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
•     
• Today's World
•     
• Who Uses It
•     
• How Analytics Works
•     
• 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.

Analytics uses data and math to answer business questions, discover relationships, predict unknown outcomes and automate decisions. This diverse field of computer science is used to find meaningful patterns in data and uncover new knowledge based on applied mathematics, statistics, predictive modelling and machine learning techniques.

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.

How Analytics Works

Every business is an analytics business. Every process is an analytics process ripe for improvement. And every employee could be an analytics user in some way. No matter what you plan to accomplish with analytics, the first requirement for any analytics project is data. Once you have data, you need to analyse that data. And then you need to deploy the results of your analysis to drive decision making. The faster organisations can move through the analytic life cycle, the quicker they can achieve tangible value from their analytics investments.

At SAS, we see these three categories – data, discovery and deployment – as iterative steps of the analytics life cycle. Regardless of the scope or scale of your project, it should include all three steps. Let’s look at each step more closely.

Data

Data today is fast, big and complex. Analytics solutions have to analyse data of any type, including traditional structured data and emerging formats, such as streaming sensor data, images and video.

To access, prep, clean and govern that data, you also need a data management strategy.

How will you collect, clean and store your data? Data preparation is estimated to take up to 80% of the time spent on an analytics project. Could that time be better spent building models?

An intelligent analytics platform streamlines data preparation with native access engines, integrated data quality and data preparation tools that automate time-consuming tasks with AI.

Finally, data governance ensures your data can be trusted because you know the source and content and can monitor data quality. Data governance also makes it easy to protect data when appropriate.

Discovery

Discovery is all about exploration, visualisation and model building. Finding the right algorithm is often a process of trial and error. But when it’s easy to document, save and compare those trials, the process works the best it can.

Choosing the right algorithm depends on several factors, including data size, business needs, training time, parameters, data points and much more. Even the most experienced data scientists can’t tell you which algorithm will perform the best before experimenting with multiple approaches.

In fact, it’s common in the discovery phase to compare different models written in a different programming language with different data features.

For example, a recent analytics project that used object detection to identify tumors in medical scans of livers began with an exploration of several neural networks and a few weeks of comparing and documenting the results of different models.

This collaborative process works best when data scientists with different skill sets can write code in the language of their choice, and nonprogrammers can use a visual point-and-click interface to explore the results of different analytics approaches.

Deployment

If you want your analytics efforts to pay off, you need to deploy the results of your discoveries and put them to use. Machine learning and other models are not meant to sit on the shelf – you must use them to get the business value. Yet the deployment phase is where most organisations struggle the most.

Whether you’re building a single model or thousands, moving from selecting models to deploying models requires model management. Model management provides version control and helps you register, validate and centrally manage your models. It helps you develop procedures and rules for model deployment and monitoring. And you also get transparency about data and model use.

Your goal should be to build models once and deploy them anywhere – to executive dashboards, right into operational systems or built into other apps through APIs.

The Analytics Ecosystem

Analytics is booming. Hundreds of firms in the analytics ecosystem provide technology and services to help organisations store, access, analyse and present data. These range from data management and visualisation to advanced analytics and prebuilt analytics solutions, including many open source analytics options.

SAS is uniquely positioned to integrate with every player in the ecosystem. The SAS Platform works on hardware from any vendor, ingests all types of data, compares models from different languages, and offers consistent governance across the data, discovery and deployment phases of the analytics ecosystem.

Are you storing data in AWS or Hadoop? Extracting data from Twitter or Google Analytics? Analysing data in Python and SAS? Running programs on Intel or NVIDIA chips? Deploying results to desktops or IoT devices?

The SAS Platform can handle it all, plus any other legacy solutions you’ve already adopted. The result? Everyone from data scientists to IT to decision makers can work in harmony from the same analytics system. Plus, you’ll benefit from model management, model monitoring, model transparency, data lineage and integration across analytics projects and packages.

By orchestrating all the elements in the analytics ecosystem, an analytics platform helps accelerate the analytics life cycle, moving you from data to tangible results. Ultimately, this improves the return on investment for all your investments in analytics – data, technology and people – and positions you for success.

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.