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 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.
Fast food, faster insights: Analytics keeps the line moving
Family-owned Boddie-Noell, the US’ largest Hardee’s fast food franchisee, used to rely on spreadsheets and manual processes to make business decisions. As a result, they were always looking back at the previous day, week or month. But just like food, data can get stale. Recognizing that a modern, analytics-based approach could take them to the next level, Boddie-Noell turned to SAS Viya to get real-time insights that have helped them work smarter, learn faster and exceed expectations – all while serving their customers’ favorites.
Analytics in today’s world
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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.
Banking
Banks and financial services organizations use analytics to manage risk, detect fraud and personalize customer experiences. Analytics can identify anomalies in transaction patterns in real time to help prevent financial crimes. Predictive analytics helps banks anticipate customer needs to improve engagement and satisfaction. Advanced AI models support credit risk assessments to optimize lending decisions.
Insurance
Insurers depend on analytics to assess risk, detect fraud and streamline claims processing. By analyzing vast amounts of structured and unstructured data, insurers can price policies more accurately. Fraud detection models, often powered by AI – identify suspicious claims to reduce losses. Advanced AI algorithms enhance customer engagement by serving up personalized policies and proactive support.
Life sciences
Life sciences organizations use analytics to accelerate drug discovery, improve clinical trials and enhance patient care. Predictive analytics optimizes clinical trial recruitment and monitors treatment effectiveness in real time. Hospitals and researchers use analytics to detect disease patterns, enabling early interventions and personalized medicine. AI-driven models analyze vast biomedical data sets to identify promising drug candidates, which reduces time to market.
Public sector
Government agencies rely on analytics to improve public services, enhance security and allocate resources efficiently. For example, law enforcement uses analytics to identify crime trends, while social services rely on data to improve program effectiveness. Predictive models, often incorporating AI and machine learning, help detect fraud, optimize tax collections and improve emergency response planning.
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.
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What is AI modeling?AI modeling involves creating programs that use one or a combination of algorithms to allow computers to think, learn and predict outcomes. Much like a human brain, AI models absorb input data – numbers, texts, images, video, sound – to learn how to predict outcomes or solve specific tasks without explicit instructions at every step.