There is an enormous amount of data collected worldwide.

The above chart depicts data collected worldwide in zettabytes (ZB) as of 2019 and it is estimated by 2025 it would be 181 ZB. A ZB is the equivalent of one trillion gigabytes and a zettabyte is equivalent to about 250 billion full-length movies.

Let's break it down further to understand the mammoth of data collection happening every minute.

Business Analytics is indispensable in today’s age of big data. It is an optical mix of Data collection, Data Visualization, Data Management, Statistics, Machine Learning, Operations research, and Critical thinking married with Domain Knowledge. An interdisciplinary field that creates insights and values in ways hitherto unheard of in the industry, government, and other fields, thereby providing a competitive advantage. 

It refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insights and drive business planning. 

 is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. 

Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are 

 and know how to interpret and analyze that information.

The potential for data analysis within a business is boundless and without limits. From tracking customer behaviour and input prices to simulating road traffic and competitors’ sales strategies, a skilled Data Scientist can contribute to the success of a business in many ways.

A few of the most crucial advantages of incorporating analytics into businesses are:

In approaching critical strategic decisions, business analytics can prove to be a valuable asset. When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents enhance the speed and precision in addressing support tickets by utilizing 

 to determine the effectiveness of the new iteration of the product compared to its original version.

—a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process but saved the company millions of dollars.

Firms that adopt data and analytics programs can realize substantial financial gains. According to 

, companies that invest in big data achieve a 6% average profit increase, with a jump to 9% for investments lasting five years.

found that businesses are able to quantify their gains from analyzing data and report an average 8% increase in revenues and a 10% reduction in costs. These results demonstrate the tangible financial rewards that can stem from a well-thought-out business analysis plan - a strategy that many companies can gain an advantage from, as the big data and analytics industry expands.

Analytics can serve purposes beyond just financial benefits. It's been discovered that companies are using predictive analytics to optimize their business processes and operations.

, organizations use data analytics to identify potential issues before they escalate. For instance, a mobile network provider surveyed in the report stated that they use analytics to predict outages a week before they occur. This enables the company to plan maintenance more efficiently, leading to cost savings and ensuring optimal performance levels of their assets.


Business analytics is always a recurring and iterative process and sometimes it doesn’t follow any specific cycle as most of the steps need to be visited as required on the job.

Identifying a problem statement and understanding the problem would be the most essential aspect of getting to business analytics. Without understanding the business or domain it is practically difficult to get to a solution or apply analytics. This would involve a lot of interaction with the business with an inquisitive and critical-thinking mindset. 

This would be the foundational goal of any business analytics solution and getting under the skin of business is quintessential for successful analytics. Any misunderstanding or incorrect information gained from business can lead to a huge blunder or a type 2 error in your hypotheses, leading to a catastrophic impact on your business.

After understanding the problem statement or business need and assuming it's done well, you will need to identify sources from where the data can be collected. This would require a holistic approach. Now one must remember that information or data always cannot be made available in a structured or tabular way which is usually a stereotype of how data is expected or perceived. 

Data can be in several forms and sizes. For example, for all call centers business data could be of voice or audio-related information, for security agencies image or video data would be necessary, or to understand your end customer perception of your brand social media data (text data) would be needed. 

As seen above, in a minute you can have millions of transactions happening all over the globe and sometimes you could be dealing with Terabytes of data to solve a basic problem. Currently, there are various mechanisms and sophisticated infrastructures in place to deal with such mammoth data which is a different discipline in data management.

As mentioned above data comes in varied forms and sizes and much of the data would be in unstructured or sometimes unrecognizable form. It is very important to make sense of the data before we proceed to work on them, hence data preparation is the most important and the hardest part of the entire process which in today’s day and age is the most underrated process when it comes to business analytics or data science for that matter. 

Since we are dealing with high volumes of data much of it would be noise and irrelevant to the requirement. With the right domain or business knowledge, it is key to identify the junk which involves the process of data cleaning, transformations, and structuring it out to make better sense of it. A well-prepared data problem is half solved.

Assuming you have prepared the data well, it's important to determine what the data is conveying or the story from the data. Data preparation and Data analysis go hand in hand and are iterative to ensure we have the right information for the problem we are solving. A standard term and technique used to understand data is 

This helps understand types, distribution (a statistical technique), relationships of variables with one another, and certain useful knowns and unknowns of the business or domain. Proper data analysis helps to identify the key variables or features needed to solve a business problem and most of the time a proper EDA can help solve critical business problems.

