Predictive Data & Analytics — What It Is and Why It Matters

Learn how predictive analytics can help organizations create a competitive advantage in the new era of data-driven business.

What Is Predictive Analytics?

Think of all the data you possess that is not being fully utilized. Not just data generated by standard customer transactions, but data collected from nontraditional sources such as social media, the web, voice files, and image documents. Today you can harness all this data—structured and unstructured alike—to gain competitive advantage. One key to obtaining these insights is analytics—in particular, predictive analytics.

Predictive analytics is the process of using all the different kinds of data that your organization creates and collects to gain insight into potential future outcomes. Note the word potential. Predictive analytics helps forecast what has a reasonable chance of occurring in the future, based on running what-if scenarios and assessing probabilities using existing data.

Predictive analytics is considered an advanced analytics technique. Advanced analytics methods differ from traditional ones in that they help businesses gaze forward instead of backward. So instead of what happened? You ask, what is likely to happen? For example, will this elevator require repair in 50 more hours of service? Or even, as your experience and expertise in analytics grows, what are we going to do about it? Or—most excitingly—as you master advanced analytics, you simply trust the system: analyze the data and do what is best for my business.

Analytics deployments often map to a five-stage maturity curve. Traditional analytics comprise the first two types of analytics on this maturity curve, advanced analytics the second three.

Predictive Analytics in Action

Using Predictive Analytics to Make Better Decisions

Learn how Intel is using predictive analytics and machine learning to mine large data sets to identify the most promising reseller business partners.

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Predictive Analytics 101

Take a crash course on predictive analytics: why it matters, how businesses can operationalize it, the impact on IT, and how Intel can help.

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How Do Organizations Benefit from Predictive Analytics?

Many enterprises today are eager to move beyond traditional business intelligence (BI) to advanced analytics such as predictive analytics.

But what can predictive analytics do for your business?

Many things. You’ll finally be able to harness your rapidly expanding volumes of data—both structured and unstructured—in real time to answer business questions about staffing, pricing, and inventory management, not to mention operational issues such as data center uptime and SLAs.

Two real-world examples:

  •  A large hospital, under pressure to reduce patient readmissions due to recent regulatory changes, built a predictive analytics model. The model considered new and unusual data—whether the patient suffered from poverty, poor literacy, limited English proficiency, little social support at home, or poor living conditions, among other factors—and combined it with patients’ electronic medical records to identify patients that were at high risk of readmission. By targeting those patients and giving them additional care, the hospital dramatically reduced readmission rates, avoiding millions in potential penalties while freeing up resources to care for more patients.1
  • A major clothing retailer placed RFID tags on products, and tracked the data within a predictive analytics model that gave it 100% visibility into inventory in near real-time. The resulting model could predict what styles and sizes stores needed to place on their shelves—so shoppers who came into stores could find what they wanted—and what the “hotspots” and best times would be for selling certain items. Not only did sales increase, but the brand was able to track and replenish inventory more efficiently.

Industry Applications and Examples

Biometrics, Predictive Analytics & Employee Health

See how a COVALENCE™ Health solution approached biometric data and predictive analytics to encourage better employee health.

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Reducing Hospital Readmissions Through Predictive Analytics

Learn how Intel and Cloudera used predictive analytics to help a large hospital group reduce readmission rates.

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Cloudera Distribution of Hadoop Solution

Retail transformation is being built on data. Learn why a retailer turned to Intel and Cloudera for a predictive analytics solution to help identify future purchasing behaviors.

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What’s the Optimal Infrastructure for Predictive Analytics?

Each type of analytics comes with a unique set of infrastructure requirements. Your first step is to look at your existing infrastructure. Analyze where your compute, network, and storage capabilities are aging and holding you back.

For predictive analytics, you may be required to modernize your infrastructure to deliver the performance, security, and memory or storage required. Your infrastructure must be flexible enough to run both commercial and open source predictive analytics solutions, and provide ample room to grow—It’s no longer enough to just grow in a linear fashion, your infrastructure might have to scale beyond your normal expectations to accomplish what you need.

