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On the road to Big Data 20/20eBook: How HPE IT brought analytics to the business

Table of contents

Outlook

Where business meets science

The journey to analytics

Preparing for the journey to analytics

Transition from historical reporting to analytics

Leading the Big Data revolution

The art of the possible at HPE

Proving value throughout the enterprise

Capitalizing on customer data

Heading off IT problems before they happen

Detecting and preventing grey marketing of HPI-branded products

Matching the right skills to any given IT project

Deploying predictive analytics to improve sales

How can HPE Big Data Analytics benefit your enterprise?

Outlook

The enterprise has experienced a data explosion over the past few years that has redefined the possible. In the HPE eBook Big Data 20/20, HPE experts explore what the future of Big Data and analytics means for that enterprise. The widely varied deluge of data in companies presents both challenges and opportunities to deliver greater value and gain competitive advantage.

Hewlett Packard Enterprise is no stranger to this and has transformed over the past decade, setting the foundation for groundbreaking exploration that is changing the way the company does business. Using analytics, Hewlett Packard Enterprise is more accurately predicting sales, improving IT service levels, and addressing customer needs. The competitive edge is changing.

Read on to learn how HPE IT uses the HP Big Data platform to enhance the business with analytics.

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Where business meets science

Every enterprise needs innovation, but IT leaders often struggle to shift from simply managing day-to-day operations to solving problems, streamlining, and looking for the next big thing. Business leaders face the same challenges; while some make innovation a priority, quite often they are more focused on closing the quarter and hitting their forecasts.

But, when you bring the two groups together, remarkable things can happen. The business asks questions:

•How can I predict sales more accurately next quarter?

•How can I get a more granular view of my customers in a certain market?

Thanks to Big Data and predictive analytics technologies, IT can help reveal the answers that drive innovation, revenue, and business value.

Since 2010, HPE’s IT group has led the charge to build best practices around Big Data and advanced analytics technologies, discovering new ways to analyze and use Big Data in its colossal IT infrastructure to develop a 360-degree view of traditional business information, human interactions, and machine data.

Saum Mathur, the technology executive at the helm of HPE IT’s four-year transformation, started using data analytics while working in aerospace as a robotics engineer supporting NASA space shuttle missions. Having worked with Big Data since long before the term became part of our standard vocabulary, Mathur played a key role in HPE IT’s Big Data journey.

“It became IT’s responsibility to look strategically and introduce business systems that would help the business not just one quarter, or two quarters, but for a few years,” Mathur says. “That’s how it started. We became the visionaries, the strategists, and the catalysts of the change.”

The endeavor included consolidating hundreds of data marts—which are specific to a particular region, function, and/or business and follow varying standards—into a major enterprise data warehouse (EDW), HPE’s system for reporting and Big Data analysis.

This yearlong EDW consolidation enabled a number of Big Data analytics projects that not only allowed HPE IT to optimize its operations, but also cemented the partnership between IT and the business.

•Hewlett Packard Enterprise improved customer analytics to more effectively nurture customer relationships.

•HPE gained visibility into the “grey marketing” of its computer and printer products—enabling the company to act faster to control sales through unauthorized channels.

•Hewlett Packard Enterprise used predictive analytics to give sales teams better insight into their pipeline data.

“You’re going from looking at your results in hindsight to getting insight and then predicting the future,” said Mathur, describing a new model for strategic planning. “This is where business meets science.”

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Saum Mathur on Big Data 20/20More information is better only if you know what to do with it. Big Data represents “a fundamental change” in the enterprise—and not just for IT. The HPE eBook Big Data 20/20 describes three shifts Big Data brings to the enterprise. Here we present Saum Mathur’s take on each of those statements.

Big Data 20/20: A 360-degree view of data has a way of democratizing data by providing a single source of truth to everyone in the enterprise. Think of it as “analytics for the people.”Mathur: When HPE executives and others saw what was possible with analytics, they wanted to leverage analytics to help meet their groups’ goals. I like to call it “the art of the possible.” Start by giving the business near-term and fixed boundary predictions—so the analytics gives them answers instead of raising more questions.

Big Data 20/20: Every product will become an experiment.Mathur: HPE IT delivers its products and services in the form of projects created for its internal clients—users—at HPE. The advice I like to give to

anyone trying to do the same is to start shallow and narrow, instead of deep and wide. We started by analyzing certain product downloads on hpe.com, for example, and what we found—that certain solutions shouldn’t ever be offered as a download because they are meant for the data center—led a product’s general manager to change his strategy.

