Improving Business Intelligence with In-Memory Analytics

Learn how In-Memory Analytics can help organizations create a competitive advantage in the new era of data-driven business.

What Is In-Memory Analytics?

Today, data lives everywhere. Its volume, velocity, and variety are increasing beyond all expectations. Harnessing data analytics has already helped many leading brands move beyond traditional business intelligence to real-time analytics for greater efficiencies, risk avoidance, and enhanced revenue through customer-tailored offerings. Businesses that are slow to tap value from data with analytics solutions can put themselves at a significant competitive disadvantage.

Speed is the key requirement for an IT infrastructure that can support analytics-driven decision-making. The business value of decision-support solutions often depends on being able to deliver results at least thousands of times faster than conventional solutions. Achieving this lofty goal requires taking a new approach to processing: In-memory computing.

The concept of in-memory computing is simple. In the conventional approach to processing data, the data resides on a hard disk in the system or attached by a network. When needed, it’s called into the local system memory (today known as RAM), and from there moves to the CPU. The long seek times for data residing on disks often can become a bottleneck.

With in-memory computing, data is stored directly in system memory. This architectural approach dramatically reduces latency by eliminating the time spent seeking data on the disk and then shuttling it closer to the CPU. In-memory computing has the potential to be significantly faster than the conventional approach.

In-memory analytics often has two other important technical components that increase the performance of the software.

  • Columnar data storage. Instead of the traditional two-dimensional structuring of data (rows and columns), in-memory analytics data has a one-dimensional, linear structure.
  •  Massively parallel processing. In-memory analytics makes full use of multi-core, multi-thread processor capabilities, which are freed to operate on the data given the reduced access latencies.

In-Memory Analytics in Action

Real-Time, In-Memory Analytics Overview

Learn how the The Intel® Xeon® processor E7 v4 product family can help can power the data-centric enterprise, and provide you with the data you need for real results.

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SAS In-Memory Analytics*

See how Intel and SAS are teaming up to help businesses generate data insights and business value more quickly by combining the Intel® Xeon® processor E7 v3 product family and Intel® SSD Data Center family for PCIe* with SAS In-Memory Analytics*.

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Big Data, Hadoop* & In-Memory Analytics

Leverage our planning guide to better understand what it takes for IT managers to plan and implement big data analytics initiatives. Start with the basics and then learn more about the Apache Hadoop* framework and in-memory analytics.

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The Maturing Business Intelligence Portfolio

Business analytics, like many IT initiatives, can become even more valuable to an enterprise as organizations gain experience and operational maturity about delivering solutions. More traditional or conventional approaches, such as descriptive and diagnostic analytics, that tell a business what happened: “where we were” instead of “where we could go.”

The next step on the maturity scale, predictive analytics, looks forward. It replaces a seat-of-the-pants approach to decision making with one that’s disciplined and data-driven. Predictive analytics operates in real time. Often, it extends its reach to people who are on the front lines making constant low-level decisions, e.g. which pallets to load into which container.

These small decisions aren’t extremely important in themselves, In the aggregate, however, they can make a big difference to the bottom line, either through cost avoidance or increased revenue. Over time, predictive analytics will enable businesses to automate processes that are now manual so that they move at “compute speed.”

In later stages of the maturity model, prescriptive analytics, explores what-if scenarios on larger time scales and projects possible outcomes. Prescriptive analytics might be used, for example, to determine the optimal location for a new retail outlet.

All of these forward-looking approaches make use of data within the organization—sometimes including transactional data—as well as many different forms of data available from third party aggregators.

In-memory analytics solutions may not replace conventional data warehouses but can enhance an organization’s total decision support capability. It’s possible to get started with in-memory analytics before engaging in a wholesale re-platform of your business.

Data and Analytics Basics

The Analytics Maturity Curve

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Building a Data-Driven Culture

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Business Value of Analytics

The sources of data with business value are endless: Data from factory sensors, from multiple retail channels, from social media, even from weather satellites and other third-party feeds. New developments like smart cities and the internet of things will only add to the load. Companies can't ignore this data if they want to remain competitive. Properly analyzed, it can increase sales by predicting the up sell most likely to succeed, cut distribution costs with smarter routing and inventory management, reduce manufacturing costs and improve quality with sophisticated root cause analysis—again, the list is virtually endless.

Sometimes, the path to actionable information derived from this flood of data is simply finding patterns in what already happened. In other instances, real-time results are needed to improve the customer experience, stop a malware exploit, or prevent fraudulent use of a credit card, to cite a couple of examples.

The barriers to its adoption are falling. All the major IT vendors offer analytics solutions, and there are numerous vertical solutions as well. The number of data scientists with the requisite skill sets to both use and support sophisticated analytics is growing. Also, many companies are working to “democratize” the use of analytics through simpler interfaces and built-in algorithms. The publicity surrounding analytics (along with its solid business case) have made funding easier to obtain.

The bottom line is that there is clear business value in analytics. Numerous brands are already using in-memory analytics to boost revenue and cut costs. Those who don’t pursue these operational advantages are at risk of competitive disadvantage.

Analytics in Action

In-memory analytics is a proven, game-changing technology that is having a huge impact right now on every aspect of business and organizational management, including manufacturing, supply chain management, human resources, marketing, distribution, finance, and more.

