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Data analytics, in a primitive sense, means making use of raw data to produce actionable insights. Data analytics in the current world plays a vital role in forecasting, analysing behaviours, improving efficiency and many more, which will be explored further in this article. We will be touching on the subject of what it means to leverage data analytics for making business decisions. We will also explore the different AI tools and techniques that help us take advantage of data analytics. You might be looking at the word ‘data’ quite a number of times in this article because it acts as an anchor for this whole article to make sense.
Loads of data are collected all the time but in its raw form, this data doesn’t mean anything. Deriving insights from this raw data to make sense of the data is the task of a data analyst, through which the company can make informed business decisions. It is all about finding patterns in data which can tell us something useful or relevant for business operations. Data analytics is used to make faster and better business decisions to reduce overall business costs and to develop new and innovative products and services. A few applications of data analytics in the real world are to predict future sales or purchasing behaviours, for security purposes to help and protect against fraud, to analyse the effectiveness of marketing campaigns, to boost customer acquisition and retention, and to increase supply chain efficiency.
A data analyst has a range of activities to perform based on the business challenge and collaborating with other teams if necessary. As a data analyst, while trying to find patterns from the data, asking a lot of questions on data is a required skill. The right questions and finding the key answers to those questions along the way is usually the first step towards analysis- ‘data analysis’. Understanding the need to conduct the analysis, defining a problem statement and making a hypothesis and knowing where to get the data from are the next steps in analysing the data. Cleaning and transforming the data would be next on the list. The original data might include duplicates, anomalies or missing data, which could distort how the data is interpreted. This is painstaking and sometimes a manual task but a crucial one to get the right business insights. Analysing the data includes some of the techniques such as regression analysis, cluster analysis and time series analysis to name a few. After collecting, cleaning, transforming, and analysing, the final step would be to interpret and share the results. This is the step where data is turned into valuable business insights. Depending on the analysis conducted, the results are presented in a way others can understand it in the form of charts and graphs via a report, dashboard or app. This is the stage where you find answers to all the questions framed initially to address and solve the business problem.
From startups to multinational corporations, the adoption of data analytics is no longer a luxury but a necessity. All the steps that a data analyst and the team perform to bring out business solutions to solve a problem and move towards decision-making are how data continues its journey.
Let us try to understand the process of taking business decisions with data analysis in a real-time scenario. For instance, an insurance client outsourcing an IT consulting firm to analyse their data from all streams to address bottlenecks and to define business logics and strategies to increase their overall operational efficiency. The insurance client may be facing challenges such as retaining customers, performance of different claims and policies across different regions, trend of sales over time, agent performance, policy renewal rate, etc. The client might have data collected from all kinds of different sources but it does not make sense until it is analysed. Cleaning, transforming and analysing this huge data by dividing into categories and subcategories for different business scenarios and requirements will align the data towards meaningfulness. The representation of data in the final step, which addresses the challenges such as trends to identify causes for customer retention rate, customer satisfaction analysis, and risk assessment, can help the senior level in a company to implement solutions by taking appropriate decisions to these problems.
Streaming platforms such as Netflix, Hotstar, and Prime-Video use data analytics to understand viewer preferences, helping in the selection and production of shows that resonate with their audience1. Telecom and edtech companies and social media platforms all make use of data and data analysis to make informed business decisions to increase their revenue and to engage with the customer.
Artificial intelligence (AI) is a way for computers to think and work like us. AI refers to a device’s ability to function similarly to human intelligence and perform tasks. The more we train it, the more it learns and re-learns to perform things better. There are many types of AI. Supervised, unsupervised training of data using AI tools and techniques to train and model the data to optimise business needs. Natural Language Processing (NLP) is a method of changing unstructured data into structured data that a machine can understand, interpret, and generate human language in a way that is meaningful and useful. Applications of NLP are used in machine translation, virtual assistants, chatbots, sentiment analysis, spam detection and many more. Machine learning (ML), Deep learning, and NLP along with coding languages such as Python and R are required for solving problems. Big Data, algorithms, Graphical Processing Units (GPU) and Application Programming Interfaces (APIs)-all of these are supporting resources to train it.
