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 modeling and machine learning techniques.
History of Analytics
In the past, data storage and processing speed limited analytics. Today, those limitations no longer apply, opening the door to more complex machine learning and deep learning algorithms that can handle large amounts of data in multiple passes.
As a result, the standard descriptive, prescriptive and predictive capabilities of analytics have been augmented with learning and automation, ushering in the artificial intelligence era.
This means we’ve gone from asking what happened and what should happen to asking our machines to automate and learn on their own from data – and even tell us what questions to ask.
Today most organizations treat analytics as a strategic asset, and analytics is central to many functional roles and skills.
One growing field of analytics powered by machine learning is natural language processing. Computers use NLP to interpret speech and text. Chatbots use NLP to answer customer service questions or offer investment advice in online chat windows. They can also offer scripted suggestions to live call center employees.
Machine learning and artificial intelligence have also brought us useful applications like self-driving cars and recommendation engines, which promise to taxi us around while we binge watch the next recommended TV series based on our tastes.
Of course, analytics shapes more than our leisure time. With faster and more powerful computers, opportunity abounds for the use of analytics and artificial intelligence. Whether it’s determining credit risk, developing new medicines, finding more efficient ways to deliver products and services, preventing fraud, uncovering cyberthreats or retaining the most valuable customers, analytics can help you understand what drives your organization’s success – and how it matters to the world around it.
Analytics in Today’s World
Put your analytics projects into action with these resources. Find what you need to plan your projects, restore trust in your data and develop an analytics strategy.
10 questions to kick off your analytics projects
How much does it cost? What problems are you trying to solve? Where is the resistance? These are just three of the key questions you should be asking to frame your analytics project.
Why trust matters with analytics
Getting more value from analytics and emerging technologies like AI starts with trust. How are analytics leaders building trust in data and analytics? MIT Sloan surveyed 2,400 business leaders to find out.
Building your analytics strategy
What’s on the agenda for the chief data and analytics officers? Defining an analytics strategy. Ensuring information reliability. Empowering data-driven decisions. And more. Download this e-book to help build your analytics strategy.
If you've been curious about how your small to midsize business could benefit from analytics but weren't sure where to start, this is the perfect webinar for you. This introduction explains how to get started with analytics for any size business.
The pressures of the digital world are hitting us all, and data overload is no longer limited to the “numbers people” within an organization. Can you name anyone in any organization who isn’t experiencing a need for speed, agility, flexibility and innovation? This makes analytics a priority for almost everyone, not just statisticians and data scientists.
As a result, organizations are looking for ways to make analytics available to more users by putting easy-to-understand insights into the hands of more employees, embedding insights directly into front-line applications or automating relevant decisions.
Technologies that offer point-and-click processes for dynamic, automatic model building are making analytics available to more users. Even complex questions can be answered by selecting a data source and stating your goal while a champion model is built in the background and natural language generation explains the model.
Organizations that lead with analytics can expect significant differentiation, outsized returns and sometimes longer-term survival.
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 analyze that data. And then you need to deploy the results of your analysis to drive decision making. The faster organizations 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 today is fast, big and complex. Analytics solutions have to analyze 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.
Discovery is all about exploration, visualization 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.
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 organizations 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 organizations store, access, analyze and present data. These range from data management and visualization 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? Analyzing 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.
Artificial Intelligence Solutions
Analytics evolves to artificial intelligence when learning is incorporated into the models, and the learning capabilities are automated. SAS analytics have an already strong foundation in AI, with solutions for advanced analytics, machine learning, deep learning, natural language processing and computer vision. Learn how to equip business leaders and data scientists for the future of AI, with the technology, skills and support you need to transform your organization for a future with AI.
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