What Is Analytics?


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What Is Analytics?

Every day, organisations, individuals, and items generate vast amounts of data. We collectively send 294 billion e-mails and 500 million tweets in a 24-hour cycle. We plug 3.5 billion searches into Google. Four petabytes of data is produced by our connected cars. And our watches, refrigerators, and televisions create and share data on a regular basis.

A basic definition of analytics

Analytics is a branch of computer science that seeks out relevant trends in data using math, statistics, and machine learning. Sifting through vast datasets to discover, analyse, and share new insights and information is what analytics – or data analytics – is all about.

What is business analytics?

Simply put, business analytics is the application of analytics to business data. It focuses on the business implications of data, as well as the decisions and actions that should be made as a result of those implications.

The importance of business analytics

Today, the use of business analytics software is often the deciding factor distinguishing industry winners from losers. Leading companies use analytics to monitor and optimize every aspect of their operations – from marketing to supply chain – in real time. They rely on analytics to help them make fast, data-driven decisions, grow revenue, establish new business models, provide five-star customer experiences, empower employees, gain a competitive edge, and so much more. Companies without analytics – or without good analytics – are left to make decisions and do business based on gut instinct and experience alone.

Business Challenges with On Premise ERP

The top business benefits of analytics are:

  • Improved efficiency and productivity

  • Faster, more effective decision-making

  • Better financial performance

  • Identification and creation of new revenue streams

  • Improved customer acquisition and retention

Enterprise analytics is one of the most rapidly expanding segments of the enterprise software industry. The COVID-19 pandemic, which has pushed many companies to find new ways to make money, cut costs, and manage the tumultuous "next standard," has accelerated this development even more recently.According to Gartner, the most common use cases being accelerated as a result of the pandemic are analytics, business intelligence (BI), and data science, which are blowing Internet of Things (IoT) and cloud applications out of the water.Analytics' problem-solving and predictive capabilities are assisting companies in dealing with pressing pandemic-related issues such as effectively predicting demand, protecting at-risk workers, and detecting possible supply chain disruptions.

Get The Facts


of companies say analytics is important to their growth and digital transformation


of organizations are currently using advanced and predictive analytics


of global enterprises plan to increase their analytics spending in 2021

Four types of analytics

Descriptive, diagnostic, predictive, and prescriptive analytics are the four forms of analytics. This super tool kit, when used together, will provide decision-makers with a comprehensive understanding of what is happening, why it is happening, what will happen next, and what to do about it – in every situation.

  • Descriptive analytics

    The question "What happened?" is answered by descriptive analytics. Basic math, such as averages and percent changes, are used in this simple type of analytics to display what has already happened in a market. Descriptive analytics, also known as conventional business intelligence (BI), is the first step in the analytics process, and it serves as a springboard for further research..

  • Diagnostic analytics

    Diagnostic analytics provides an answer to the question, "Why did this happen?" It goes beyond descriptive analytics by using techniques like data exploration, drill-down, and correlations to delve deeper into data and pinpoint the causes of events and behaviours.

  • Predictive analytics

    Predictive analytics provides an answer to the question, "What will happen in the future?" This branch of advanced analytics predicts what will happen next by combining results from descriptive and diagnostic analytics with sophisticated predictive modelling, machine learning, and deep learning techniques.

  • Prescriptive analytics

    Prescriptive analytics provides an answer to the question, "What do we do now?" This cutting-edge method of analytics draws on descriptive, diagnostic, and predictive analytics results, and employs cutting-edge tools and techniques to evaluate the implications of potential decisions and decide the best course of action in a scenario.

Common components of business analytics

Business analytics is a broad field with many different components and tools. Some of the most common ones include.

Data aggregation

Data must be obtained from a variety of sources, organised, and cleaned up until it can be analysed. For analytics, a solid data management strategy and a modern data warehouse are needed.

Data mining

Data mining sifts through vast datasets, analyses data from different perspectives, and uncovers previously unknown trends, patterns, and relationships using statistical analysis and machine learning algorithms.

Text mining

For qualitative and quantitative research, text mining examines unstructured text datasets such as documents, e-mails, social media messages, blog comments, call centre scripts, and other text-based sources.

Forecasting& predictive analytics

Forecasting makes predictions about potential results based on historical evidence, while predictive analytics makes predictions based on advanced techniques.

Simulation and what-if analysis

Simulation and what-if analysis can be used to evaluate various scenarios and refine future decisions after projections and predictions have been made.

Data visualization & storytelling

Data visualisations, such as graphs and maps, make it simple to understand and communicate data trends, outliers, and patterns.

Examples of analytics

Analytics is used by companies of all sizes and in a variety of sectors, including retail, healthcare, and sports. Many analytics tools are specialised for a particular market, function, or line of business. Here are a few examples of today's analytics:

  • Financial analytics

    Financial analytics has traditionally been used to generate a regular collection of results. Financial analytics has grown as finance has taken on a more strategic role in the market, integrating financial and operational data with external data sources to answer a broad variety of business questions.

  • Marketing analytics

    Marketing analytics combines data from a variety of sources, including social media, the Web, e-mail, smartphone, and more, to provide marketers with a holistic view of their campaigns' performance. Users can mine millions of rows of data to increase campaign effectiveness, hyper-personalize marketing messages, evaluate social media sentiment, target potential customers at precisely the right time, and more.

  • Supply chain analytics

    They optimise everything from sourcing, manufacturing, and inventory to transportation and logistics using real-time data from a variety of sources, including Internet of Things sensors.

Modern analytics technologies

The era of artificial intelligence (AI) and machine learning has arrived, thanks to virtually limitless data storage and lightning-fast processing speeds. Analytics are being “augmented” by these developments, making them infinitely more efficient than before.

AI and machine learning analytics can identify trends, locate outliers, and make correlations in Big Data much more quickly and accurately than ever before. They can use the cloud to access more data from more sources, such as social media and Internet of Things sensors, to uncover insights, opportunities, and threats that would otherwise go unnoticed.

Machine learning algorithms can even simplify some of the most difficult steps in the analytics process, allowing even non-data scientists – not just data scientists – to use advanced and predictive analytics.

All of this, of course, is accessible through mobile devices, allowing users to get answers to ad hoc questions no matter where they are.

  • What is Advanced analytics?

    Advanced analytics refers to a form of analytics that employs sophisticated tools and techniques to explore data autonomously (or semi-autonomously). Predictive modelling, data and text mining, sentiment analysis, machine learning, neural networks, statistical algorithms, and complex event processing are examples of technologies and techniques that go beyond conventional BI capabilities.

  • What is Big Data Analytics?

    Big Data analytics is a form of advanced analytics that analyses very large datasets from a variety of sources, including structured, semi-structured, and unstructured data. Big Data analytics can uncover hidden patterns, unknown correlations, and other meaningful insights in datasets using complex tools and techniques like predictive modelling, what-if analysis, and machine learning algorithms.

  • What is Augmented Analysis?

    These powerful AI-driven analytics democratise advanced analytics by automating complex processes and allowing users to ask questions and understand answers with minimal training.

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