Analytics

Analytics is a field of computer science that uses data and math to answer business questions, discover relationships and uncover new knowledge. By blending applied mathematics, statistics, predictive modeling and machine learning, analytics can reveal meaningful patterns in data, predict unknown outcomes, forecast future trends and automate decision making.

• History
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• Today's World
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• Who Uses It
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• How Analytics Works
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• Next Steps

History and evolution of analytics

Strategic use of data and analytics can fuel innovation and enable smarter business decisions. To seize these advantages, many functional roles and skills across organizations routinely analyze data and incorporate automated business processes – not just statisticians and data scientists.

But where did the practice of analytics start?

The use of analytics tools can be linked with the origins of data management, which started in the 1890s with mechanical punch cards that recorded information (data) on a thick card. Analytics used to be restricted by the tedious nature of collecting and managing data stored at physical locations (with limited capacity).

Over time, many changes have transformed the fields of business analytics and business intelligence. For example, we now have vast types and amounts of big data at our fingertips, such as real-time data from sensors, social media data and IoT devices. Various forms of data storage exist and cloud computing options abound.

In an era of artificial intelligence (AI), the standard descriptive, prescriptive and predictive capabilities of analytics have been augmented with machine learning and automation. We no longer ask only what happened in the past and what should happen next. Now, we can ask our machines to automate and learn on their own from data – and even tell us what questions to ask.

Who's using analytics?

Recent advancements in technology have increased the potential of analytics. With more storage, computing power, processing capabilities and algorithms, it’s easier than ever to apply analytics to big problems and get answers from the data – in every industry.

Why analytics matters

Analytics is not just a tool for making processes more efficient – it helps people at all levels of an organization envision entirely new possibilities. Today, many organizations embed analytics into front-line applications and use it to automate decision-making processes.

As diverse data pours in from the digital world, organizations search for new ways to explore, manage and gain insights from this data. By prioritizing data analysis, they can learn how to differentiate from competitors, build outsized returns and strategically plan for long-term survival.

Flexible point-and-click analytics solutions can put easy-to-understand insights directly into the hands of more employees. Such analytics solutions can help organizations:

• Determine credit risk in the face of global economic crises, technology advances and increased regulatory requirements.
• Evaluate the performance of marketing activities and optimize ROI with the science of marketing analytics
• Detect and prevent fraud in banking, insurance and other industries – with the option of supplementing traditional analytics and AI with newer technologies like generative AI (GenAI).

Machine learning and AI applications

Analytics evolves to AI when learning is incorporated into models and the learning capabilities are automated. As technologies like machine learning and AI evolved over time, they brought us popular applications like self-driving cars and recommendation engines.

Now, machine learning and deep learning algorithms run with ever greater complexity and speed. Through automation, AI and cloud computing, organizations and researchers can tap into powerful AI models and automate complex decisions at the touch of a button.

Opportunities abound for advanced analytics and AI applications. For example:

• Natural language processing (NLP) relies on analytics powered by machine learning to help interpret speech and text.
• Chatbots use NLP techniques like retrieval augmented generation (RAG) models to answer customer service questions quickly and reliably or offer investment advice in online chat windows. They can also offer scripted suggestions to live call center employees.
• AI agents use data and AI to understand complex problems so they can reliably execute tasks (with varied levels of autonomy) to solve real-world problems.

How analytics works

Using analytics and AI effectively involves three phases: managing data, developing models and deploying insights. Like any journey, the faster you move through these phases of the analytics life cycle, the sooner you can achieve value.

Let’s take a closer look at each phase.