With robust data analysis and understanding of the variables of a dataset, usually, we can intuitively determine how a certain outcome is achievable or what key factors influence an expected result in the interest of the business either to sustain or grow the business in a broader sense. Therefore, in companies that have a business analytics vertical a lot of emphasis is given, and best practices are put in place for establishing 

 to ensure the above points are done well with a set quality output. 

But in most cases, we can’t leave it to intuitiveness and assumptions as a business would need scientific backing. Here is where the concepts of advanced statistics come into play to provide us with precision and optimized error results with scientific validation of the outcome. 

Further, with a need for a robust solution and scalability, it is essential to employ algorithms (Statistical & Machine learning) that not only help us predict the outcomes with a healthy lead time but also precisely indicate what needs to be done with optimized efforts to achieve them as well.

The use of algorithms to get precise outcomes is essential for the business which reduces the risk of uncertainty and helps plan better. But there are several algorithms to solve basic problems. It is a good practice in business analytics to be iterative with the use of the right algorithms and varying factors (simulate worst to best-case scenarios) to arrive at varied outcomes and choose the best or optimal outcomes depending on the business requirement. This would further strengthen your case for the solution you will provide to the business.

As seen through the above points, business analytics is a key enabler to make the right decisions with various methods and techniques. It has a proven record and sophisticated scientific backing which simulates real-world scenarios and helps us understand the best decisions in order to achieve the business goals or needs.

Business analytics has many use cases, but when it comes to commercial organizations, Business Analytics is typically used to:

Analyse data from a variety of sources. This could be anything from cloud applications to marketing automation tools and CRM software.

Use advanced analytics and statistics to find patterns within datasets. These patterns can help you predict trends in the future and access new insights about the consumer and their behaviour.

Monitor KPIs and trends as they change in real-time. The ability to centralize business data and swiftly arrive at precise conclusions is made attainable with this feature.

Support decisions based on the most current information. With business analytics providing such a vast amount of data that you can use to back up your decisions, you can be sure that you are fully informed for not one, but several different scenarios.

While these are the most common use cases, there are four primary methods of business analysis. They’re implemented in stages, starting with the simplest. One method isn’t more important than another, it all depends on what your end goal is when using Business Analytics.

4 Primary Techniques of Business Analysis

The four primary techniques of business analysis, widely employed, are executed in a step-by-step manner starting with the least complex one. The significance of each method varies depending on the intended outcome of using Business Analytics.


By implementing these four types of analysis, your data undergoes a thorough process of cleansing, examination, and comprehension, enabling you to craft solutions to any obstacle your organization might encounter.


 Analyzing historical data and key performance indicators to uncover trends and patterns through interpretation. This allows for looking at the larger picture view of what happened in the past and what is happening currently using data aggregation and data mining techniques. Many companies use descriptive analytics for a deeper look into the behaviour of customers and how they can target marketing strategies for those customers.

Examining past performance to identify the factors that shape particular trends is the central focus of this process. This is accomplished by utilizing drill-down, data discovery, data mining, and correlation analysis to uncover the root causes of specific events. Once a comprehension of the probability of the event and the reasons behind it is established, algorithms for classification and regression are applied.

Uses statistics to forecast and assess future outcomes using statistical models and machine learning techniques. This often takes the results of descriptive analytics to create models that determine the likelihood of specific outcomes. This type of analytics is often used by sales and marketing teams to forecast the opinions of specific customers based on social media data.

 Draws upon historical performance information to suggest the best approach for comparable circumstances in the future. Not only does this type of business analytics determine outcomes, but it can also recommend the specific actions that need to occur to have the best possible result. This is often achieved using deep learning and complex neural networks.

This type of business analytics is often used to match various options to the real-time needs of a consumer. Deciding which method to go with will depend on the business situation at hand.

a)	Domain knowledge or Business Understanding:

Understanding the domain or business is very essential without which you cannot be successful in the implementation of analytical solutions. Without domain or business understanding the business teams will never take you seriously and they will result in cut down or no analytical projects coming your way.

In order to come up with scientific explanations of outcomes to enable decisions, it is important to have a good understanding of certain disciplines of science majorly mathematics and statistics. In order to understand the right applications of algorithms on a given data set, it is necessary to align the outcomes of the algorithm with the business need.

