data to generate valuable insights, then you are missing something important. Analyzing people’s data for accessing their development needs can add value. So let us see how analytics can help the organization in its talent development programs. The process of talent development starts with hiring the right talent. The analytics system inside the organization helps in storing all information related to the employees. This information is not limited to their demographic and skill set but about their behavioral habits shown during the hiring process too. One can store the recording of the employee’s conversation during the interview process, which can be analyzed using big data methodologies for depth understating while onboarding of the employee. The combination of human and machine intelligence is shifting the thinking process within the HR spectrum. Machine learning algorithms help analyze people’s data and provide the required input for skill measurement at any point in time. Analytics tools are managing the talent scorecard, skill matrix, workforce planning, talent pool development, analysis, access to resources, etc. Several tools can quickly build a useful talent dashboard, which can easily help senior management view the current talent pool. Analytics helps in all significant talent development areas, such as recruiting, development, and retention of talent.

These days’ organizations are collecting multiple data points about their employees. Later these data points help in the development of training programs. Bringing an agile methodology with the use of analytics is making talent development programs friendlier. Predictive analytics techniques help HR managers segment employees based on their training needs, building different retention strategies for another segment. By using statistical modeling techniques, cost management has become more comfortable. Predictive analytics is assisting hiring managers in hiring the right talent based on the data. For example, an HR manager can get multiple inputs from several available sources on the internet using analytics tools. Be it a social media platform where a prospective employee is posting his view. Therefore, the origins of information about the employees are not limited to their resume anymore. After hiring, their skill set development, a program they are part of, and their platform are being analyzed by these analytics tools to get better insights about the employee.

Overall, data analytics using machine learning, artificial intelligence, and big-data methodology is becoming a future for talent development. Therefore, all the organizations recommend bringing workforce planning as close as possible to HR analytics. Let data-driven decision becomes a culture so every employee, no matter what level he is in the system, believes in data analytics’s capability. There are several models exist which help in building data-driven culture across the organization. An organization needs to select one of them based on their business areas and living culture. Transformation may not be natural, but when done, the future will look different.

(The above article is published by ISTD Delhi chapter in their Souvenir 2020 which was released in National Conclave on 5th and 6th September 2020)

Application of Data Science in Marketing

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The consumer is changing. The mindset of buyers does not have a specific way. Every day, Companies are trying to read the consumer’s mind and are always eager to explore what is going inside the mind of a consumer while making a purchase decision. The market place has changed now. The conventional marketing techniques are becoming obsolete. The first set of changes in product marketing came when the culture of supermarkets emerged. Many traditional shopkeepers had rejected the supermarket concept then, but when reality hit them hard, then the corner grocery stores also allowed customers to choose and pick the product on their own. The same thing repeated when the e-commerce portal started selling the products online. No one had ever imagined that one could buy anything on a click while sitting in the comfort of his or her own or while moving.

Data Science is one of the areas, which is helping the marketing department of every company. Whether one is selling the product or services, both are getting tremendous help in every decision they make from data science techniques. The companies are collecting and storing the data every second. This data is producing valuable information using data science techniques and tools. From product inception to product decline, everything is changing with the help of data science. Marketing research and consulting firms who are the most crucial supplier of consumer data to large organizations are generating insights from these data sets. The data is present in many formats and broadly classified in two types: structured and unstructured data. Nowadays, there are many techniques and tools available to analyze both types of data and provide real-time information for decision-making. We have highlighted some of the primary applications of data science techniques in the critical area of marketing decision making.

Customer Segmentation – If, as a company, you know who is buying your product, then you will never have to worry about your bottom line. Understanding the different segments of buyers can help in many ways. You can create a customized product for different types of consumers. Your marketing strategy can be shaped and reach to the relevant consumer. Data Science can help create a segment of your consumer based on their buying patterns and demographic information. Customer Relationship Management systems can provide secondary data and online methodology to collect primary data about the customers, which can help data scientists build the segmentation model. Cluster Analysis techniques are data science techniques that help define the segment of consumers for any products. Behavioural data plays an important role apart from demographic information while creating a segment of consumers.

Pricing Strategy – Data Science helps in defining the appropriate price of a particular product. It is essential to know how much a consumer will pay for a specific product. One of the crucial techniques is conjoint analysis. In this analysis, a consumer is shown a different combination of attributes of a product, and then they select which one he will prefer to buy and on what price. Post that, the data science algorithm analyzes the data to provide the optimal amount chosen by a set of consumers.

Competition Benchmarking– Market is a battleground where there are so many opponents who want to attract consumers. There is no shortage of options for any product. The age of monopoly is almost gone. Therefore, it is essential to know whom you are fighting. The completion benchmarking can help the company to win this. A company compares themselves with their competition by this process. There are several methods available under data science that can help analyze the performance of the product, representation of channel, and reach. Social Media Analytics is highly used these days for improving the benchmarking process.

Supply Chain Management – This is one of the critical areas for any business, especially for a product-based company. Nowadays, the whole process of Supply chain management is automated using data science algorithms and tools. Some companies have built a zero manual intervention model for their supply chain management. In the past, the supply chain function was an operation process purely, but with the help of the latest data science techniques, blockchain, artificial intelligence, it is an emerging strategic advantage. Demand forecasting, procurement, distribution is using various modelling techniques and real-time data.