Big Data 20/20: Security and privacy concerns rise to the top.Mathur: Securing data is a must in today’s enterprise. In Big Data analytics, you need clear policies on how to use public data like social media posts about customers and employees for marketing or other purposes.

Saum Mathur previously served as CIO of HP Software and Vice President of Information Management IT. He recently left Hewlett Packard Enterprise to continue his Big Data journey with an HPE partner.

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The journey to analytics

Preparing for the journey to analyticsTo achieve similar results in your own organization, you can follow best practices applied within HPE IT. For starters, you need a solid framework from which to launch your Big Data analytics projects and test cases.

At Hewlett Packard Enterprise, Mathur and his team consolidated 700 separately managed data marts into one EDW over the course of a year. The challenge was not just the quantity of data marts, but also the fact that different people maintained them. Consolidating them into one EDW required

management of organizational change, as well as greater collaboration between IT and the business, since many of the data marts had built up over time and “belonged” to the business prior to consolidation.

“Data marts become like barnacles on a ship,” Mathur said.

“When the ship moves through the water, you collect these barnacles.”

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Taking the first step toward predictive analyticsHPE IT consolidated hundreds of fragmented data marts into a major enterprise data warehouse to establish reporting consistency and lay the groundwork for predictive analytics.

The consolidation served as a solid framework that the team could build upon. “There is no substitute for setting a good data foundation in your enterprise,” Mathur said. “There is no (easy button).”

700+ Data marts across HPE

Data mart Data mart Data mart Data mart Data mart Data mart

Data mart Data mart Data mart Data mart Data mart Data mart

Data mart Data mart Data mart Data mart Data mart Data mart

Data mart Data mart Data mart Data mart Data mart Data mart

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Transition from historical reporting to analyticsOnce HPE’s data was integrated into a single data warehouse, the next step was to find a solution nimble and powerful enough to run the kind of analytics the business was clamoring for.

In the course of consolidating its data marts to an EDW and launching pilot projects using HPE Vertica as an analytics engine, HPE IT learned three things:

1. IT teams were duplicating work by moving the same data for different projects.

2. They had to keep building integrations to move data between the EDW and HPE Vertica, which was costly and time-consuming due to the complex nature of the custom EDW.

3. The data being used for analytics was limited to a small subset of the data extracted from the EDW. They weren’t taking advantage of the massive body of data available.

These factors led to the decision to move the EDW’s entire 2 PB worth of data to HPE Vertica. Upon completion, data from over 300 systems would feed into HPE Vertica for analytics and reporting, increasing the value and accuracy of prediction significantly.

As former HP Vice President of Information Management Randy Bell explained, “You can run three kinds of analytics: historical, prescriptive, and predictive.” Hewlett Packard Enterprise wanted to be in a position to apply predictive analytics to as many business and IT issues as it could.

The transition from historical reporting to analytics requires a change in mindset and early wins that demonstrate value. Mathur recommended starting with near-term predictions and limited use cases to get the dialogue going with the business. “If IT is going to be the catalyst, IT has to begin with projects that deliver value, and demonstrate that value to the business,” said Mathur.

For further advice on how to get more value from your data warehouse, visit the HPE Vertica page.

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Leading the Big Data revolutionWith an established business-IT partnership, you can apply three additional best practices from Hewlett Packard Enterprise to ensure successful analytics projects:

1. Ensure that your IT teams have the right technical and business skills in-house. “For analytics, you have to understand the business a lot more intimately than in the past. IT teams need to be more business savvy,” said Mathur.

2. Make sure the required mindset shift takes place in IT. IT needs to be willing to try new things and work collaboratively. Find a use case early on that delivers more than money as a benefit. HPE’s Grey Marketing use case is a perfect example because it impacts both revenue and branding.

3. Focus on security. Hewlett Packard Enterprise needed to implement a variety of new tools for analytics and integrate security into each, to overcome the security limitations inherent in Big Data technologies. The team created centers of excellence around certain solutions to demonstrate IT’s capabilities before rolling them out to the business.

Imagine where you can go as a company once you have the framework for analytics in place and a handful of critical proof-point use cases under your belt. At Hewlett Packard Enterprise, “the business has seen the art of possible, and they are imagining all kinds of things that could be done,” Mathur said.

“In the enterprise, this is a revolution,” he concluded. “You need revolutionaries to lead the charge.”

That journey from disparate data marts to a single EDW platform and into an advanced analytics environment set the foundation for Hewlett Packard Enterprise to report on the same data across the enterprise and provide truly predictive analytics.