Business Analytics is an important decision-making tool that helps organizations in formulating a business strategy

Business Analytics and its importance for an organization

As a generation, we are witnessing a revolution: the rise of artificial intelligence (AI). AI has the potential to change the way we do business and live our lives. We are only beginning to understand the full implications of this technology, but it is already having a profound impact on the world. 

In this article, let us explore how AI is transforming the world, what tools and technologies are available, why AI is important, and how we can prepare for the future of AI.

Artificial intelligence (AI) is creating intelligent machines that can think and act like humans. It is an area of research that has been around for decades but has only recently started to gain mainstream attention. AI can be used to automate tasks, analyze data, and solve complex problems. It has the potential to revolutionize a wide range of industries, from healthcare to finance.


At its core, Artificial Intelligence is an umbrella term that covers a variety of technologies and tools, including machine learning, natural language processing, and neural networks. It is a rapidly evolving field, with new developments and advancements being made on a daily basis.


The impact of AI on the world is already being felt.  

AI is transforming the world in a variety of ways, including:

AI is being used to automate a wide range of tasks, from customer service to manufacturing, which can increase efficiency and reduce costs.

AI is being used to analyze data and make predictions about future events, which can be used to improve decision-making in fields such as finance, healthcare, and marketing.

AI is being used to personalize experiences for users, such as recommending products or content based on their preferences.

AI is being used to create assistive technologies for people with disabilities, such as speech recognition software and autonomous wheelchairs.

AI is being used to develop robots that can perform a wide range of tasks, from assembly line work to search and rescue operations.

AI is being used to develop autonomous vehicles, which have the potential to reduce accidents and improve transportation efficiency.

AI is being used to develop diagnostic and treatment tools, such as computer-aided detection of diseases and personalized medicine.

AI is being used to detect and prevent cyberattacks, such as identifying and blocking malicious network traffic.

AI Innovation and Its Impact on the World

AI innovation is having a profound impact on the world. AI is being used to improve healthcare, create smarter cities, and make transportation safer. It is also being used to improve education by providing personalized learning experiences. AI is even being used to help combat climate change, as it can be used to reduce energy consumption and develop more sustainable solutions.

AI is also impacting the economy. Companies are using AI to increase efficiency, reduce costs, and provide better customer service. AI is also being used to create new jobs, as companies are investing in AI-powered products and services.


What is the Future of Artificial Intelligence?

The complete implications of AI are unknown at the moment. Some experts believe that AI will continue to evolve and become more intelligent, eventually surpassing human intelligence. Others believe that AI will remain a tool to be used by humans, but that it will become increasingly sophisticated and powerful.

The future of AI will depend on its application. If it is used responsibly and ethically, it could be a powerful force for good in the world. If it is misused, it could have devastating consequences.

AI has a lot of potential benefits. A few of them are listed below:


It can be used to automate tedious tasks, allowing companies to save time and money.

It can be used to analyze large data sets, allowing companies to gain valuable insights.

Artificial Intelligence can be used to solve complex problems, allowing companies to develop innovative solutions.

AI also has the potential to improve customer service. AI-powered chatbots can be used to provide 24/7 customer service, and AI-powered recommendation engines can be used to personalize the customer experience.

AI can also be used to create more efficient and effective marketing campaigns.

AI also has some significant challenges. Some of the biggest challenges are: 


The risk of bias. AI algorithms can learn from data that is biased, resulting in inaccurate predictions. AI also raises concerns about privacy, as the data collected by AI algorithms can be used to profile individuals and target them with personalized ads.

Another challenge is the lack of transparency. AI algorithms are often opaque, making it difficult to understand how they make decisions. This lack of transparency can lead to distrust in AI algorithms and a reluctance to use them.

Finally, AI raises ethical concerns. As AI algorithms become more sophisticated, there are questions about how they should be used and who should be held responsible when they make mistakes.

AI-powered assistants, such as Amazon Alexa and Google Home, are becoming more common in our homes. AI-powered chatbots are helping us get quick answers to our questions. And AI-powered recommendation engines are helping us discover new products and services.

AI is also being used in the workplace. AI-powered tools are being used to automate mundane tasks, allowing employees to focus on more important work. AI-powered analytics are being used to gain insights into customer behavior and improve decision-making. And AI-powered bots are being used to manage customer interactions, freeing up employees’ time.

There are a variety of AI tools and technologies available today. For example, Neural Networks, another AI tool, allow computers to mimic the human brain.


