This article was written by Mahadevi Jinnur, who a Training program on using AI for business growth out of Capability arbitrage.
This article was edited and published by Shashwat Kaushik.
Data analysis, in its primitive sense, means using raw data to generate actionable insights. Data analytics plays a crucial role in today’s world in forecasting, behavioral analysis, improving efficiency and much more, which will be discussed in more detail in this article. We will address the topic of what it means to use data analytics to make business decisions. We will also explore the various AI tools and techniques that help us reap the benefits of data analysis. You may see the word “Data’ I’m mentioned quite often in this article because it serves as an anchor to make the entire article make sense.
There are tons of data being collected all the time, but in its raw form, that data means nothing. Deriving insights from this raw data to understand the data is the job of a data analyst, which helps the company make informed business decisions. It’s about finding patterns in data that can tell us something useful or relevant to business operations. Data analytics is used to make faster and better business decisions, reduce overall company costs, and develop new and innovative products and services. Some real-world applications of data analytics are to predict future sales or purchasing behavior, aid in security purposes and protect against fraud, analyze the effectiveness of marketing campaigns, increase customer acquisition and retention, and increase supply chain efficiency .
A data analyst must perform a range of activities based on the business challenge and collaborate with other teams as needed. As a data analyst, it is a required skill to ask lots of questions about data while looking for patterns in the data. Asking the right questions and finding the most important answers to these questions is usually the first step to analysis – “data analysis”. The next steps in analyzing the data are understanding the need to perform the analysis, defining a problem statement and forming a hypothesis, and knowing where the data comes from. Next on the list would be cleaning and transforming the data. The original data may contain duplicates, anomalies, or missing data that could confound the interpretation of the data. This is tedious and sometimes a manual task, but a crucial task for gaining the right business insights. Analyzing the data includes some techniques like regression analysis, cluster analysis and time series analysis to name a few. After collecting, cleaning, transforming and analyzing, the final step would be interpreting and sharing the results. This is the step where data is transformed into valuable business insights. Depending on the analysis performed, the results are presented in the form of charts and graphs through a report, dashboard or app for others to understand. In this phase you will find answers to all the questions initially asked in order to address and solve the business problem.
From startups to multinational corporations, adopting data analytics is no longer a luxury but a necessity. All the steps that a data analyst and the team take to develop business solutions to solve a problem and make decisions are how data continues its journey.
Let’s try to understand the process of making business decisions using data analysis in a real-time scenario. For example, an insurance client hired an IT consulting firm to analyze its data across all streams to address bottlenecks and define business logic and strategies to increase overall operational efficiency. The insurance customer may face challenges such as retention of customers, performance of different claims and policies in different regions, sales trends over time, agent performance, rate of contract renewals, etc. The customer may have data that comes from the were collected from a variety of sources it doesn’t make sense until it is like this analyzed. By cleaning, transforming and analyzing these massive amounts of data by dividing it into categories and subcategories for different business scenarios and requirements, the data is made more meaningful. Presenting the data in the final step, which addresses challenges such as trends to identify root causes of customer retention rate, customer satisfaction analysis and risk assessment, can help a company’s leadership to implement solutions by making appropriate decisions for these problems.
Streaming platforms like Netflix, Hotstar and Prime Video use data analytics to understand viewer preferences to help select and produce shows that resonate with their audience1. Telecom and edtech companies as well as social media platforms all use data and data analytics to make informed business decisions to increase their sales and engage with customers.
Artificial intelligence (AI) enables computers to think and work like us. AI is the ability of a device to function and perform tasks similar to human intelligence. The more we train it, the more it learns and continues to learn to do things better. There are many types of AI. Supervised and unsupervised training of data using AI tools and techniques to train and model the data to optimize business needs. Natural Language Processing (NLP) is a method of transforming unstructured data into structured data that a machine can understand, interpret, and produce human language in a meaningful and useful way. Applications of NLP are used in machine translation, virtual assistants, chatbots, sentiment analysis, spam detection, and much more. Solving problems requires machine learning (ML), deep learning and NLP, as well as programming languages such as Python and R. Big data, algorithms, graphical processing units (GPU), and application programming interfaces (APIs) are all supporting resources for training.
By effectively using data, companies can improve their current operations, innovate, and adapt to future challenges and failures. In this ever-evolving, data-driven world, leveraging data and achieving the best results across all types of businesses and operations is supported by AI tools. The application of AI can range from meme creation, search engine optimization (SEO), job creation, teaching and learning of new languages, healthcare, arts, government activities, space exploration and education to self-driving cars, auto-correcting spaceships, etc resulting in an endless listing.
AI tools used in business decision-making increase efficiency by automating repetitive tasks. It reduces overall human error and increases accuracy. Manual and tedious analysis tasks are taken over by the AI tools, which in turn help in in-depth analysis, analyze large and complex data sets in the shortest possible time and derive meaningful insights. AI tools help aspiring entrepreneurs and startups focus on providing low costs and increased scalability to their customers. Companies that do not prepare to integrate the benefits of AI tools and techniques into their business operations will fall behind in the competition.
AI tools and techniques go hand in hand. Business intelligence (BI) tools like Tableau, Power BI and Looker play a critical role in extracting the essence of data and presenting it in a way that drives success. All BI tools integrated into programming languages help analysts create and automatically distribute reports on key business metrics. A company selects these AI tools based on its needs and trains its employees to properly use these tools to achieve results. Robotic Process Automation (RPA) is another such technique used by companies to automate invoice processing, optimize HR operations and onboarding processes, and improve customer service.
The possible uses of AI in business are countless. In all areas: healthcare, agriculture, finance, education, navigation, e-commerce, inventory management, procurement, travel and tourism, logistics and supply management. Every sector considered today is powered by data-driven AI. By using AI techniques, decisions in all areas can be made more easily and efficiently than before.
AI has become an inevitable part of human life. Competition in the market and in our daily lives makes it necessary for us to become familiar and familiar with the use of AI to our advantage. AI systems, tools and the various techniques used in the industry make optimal use of data as a primary source to derive and drive business growth. Since the beginning of the development and application of AI, all of our AI-based decisions have always been influenced by a spectrum of fears. It is now true that AI will not completely replace humans, only those who do not embrace it and use it to improve themselves. Although the current trend in the application of AI is slow, it is increasing rapidly and in the near future, AI-based solutions, decisions and optimizations will increase and deliver results that exceed our expectations.