The infrastructure should be capable of running a range of analytics workloads—from real-time, in-memory SAP HANA* or Oracle* Exadata* databases to streaming analytics (Storm*, Flink*) and big data Hadoop* deployments. Today you might want to build a Hadoop* data lake, but tomorrow, a standalone Spark* environment. Your infrastructure must be flexible enough to do both.

Don’t forget to consider cloud. Cloud compute and storage capabilities can augment your infrastructure when your goal is large-scale predictive analytics, and allow you to grow both on-premise and off-premise where appropriate. Cloud also can speed up the deployment of infrastructure or platform solutions that you don’t currently have in your plan.

To achieve all this, choose infrastructure components that adhere to industry standards, but don’t stop there. That’s just the minimum. Your components should also be tested on and optimized for predictive analytics workloads, going beyond—for example—general processor capabilities to customizable FPGAs for acceleration of targeted analytics workloads, memory, storage, Ethernet, and interconnect as well as platforms optimized for deep learning.

Analytics Products from Intel

Intel Xeon® Scalable processors

Take a Major Leap Forward with new Intel Xeon® Scalable processors and see how You can drive actionable insight, count on hardware-based security, and deploy dynamic service delivery.

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Intel® SSD Data Center Family

Eliminate bottlenecks with the best data center storage solutions. Modernize your infrastructure to keep up with the demands of digital business. Intel® SSDs for the data center are optimized for performance, reliability, and endurance.

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How Does Open Source Play into Predictive Analytics?

You can deploy predictive analytics without evaluating open source tools but you’d be missing out. The open source analytics community is broad and deep, and has produced a stellar portfolio of advanced analytics tools, from Hadoop*, to Spark*, to Hive*, plus a multitude of others that are constantly being improved and advanced by the global open source community. 

Open source predictive analytics tools are—on the surface—significantly less costly to deploy than proprietary analytics platforms. This makes them attractive to companies just dipping into the predictive analytics waters. They are also extremely flexible, providing you with a multitude of deployment options for a wide range of analytics workloads. 

Yet this very flexibility makes them challenging to use. You need people with emerging and advanced skills—data scientists, data engineers, and data analysts—if you go the open source route. You can hire them or you can grow the expertise in house, which takes time. Many companies end up hiring consultants, which adds to the cost of open source initiatives. 

Some companies choose to do proof-of-concept (POC) tests of predictive analytics systems using open source, then switch to proprietary solutions for production. But increasingly, open source is playing a key role in production solutions because of the opportunities it offers companies to leverage all their data—structured and unstructured—and to test exciting new analytics concepts. This often leads to a mix of open and proprietary technologies, which allows you to pick the best solutions for different jobs, and combine them for optimal results.

Intel IT Peer Network

The Next Step in Big Data Analytics

See how predictive analytics fits into the bigger picture of big data analytics, and what your organization needs to do to succeed.

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A Startup Approach to Predictive Analytics in Healthcare

Learn how to think about leveraging the power of predictive analytics, and why advanced analytics is important to organizational growth.

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Moving from Reactive to Predictive Analytics

The path of analytics transformation is changing. See how you should approach your organizations data and analytics evolution.

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Making a Business Case for Predictive Analytics Inside Your Organization

One of the biggest barriers to adopting predictive analytics is estimating the value a proposed initiative will offer your business. 

You know the value of your BI solution today because you can't live without it. But establishing the value of predictive analytics? First, you must justify the upfront expense of building out a new infrastructure, hiring or growing the skills, and purchasing the analytics platforms or tools. You have to prove that the investment is going to provide your business with more than the rear-view mirror view that traditional analytics has been giving you for the last decade. 