These tools and technologies can be used in a variety of ways. For example, machine learning can be used to create recommendations for customers, natural language processing can be used to understand client queries, and neural networks can be used to detect objects in images.


There are many different Artificial Intelligence tools available, and new ones are being developed all the time. Some of the most popular and widely-used AI tools for businesses include:

Machine learning is one of the most common AI tools, as it allows computers to learn from data to create predictive models. The platforms, such as TensorFlow and Scikit-learn, provide a variety of tools and libraries for building and deploying machine learning models.

Natural Language Processing (NLP) Tools: 

Natural language processing is another common AI tool, as it allows computers to understand human language. NLP tools, such as spaCy and NLTK, are used to process and analyze human language. They can be used for tasks such as sentiment analysis, language translation, and content generation.

Computer vision tools, such as OpenCV and TensorFlow, allow businesses to analyze and understand images and videos. They can be used for tasks such as image recognition, object detection, and facial recognition.

Chatbot platforms, such as Dialogflow and Botkit, allow businesses to create and deploy chatbots for customer service, marketing, and other purposes.

Robotics and automation tools like ROS and PyRobot are widely used to automate repetitive and tedious tasks in manufacturing, logistics, and other industries.

These AI tools can provide businesses with a variety of benefits, including increased efficiency, improved customer service, better decision-making, and the ability to automate repetitive tasks. Additionally, AI can help businesses to analyze large amounts of data, identify patterns and trends, and make predictions about future events.

 is a powerful language model that is transforming the AI world by enabling the development of more sophisticated natural language processing (NLP) applications. These applications include chatbots, language translation, text summarization, and question answering. The model's ability to generate human-like text has also led to its use in creative writing and content generation. Additionally, the model's ability to understand and respond to context is helping to improve the overall user experience in NLP-based applications.

ChatGPT is a powerful language model that can be beneficial for businesses in a variety of ways. Some of the key benefits include:

Automating customer service: ChatGPT can be used to create chatbots that can answer customer questions, provide information, and even handle basic transactions. This can save businesses time and money by reducing the need for human customer service representatives.

Content creation: ChatGPT can be used to generate high-quality, human-like text, which can be used for various purposes such as blog posts, product descriptions, emails, and more. This can save businesses time and money by automating the content creation process.

Language translation: ChatGPT can be used to translate text from one language to another, which can help businesses to expand their reach and communicate with customers in different languages.

Improving search engine optimization (SEO): ChatGPT can be used to generate high-quality, keyword-rich content, which can help businesses to improve their search engine rankings and drive more traffic to their website.

Improving the user experience: ChatGPT's ability to understand and respond to context can help businesses to create more personalized and engaging experiences for their customers.

Overall, ChatGPT is a versatile tool that can help businesses to automate various tasks, improve their communication and customer service, and create high-quality content more efficiently.

 is a variant of GPT-3, which is a large-scale language generation model developed by OpenAI. DALL-E (pronounced "dolly") stands for "DALL-E 2: Generative Pre-training of Image and Language Models" is a model that can generate images from natural language text. It can take text prompts such as "two dogs playing with a frisbee on a beach" and generate an image of that scene. The model was trained on a dataset of text and image pairs, allowing it to learn the relationship between language and image content.

Generating images from natural language text, which can be used in applications such as visual storytelling, content creation, and advertising.

Image search and retrieval, where users can search for images using natural language queries.

Enhancing the capabilities of existing image-based applications, such as image captioning and visual question answering.

Generating images for use in machine learning training sets, which can be used to improve the performance of image recognition and other computer vision models.

DALL-E and similar models have the potential to revolutionize the way we interact with and understand images.

 is a design automation platform which utilizes AI and machine learning to help businesses create professional-looking designs. The platform enables users to create a wide range of designs, such as social media graphics, logos, marketing materials, and more, quickly and easily.

The platform uses natural language processing (NLP) and computer vision technologies to understand user's design intent, and generate designs that match their requirements. It also includes a library of templates and pre-designed elements, which users can customize to create their own designs.

Canva is beneficial for businesses in a number of ways:

It saves time and money by automating the design process and eliminating the need for manual design work.

It allows businesses to create professional-looking designs without needing to hire a designer.

AI has massively transformed our lives and this is only the beginning. It has opened up a whole new world of possibilities 

Business Strategy

Today’s Articles

Business Analytics and its importance for an organization

Witnessing a Revolution: How Artificial Intelligence is Transforming Our World