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The art of the possible at HPE

Proving value throughout the enterpriseHPE IT has demonstrated value through a number of analytics-driven projects that touched sales, marketing, brand protection, HR, and IT operations.

•A solution dubbed Customer 360 helped product managers in HPI’s Printing and Personal Systems Group use HPE Vertica to quickly gain a better understanding of customer buying patterns.

•The HPI Brand Protection Team enlisted the power of HPE Vertica to detect and minimize the reselling of HPE’s products via unauthorized distribution channels.

•HPE’s sales managers were able to focus on the most potentially profitable leads using predictive analytics for sales pipeline data.

•Using a proof of concept based on HPE IDOL, HPE’s human resources team has helped HPE Enterprise Services match its technical experts with client engagements.

•HPE IT Operations uses HPE Vertica to discover and resolve outages and issues faster, with fewer technical staff.

In each case, IT and business leaders worked together to design the solution, set up the proofs of concept, and launch production versions.

Capitalizing on customer dataProduct managers in HPI’s Printing and Personal Systems (PPS) Group wanted a clearer understanding of customer buying patterns so they could quickly respond with offers that meet customer needs and match where customers are in the buying cycle.

The Customer 360 solution has proven valuable to the PPS marketing team. It can perform persona mapping to the installed base of existing customers, rank customers based on their inclination to repurchase, and drive more sales because offers are more precisely targeted.

Achieving a 360-degree view of buying patternsCustomer 360, an HPE Vertica-powered solution, dramatically shortens the time it takes to create a clear picture of selected customer buying patterns, allowing the PPS team to deliver same-day offers to clients.

Business problemNeed for a faster and better understanding of customer buying patterns. Previous model required three days to gather, clean, score, and export data.

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The solutionImplement an HPE Vertica-based Customer 360 solution, which allows business to gain access to a greater set of data at a significantly faster pace, reducing the time from three days to 3.5 hours.

Business benefits•Across all hp.com domains are more responsive to online behavior.

•Improved propensity models drive more accurate and relevant customer scoring, thereby improving marketing campaign effectiveness and increasing revenue potential.

•Significant performance improvement in data load and scoring.

Performance at scale

Real-time analytics

Agile environment

Fastest time-to-value

Application integration

Massively parallel processing

High availability

Automatic database design

Advanced compression

Column orientation

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Heading off IT problems before they happenTo help manage the operations of HPE Global IT, HPE IT vetted HPE Operations Analytics, a Big Data analytics solution for IT, setting up several pilots before opting to roll it out across the enterprise.

Gary Brandt, HPE Global IT Functional Architect, works with the team that ensures service-level agreements with the business are met. Last spring he witnessed an unplanned validation of the system.

Operations Analytics was running in a test environment but was not yet in production.

“All of a sudden, the support team starts getting hammered with requests,” Brandt says, but IT couldn’t see what was wrong because everything looked healthy. “Within 20 minutes of creating a few dashboards in Operations Analytics, we found a pattern.”

Keeping HP global IT operations up and runningHPE Operations Analytics enables IT to dramatically decrease incidents, and troubleshoot and recover faster from incidents that do occur.

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Number of incident tickets per month

Average help deskcalls per month

Average number ofmajor incidents/meetings permonth

Unplanned downtime decreased 37%Average cycle time decreased 4%

Proactive monitoring of planned changes per month

Configuration items in UCMDB 68,770

24,000

5,000,000

47,000

66,000

2,000

Servers

Network devices

Applications900

300/800

Scheduled jobs executedper month

19,000,000Event notifications sentper month

1,500

It would have been impossible to cull manually through the log data to find it. The ability to analyze huge amounts of data simultaneously to detect anomalies and reveal hidden relationships saved the day. The root cause turned out to be a number of “starved ports,” which had caused a network traffic jam.

“We are now in a position where we can apply predictive analytics using Ops Analytics to look at historical performance data for all IT services, apps, and so forth,” Brandt says. This allows for a form of guided troubleshooting that greatly decreases the time and effort required by IT staff if an outage does occur. “It allows us to avoid repeating negative outcomes of the past to better prepare for the future.”

For more on how HPE IT leverages Ops Analytics, see this case study.

Detecting and preventing grey marketing of HPI-branded productsEstablished companies must protect their brands on many fronts. With a large line of consumer and business PCs and printers in the market, HP needed to find out the scale of the problem. By providing insights and then intelligence to pinpoint and counteract the grey market activity, HPI is able to recover as much discount revenue as possible and solidify trust among customers and authorized partners.