The overarching first rule for ensuring value: engage the business. Predictive analytics doesn’t exist in a vacuum. You use it to solve business problems. Ask your business users to identify their pain points that can be solved with predictive analytics. Pick a challenge that they’ve been struggling to solve, but which has been beyond the capabilities of your current data sources and analytics systems. Or choose a new problem that they’ve never considered trying to solve before because the data sources were new, untested, or unstructured.

The second rule is start small. Predictive analytics can be overwhelming. It is, after all, a highly complex area that changes every day. New solutions and new tools are always coming onto the market—particularly in the open source world—and it’s not clear how they all integrate with each other. There are also data security considerations to consider. 

Ask yourself some basic questions: What exactly am I trying to achieve for the business? How will predictive analytics give me more insight than traditional analytics? Is the data that I need available—and will it provide competitive business advantages? What’s the expected ROI?

Data and Analytics Basics

The Analytics Maturity Curve

See how Intel’s Analytics Maturity Curve breaks down the past, present, and future of analytics into five phases. And, learn how the right analytics solutions can give you a competitive edge.

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Building a Data-Driven Culture

Get a better understanding of what it takes to build a data-driven business by reading some of the best practices we’ve learned from working with hundreds of businesses.

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The Advantages of Partnering with Intel

Intel is driving innovation to help you thrive in predictive analytics. Intel® technologies span every aspect of infrastructure to help businesses harness predictive analytics for competitive advantage.

Specifically, Intel defines and drives the standards that drive compute, network, and storage for the world. Intel innovations have been tested on and optimized for the broadest ecosystem of predictive analytics solutions in the industry and support a predictive analytics-capable infrastructure for a broad range of workloads—whether running on open source or proprietary platforms. Because the new predictive analytics platforms are all based on Intel architecture, you have the opportunity to do analytics everywhere, opening up possibilities for distributed analytics as part of every deployment.

When it comes to compute, Intel® processors cover the full range of predictive analytics needs. The portfolio extends beyond general-purpose Intel Xeon® processors to critical ancillary technologies such as customizable FPGAs for acceleration of analytics workloads, memory, storage, ethernet, and interconnect.

When modernizing your storage for predictive analytics, Intel technology delivers breakthrough application performance—and faster time to insights. Designed for seamless performance and enhanced capabilities with Intel® processors, chipsets, firmware, software, and drivers, Intel storage solutions provide speed, low price points, and performance.

Intel also helps with the networking aspects of predictive analytics. Delays in moving your data from your data lake or warehouse to your compute infrastructure can be costly in real-time operations. To help your organization avoid these delays, Intel provides one of the fastest fabrics available to speed predictive analytics workloads across network pipes.

And don’t forget security: Intel hardware and software security tools help protect access security, as well as both security of data at rest and data in motion.

Click here to learn more about how Intel can support your organization’s predictive analytics strategy.

Take Advantage of Our Advanced Analytics Ecosystem

Microsoft

Windows Server* 2016 empowers the advanced data center with software-defined compute, storage, and network capabilities that are elastic and cost-effective. It’s optimized for Intel's technologies to deliver great performance, optimization, efficiency, and scalability.

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Cloudera

Cloudera and Intel deliver enterprise-grade innovations to the Apache Hadoop* framework in security, performance, management, and governance.

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SAS

The power and performance of next-generation Intel Xeon® Scalable processor platform makes it possible for SAS to help retailers run more complex analysis faster than ever.

Learn more about the SAS partnership

SAP

SAP HANA2* and the Intel Xeon® Scalable processor platform drive innovation and results by ensuring data is ready for business decision makers, without interruption.

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DELL EMC

Dell EMC IT utilized the Intel Xeon® Scalable processor platform to develop a data lake architecture to enable real insights that move businesses forward.

Learn more about the Dell EMC partnership

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1https://www.thailand.intel.com/content/www/th/th/healthcare-it/solutions/documents/predictive-analytics-reduce-hospital-readmission-rates-white-paper.html?wapkw=hospital+readmission+rates