By leveraging data in an effective manner, companies can improve their current operations and innovate and adapt to future challenges and failures. In this ever-evolving world encapsulated by data, harnessing data and bringing the best results in all kinds of business and operations is backed up by AI tools. Application of AI can range from meme creation, search engine optimisation (SEO), creating jobs, teaching and learning new languages, healthcare, art, government activities, space research, and education up to self-driving cars, auto-correcting space crafts and leading to an endless listing.
AI tools used in business decision-making increase efficiency by automating repetitive tasks. It reduces the overall human error and enhances accuracy. Manual and tedious tasks of analysing are done by the AI tools, which in turn help in in-depth analysis, analysing large and complex sets of data in the minimum amount of time and deriving meaningful insights. AI tools help budding entrepreneurs and startups to focus on bringing clients low cost and increased scalability. Companies that do not adapt to working and fusing the advantages of AI tools and techniques into their business operations will fall back in the competition.
AI tools and techniques go hand in hand. Business intelligence (BI) tools such as Tableau, Power BI, and Looker play a pivotal role in extracting the essence of data and presenting it in a way that drives success. All of the BI tools integrated with programming languages help analysts generate and automatically distribute reports on key business metrics. A company based on its needs chooses these AI tools and trains their employees to use these tools in the right manner to produce results. Robotic Process Automation (RPA) is another such technique employed by companies to automate invoice processing, smooth HR operations and onboarding processes, improving customer service.
The applications of AI in business are innumerable. In healthcare, agriculture, finance, education, navigation, e-commerce, inventory management, procurement, travel and tourism, logistics and supply management–all the fields. Any sector taken into consideration today is backed by data-driven AI. Decisions in any of the fields are made easily with more efficiency than before because of the application of AI techniques.
AI has become an inevitable part of human life. The competition in the market and in our daily lives makes it a necessity for us to become familiar and well-versed with employing AI for our benefit. AI systems, tools, and the different techniques used in industries make the best use of data as a primary source to derive and drive business growth. Since the beginning of the development and application of AI, there has always been a spectrum of fear lining all our AI-based decisions. It is now true that AI will not replace humans entirely but only the ones who do not embrace and employ it for their improvement. 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 optimisations will grow and deliver results beyond our expectations.
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This article has been edited and published by .
Introduction
Data analytics, in a primitive sense, means making use of raw data to produce actionable insights. Data analytics in the current world plays a vital role in forecasting, analysing behaviours, improving efficiency and many more, which will be explored further in this article. We will be touching on the subject of what it means to leverage data analytics for making business decisions. We will also explore the different AI tools and techniques that help us take advantage of data analytics. You might be looking at the word ‘data’ quite a number of times in this article because it acts as an anchor for this whole article to make sense.
What is data analytics
Loads of data are collected all the time but in its raw form, this data doesn’t mean anything. Deriving insights from this raw data to make sense of the data is the task of a data analyst, through which the company can make informed business decisions. It is all about finding patterns in data which can tell us something useful or relevant for business operations. Data analytics is used to make faster and better business decisions to reduce overall business costs and to develop new and innovative products and services. A few applications of data analytics in the real world are to predict future sales or purchasing behaviours, for security purposes to help and protect against fraud, to analyse the effectiveness of marketing campaigns, to boost customer acquisition and retention, and to increase supply chain efficiency.
What does a data analyst do
A data analyst has a range of activities to perform based on the business challenge and collaborating with other teams if necessary. As a data analyst, while trying to find patterns from the data, asking a lot of questions on data is a required skill. The right questions and finding the key answers to those questions along the way is usually the first step towards analysis- ‘data analysis’. Understanding the need to conduct the analysis, defining a problem statement and making a hypothesis and knowing where to get the data from are the next steps in analysing the data. Cleaning and transforming the data would be next on the list. The original data might include duplicates, anomalies or missing data, which could distort how the data is interpreted. This is painstaking and sometimes a manual task but a crucial one to get the right business insights. Analysing the data includes some of the techniques such as regression analysis, cluster analysis and time series analysis to name a few. After collecting, cleaning, transforming, and analysing, the final step would be to interpret and share the results. This is the step where data is turned into valuable business insights. Depending on the analysis conducted, the results are presented in a way others can understand it in the form of charts and graphs via a report, dashboard or app. This is the stage where you find answers to all the questions framed initially to address and solve the business problem.
How does data analytics drive business decision-making
From startups to multinational corporations, the adoption of data analytics is no longer a luxury but a necessity. All the steps that a data analyst and the team perform to bring out business solutions to solve a problem and move towards decision-making are how data continues its journey.