Using historical data, the team created a detection engine and predictive model. They moved quickly through the proof-of-concept phase in large part because of HPE Vertica’s speed, says Information Systems Architect Alexander Hipolito. The system allowed the Brand Protection Team to review four times more leads than before and increase the recovery of “discount dollars” from proven grey market deals.

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Using data analytics to thwart grey marketingWorking to thwart the sale of genuine HPI products that have been diverted from authorized distribution channels, the Brand Protection Team uses diagnostic and predictive analytics to boost the recovery of discount revenue.

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Substantial loss in profits annually

Decreasedauthorized-partnersatisfaction

Decreased end-customer satisfaction

Estimated loss to the IT Industry is $1 billion USD

•Customers may receive non-authentic, old, or used products

•May invalidate customer warranty or service agreements

•Impacts sales inventory in country

•Partners may receive non-authentic or tampered products

•Partners’ legitimate sales are affected negativelyImpact of grey

marketing

Matching the right skills to any given IT projectToday’s IT professionals are a talented bunch. In a very large enterprise, how do you track any expert’s potentially varied skills and expertise, going beyond what’s in a resume? To understand context and how skills are related, you need a human being. Or do you?

HPE IDOL (Intelligent Data Operating Layer) provides the power to locate contextual information in unstructured data. HPE IDOL enabled resource brokers in HPE Enterprise Services (ES) to surface highly accurate and targeted matches and ensure they were assigning the appropriate candidates to any given ES project.

For inspiration on more HPE IDOL use cases, visit the IDOL page.

Deploying predictive analytics to improve salesSales teams live and die by the type and accuracy of information they have on existing and prospective opportunities. The challenge for HPE sales managers was to understand the current status of opportunities, options, and quota gaps. They also needed to know the most productive opportunities to pursue and which pursuit strategies would be most effective.

Call it the sales holy grailHPE Vertica’s powerful engine uses the same data set to fulfill both the traditional business intelligence reporting and analytic needs. This capability enabled Hewlett Packard Enterprise to create a simplified, cost-efficient environment that gave the sales managers the data they needed to understand the win/loss probability of every opportunity based on history and previous data points.

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Refining sales pipeline data HPE Vertica enables traditional sales pipeline reporting and strategic forecasting.

Sales manager dashboard

Operational data with analytical results

Near real-time

Data subjects (salesforce.com) Master data Traditional

BI

Analytics

Leads

Data mining, selection, and deployment of model in Vertica

Opportunity

Customer

Product

Regions

Partners

Sales territory

Deals

Orders

Shipments

Traditional sales pipeline reporting

1. Forecasting

2. What ifs

3. Formulate sales strategy

4. Actionable guidance to sales managers

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How can HPE Big Data Analytics benefit your enterprise?

The path to predictive analyticsStarting with the consolidation of EDW from 700-plus fragmented data marts, HPE IT predictive analytics has helped HPE business units achieve beneficial outcomes.

2005–2008 2009–2013 2014 2015 2016…

• Integrated view of business

• Single data warehouse

•Alignment to enterprise data model

•One business intelligence user experience

•One delivery model

•Over 700 data marts

•No integrated view of business

•Multiple technologies and licenses

•Multiple data centers

• Business intelligence budgets ownedacross businesses

• Insight and foresight

• Big Data—HPE Vertica, HPE IDOL, Hadoop

•Diversified data management

• Flexible data model approach

•Tool suite targeted to segments

•Open governed delivery model

IntegratedFragmented Predictive

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“Our analytical journey really took off once we started investing in Hadoop and HPE Vertica as part of the HPE Big Data Analytics platform,” Mathur said. HPE’s Big Data Platform consists of Hadoop, HPE IDOL (for making sense of unstructured data), HPE Vertica, enterprise security, and a growing number of applications that integrate HPE software and industry-specific solutions.

The beauty of a platform like HPE’s Big Data Platform, added former HP Vice President of Information Management Randy Bell, is that you don’t have to build the connections between components yourself. Enterprises don’t want to have to become systems integrators just to be able to monetize Big Data analytics.

“If I can leverage a platform from HPE, which assures me that these technologies are integrated in a secure way, the benefit to me is astronomical,” Bell said.

By consolidating its data into one enterprise data warehouse and leveraging the HPE Big Data Analytics platform, Hewlett Packard Enterprise is driving predictive analytics across the company. From sales to brand protection to IT operations to HR, the power of Big Data has changed what is possible at HP.

Learn more atBig Data 20/20

HPE Big Data Platform

HPE Operations Analytics

© Copyright 2014, 2016 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

4AA5-5924ENW, March 2016, Rev. 1

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