Target Marketing – The dimension of marketing has completely changed now. In the past, a banner or posters were placed on various locations where maximum people can see, and the company used to believe that the product information has reached the right consumer that is not true anymore. Nowadays no one cares what is displayed until it is essential information for him or her. The predictive modelling techniques in data science is helping companies to create target based marketing campaign. The online advertisement is fully algorithm-driven. The data science algorithm running behind knows whom to show what. These automated techniques provide a maximum reach of any product to the right consumers.

The above mention examples show that it is essential for every marketing person to know about data science. The decision based on gut filling may not always help, but the decision based on the right data will still help. There are many resources available to educate in the field of data science. The prerequisite for enhancing the knowledge in the file of data science is basic mathematics and statistics, along with computer science. A marketer who has expertise in his domain and basic understating of data science can generate gold for any organization. Remember that in the upcoming time, only data will be the truth rest everything will be an illusion.

(The Above article has been published in the Second Edition of PHD-Chamber Journal of Ideas Innovations)

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Text Analytics for understating the emotion in the written text

In the present day, every company has access to a large volume of unstructured data, which can help in strategic business decisions. One of the essential data types is textual data. For example, a business must know what customers feel about their products or services. Similarly, in research, it is essential to understand what a respondent is saying on his own. When we prepare a structured questionnaire to collect responses, we ask close-ended questions that give us direct answers, and we have open-end or free answers questions in which the respondent writes sentences. These two types of questions provide two kinds of data one in a structured format and another in the unstructured format as a text format. The whole ecosystem of the data is of the two forms only. In an Unstructured format, there is further addition of Audio, video, and images.

Text Analytics is one area by which the text or responses collected through different media are analyzed using tools and algorithms. Previously, the text data was investigated by the manual reading of each sentence. With the advancement and availability of the latest tools, the text analytics process is moving towards automation. Several methods and tools available can quantify the text data to provide patterns, trends, and insights. Natural Language Processing (NLP) is one of the widely used concepts that help in analyzing text.

The input for text analytics or text mining comes from online reviews, twitter feeds, Facebook posts, emails, survey questions, and customer feedback. Due to the boost of social media platforms, everyone has the power to write content related to anything. These written contents are a gold mine for respective stakeholders. Once the input is in place, the software or tools perform further analysis. To provide the full context, let us consider the process followed in the R software.

Firstly, in the R environment, a corpus is built using the input data that is simply a collection of unstructured text. Once the corpus is ready, then the data cleaning is done by removing the numbers, punctuation, stop words, whitespace, etc. After that, the tool generates a Document Term Matrix. The document term matrix is a matrix in which the words are present as columns with their count as rows. This matrix is used for further analysis to get valuable insights from the text data.

The text analytics results come in the form of word count, frequencies, word clouds, word association, correlations, and clusters. Apart from these analyses, the tool also provides the facility to conduct sentiment analysis of text data. The sentimental analysis is the process by which one can interpret and classify the data into three emotions: positive, negative, and neutral. With the help of the content analysis, a business or research scholar can identify respondent sentiment towards the company, brands, products, or research scenarios. If a business can know what a customer feels, then they can improve their offerings.

Artificial Intelligence is helping the text analytics domain in a big way. The infrastructure for holding a massive amount of data is available these days. So many cloud service providers give services for storing and mining unstructured data. The future is looking promising for text analytics.

(The above article was published as an Abstract at ICMIT2020 conference)

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The changing face of research

Technology has bought research from the world of pen and paper surveys to the digitally recorded and machine analyzed. Right from collecting data to the presentation of findings, everything is quick, concise, and available at a click. Everyday new tools are emerging and replacing the traditional method of conducting research.

While in traditional research, customers and prospects used to be the target, and data collection happens using face-to-face Interviews and surveys. In the current digital world, buying, watching, search habits, etc. is defining the persona of a person which is a target. How we behave digitally has become more important than what we answer in a particular survey question. Data retrieved from the apps installed on our smartphones tell more about us which usually we don’t admit or say. The devices that we use – be it handheld or laptop, fitness gadgets, etc. examine our habits’ patterns and provide input to the researchers. Access and uses of significant data sources are widening with the help of the latest tools and technologies.

These new-age methods of identifying suitable consumers not only are more reliant but also are timesaving. Marketers today need real-time availability of insights for quick decision-making. The analysis of the information collected is not limited to regular findings using survey analysis tools. It is now also based on Machine-learning models, Natural language processing, etc through which the results are both faster and quicker.

The final delivery of information is also changing face with earlier being only as a report presentation using PowerPoint, Word, or Excel sheets to live data streaming dashboards. These customized dashboards are available on mobile devices too. The right time research is evolving as real-time research.

With all these advancements, there are also some inherent challenges in the transition. Lack of required talent is one of them – there is no formal training available to train on modern research techniques. The only source is the internet, and the availability of humongous information on the internet is scary and confusing for learning the advanced and upcoming tools or technologies. Validation of information present on the internet becomes difficult for the researcher. Another critical challenge is the selection of the right tools from multiple new-age options. Every means and method has its pros and cons. It is essential to understand any particular tools or method’s strengths and weaknesses before using it. Some specific techniques might be useful for one type of research but it can be disastrous for another.

Thus, while educating the industry about the new techniques and equipping the current researchers with the latest tools and right skills is the hour’s need.

(The above article was published as an Abstract at ICMIT2019 conference)