Let us try to understand the process of taking business decisions with data analysis in a real-time scenario. For instance, an insurance client outsourcing an IT consulting firm to analyse their data from all streams to address bottlenecks and to define business logics and strategies to increase their overall operational efficiency. The insurance client may be facing challenges such as retaining customers, performance of different claims and policies across different regions, trend of sales over time, agent performance, policy renewal rate, etc. The client might have data collected from all kinds of different sources but it does not make sense until it is analysed. Cleaning, transforming and analysing this huge data by dividing into categories and subcategories for different business scenarios and requirements will align the data towards meaningfulness. The representation of data in the final step, which addresses the challenges such as trends to identify causes for customer retention rate, customer satisfaction analysis, and risk assessment, can help the senior level in a company to implement solutions by taking appropriate decisions to these problems.
Streaming platforms such as Netflix, Hotstar, and Prime-Video use data analytics to understand viewer preferences, helping in the selection and production of shows that resonate with their audience1. Telecom and edtech companies and social media platforms all make use of data and data analysis to make informed business decisions to increase their revenue and to engage with the customer.
What is AI
Artificial intelligence (AI) is a way for computers to think and work like us. AI refers to a device’s ability to function similarly to human intelligence and perform tasks. The more we train it, the more it learns and re-learns to perform things better. There are many types of AI. Supervised, unsupervised training of data using AI tools and techniques to train and model the data to optimise business needs. Natural Language Processing (NLP) is a method of changing unstructured data into structured data that a machine can understand, interpret, and generate human language in a way that is meaningful and useful. Applications of NLP are used in machine translation, virtual assistants, chatbots, sentiment analysis, spam detection and many more. Machine learning (ML), Deep learning, and NLP along with coding languages such as Python and R are required for solving problems. Big Data, algorithms, Graphical Processing Units (GPU) and Application Programming Interfaces (APIs)-all of these are supporting resources to train it.
AI tools and techniques used for data analysis and making business decisions
By leveraging data in an effective manner, companies can improve their current operations and innovate and adapt to future challenges and failures. In this ever-evolving world encapsulated by data, harnessing data and bringing the best results in all kinds of business and operations is backed up by AI tools. Application of AI can range from meme creation, search engine optimisation (SEO), creating jobs, teaching and learning new languages, healthcare, art, government activities, space research, and education up to self-driving cars, auto-correcting space crafts and leading to an endless listing.
AI tools used in business decision-making increase efficiency by automating repetitive tasks. It reduces the overall human error and enhances accuracy. Manual and tedious tasks of analysing are done by the AI tools, which in turn help in in-depth analysis, analysing large and complex sets of data in the minimum amount of time and deriving meaningful insights. AI tools help budding entrepreneurs and startups to focus on bringing clients low cost and increased scalability. Companies that do not adapt to working and fusing the advantages of AI tools and techniques into their business operations will fall back in the competition.
AI tools and techniques go hand in hand. Business intelligence (BI) tools such as Tableau, Power BI, and Looker play a pivotal role in extracting the essence of data and presenting it in a way that drives success. All of the BI tools integrated with programming languages help analysts generate and automatically distribute reports on key business metrics. A company based on its needs chooses these AI tools and trains their employees to use these tools in the right manner to produce results. Robotic Process Automation (RPA) is another such technique employed by companies to automate invoice processing, smooth HR operations and onboarding processes, improving customer service.
The applications of AI in business are innumerable. In healthcare, agriculture, finance, education, navigation, e-commerce, inventory management, procurement, travel and tourism, logistics and supply management–all the fields. Any sector taken into consideration today is backed by data-driven AI. Decisions in any of the fields are made easily with more efficiency than before because of the application of AI techniques.
Conclusion
AI has become an inevitable part of human life. The competition in the market and in our daily lives makes it a necessity for us to become familiar and well-versed with employing AI for our benefit. AI systems, tools, and the different techniques used in industries make the best use of data as a primary source to derive and drive business growth. Since the beginning of the development and application of AI, there has always been a spectrum of fear lining all our AI-based decisions. It is now true that AI will not replace humans entirely but only the ones who do not embrace and employ it for their improvement. 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 optimisations will grow and deliver results beyond our expectations.
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