For many organizations, the key benefit of in-memory analytics is the ability to process vast quantities of data fast enough so that the resulting insights become a difference maker. Pattern recognition involving large amounts of data is a key use case. The IRS*, for example, analyzes tax returns as they are being processed to identify patterns of mistakes or problems. The result has been interventions that stopped the IRS from erroneously refunding several hundred million dollars.

Predictive analytics is perhaps the most useful application of in-memory technology. At UPS*, predictive models for delivery operations are responsible for reductions in miles driven, which saves the company money and reduces the overall company carbon footprint.1

Predictive analytics is particularly effective in retail. A retailer, for example, has the ability to initiate an in-memory analytics project to produce targeted marketing campaigns, resulting in reduced costs. Any industry can likely benefit from approaches such as this.

Industry Applications and Examples

In-Memory Data Platform

Learn how we are optimizing our supply chain with our In-Memory Data Platform, and see how in-memory data platform supports Intel IT’s goal of a dynamic supply chain that can instantly respond to changes.

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Near-Real-Time Inventory Management

Read how Intel and SATO Global Solutions have partnered to develop am data-driven retail solution that can help to integrate near-real-time inventory data and customer data for improved efficiency, sale, and customer experience.

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Leverage In-Memory Computing with AWS

Develop a cost-effective big data engine in the cloud. See how you can scale-out, scale-up, or bring data to the edge with a with the AWS platform using the latest Intel® Xeon® processors.

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Intel IT Peer Network

In-Memory Dream Team

The business-intelligence game is getting tougher. See why in-memory analytics can’t be viewed as a luxury in today’s aggressive, fast-paced market.

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The Next Step in Big Data Analytics

Ready to advance analytics in your organization? Learn what three things you’ll need to making the leap into predictive and prescriptive analytics.

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2017 Breakthrough in Persistent Memory

Read about the new generation of Intel persistent memory, based on the groundbreaking 3D XPoint™.

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Step-by-Step Guide to Getting Started

Here is a five-step process for getting started with in-memory analytics.

  1. Identify pain points. Consult with business unit leaders to create a list of pain points that would be difficult or impossible to solve with existing systems. This list should be prioritized based on those items that align with existing strategies, hold promise for new insights, are within the skill capabilities of the IT organization, and have a solid business case. With some iteration, the end result should be a clear list of objectives and the resources to achieve them.
  2. Research and familiarize yourself with the available analytics solutions on the market. (Intel is an excellent resource for this information.) In light of that knowledge, evaluate your current infrastructure. It’s important to understand where the data to be analyzed will be coming from, who owns it, and what measures will be necessary to ensure data quality and security.
  3. Identify and cultivate the skills your team will need. Hire new talent or plan to outsource some tasks if necessary. In many cases, new employees will be coming with skill sets that match your needs.
  4. Establish technology requirements above and beyond what’s currently in place. In-memory analytics requires modern hardware, including compute, storage, and networking infrastructure. You will also need to determine which analytical queries and algorithms you’ll need to generate to achieve the desired outputs, and then decide how those outputs can be presented in engaging ways. Look to both proprietary and open source solutions for your software, as a wealth of options exist.
  5. Create the final use cases for the project. Determine what data will be used, and map out data flows. Then develop a test environment for a production version.

If this sounds like a process you already do for your other projects, it should. Advanced analytics does not require a new approach to how you manage your IT portfolio. It merely requires you to think outside the box for new solutions.

In Memory Analytics: Build Your Stack

See how in-memory analytics help create new opportunities, drive enhanced service delivery, and reduce time-to-insight.

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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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Intel in Analytics: Hardware and Beyond

Intel offers the industry’s broadest and platform for in-memory analytics, with significant capability to scale with increasing workloads., It is capable of supporting a variety of diverse analytic workloads, including real-time, in-memory databases, scale-out Spark* deployments, high performance computing (HPC), and machine learning. It incorporates compute, storage, memory, fabric and networking technologies, all optimized for “work better together” performance where the whole is greater than the sum of the parts.

The result is a flexible infrastructure, with built-in security that delivers the high performance needed to meet today’s needs while forming a solid, trustworthy foundation for the future.

Intel® architecture gives IT organizations a consistent baseline across their infrastructure, with a predictable path for scaling analytics initiatives over time and a broad product offering, which means there is no need to support multiple architectures. It also offers a consistent software-programming model for developers, enabling them to focus on enhancing performance and features.

Intel architecture is supported by a rich ecosystem of hardware and software partners. Intel actively collaborates with these partners on an ongoing basis to help optimize their product performance on Intel® architecture.

With Intel as an analytics partner, organizations have the flexibility to choose an open source software platform or one of the industry-leading commercial platforms, such as those from SAS*, SAP*, Oracle*, IBM* and Microsoft*, and many others.

With its history of success, Intel is a rich source of information about what it takes to succeed with an in-memory analytics initiative.

Intel is primarily known for its processors, and for many, the Intel® Xeon® processor family has long been synonymous with in-memory analytics. The full story, however, is much, much broader, and well worth investigating. Click here to learn more about how Intel can help your organization to develop an in-memory 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.

Learn more about the Microsoft partnership

Cloudera

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

Learn more about the Cloudera partnership

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