Intel s Big Data Journey

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Intel’s Big Data Journey

Richard Mason- Marketing Analytics Product Owner – Intel IT March 2015

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Legal Notices

This presentation is for informational purposes only. INTEL MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY. Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.

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2014 Intel IT Vital Statistics

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>6,065 IT employees

50 global IT sites

>106,000 Intel employees

1

170 Intel sites in 66 Countries

61 Data Centers

(91 Data Centers in 2010) 80% of servers virtualized (42% virtualized in 2010)

>119,000+ Devices

100% of laptops encrypted 100% of laptops with SSD’s >53,700 handheld devices 164 mobile applications developed

Source: Information provided by Intel IT as of Jan 2015

1_{Total employee count does not include wholly owned subsidiaries that Intel IT}

does not directly support

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Intel IT’s Big Data Journey

Strategic focus Intel IT for the past 4 years has been increasing value

from Business Intelligence



Recognition that to drive greater value we needed to leverage more

types of data



Big Data, Semi Structured Data, Unstructured Data

From more sources…



Weblogs, Sensors, Social media….

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Big Data Opportunity: Enable Greater Business Value

The amount of data available and our

ability to harness it is increasing

Opportunity with Big Data is to Move up

the BI Maturity Curve

Decisions will be based less on

hindsight and more on foresight

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Big Data Challenges

Workforce

 Big data skills

 Identify opportunities  Big data consumption

skills in the business  Cultural Shift

 Business acumen in IT  Data algorithms

 Software

Technical

 Selecting the best platform for each business problem  Investment in

complementary capabilities, such as visualization

 Balancing total cost of ownership, supplier

instability, and leadership

The value of business intelligence gained from big data far outweighs technical and human resource investments

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PoC

IT-Managed Big Data Platform Evolution

Enterprise Data Warehouse (MPP) Line Of Business Data Marts Hadoop/MapReduce Platform 2012 2013 POC Visualization Platform NoSQL Data Stores e.g. Graph DB

2014 In-Memory Analytics Platform

2015+ Predictive

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Sales & Marketing Account Recommendation

Problem



Intel’s Online Sales Centers reach a distributed customer base

of resellers (140k)



How to focus limited sales attention on customers who are more

likely to buy

Solution



Intel IT worked closely with business to understand how we

focus on resellers



Built a model to consolidate data from various sources

Benefit



Enterprise deployment of the solution has generated

incremental revenue in excess of $35 million to date

Who

What

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Increase Impact of

Marketing Investments

Optimise xChannel Marketing Investments

Problem



Limited ability able to manage/optimise the effect

of marketing across channels

Solution



Consolidate key data sources (big and small) to

enable extendable integrated analysis platform

Benefit

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Factory Optimisation using IoT and Big Data

Problem



Unpredicted factory equipment failures, and component failures

increase the production costs

Solution

 Take advantage of available structured data from processes  Add semi-structured (sensor data)

 Tap into unstructured (like shift reports, operator log entries)

Benefit

 A pilot resulted in $9 Million ROI based on improved equipment uptime, increased yield and productivity

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Post Silicon Validation

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Problem

 Complexity and time to complete testing of a microprocessor design (1 year)

Solution

• Machine learning & data visualization to identify gaps and optimise test coverage.

Benefit

• The result is a reduction in post-silicon validation time by 25 percent which will improve our product time to market roughly a quarter

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Analytics in 2014

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Predicted part failure rates and sent

preventative maintenance crews to anticipate issues. Significantly reduced engine maintenance costs.

Data and Micro-Targeting helped

win re-election for Obama

Built a voter file that included voter history, demographic data, and preferences to identify “persuadables” to target. Linked voter files to zip codes and individuals within households to target for early voting.

Real-time Intelligence Boosts Home

Loans

Using analytics, sales representatives were able to understand past purchases on large home improvements to make real time offers for home loan products.

Realized 21% increase in customer spend in one month.

Predictive Analytics Increases Sales

Analyzed demographics and member behavior to customize marketing communications. Predicted buying habits of its 6 million members, boosting renewal rates from 85% to 90%+

How do other Organisations take advantage of Analytics?

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Taking Big Data to the Next Level at Intel

Continue to Grow Value from Big Data

 Continue maturity shift to predictive and prescriptive analytics  In Memory Analytics as an enabler for near real time analytics  Additional Data Stores to Support targeted workloads – GraphDB  Continue Skills Development

Broaden Access to Wider Audience

 Education - Continually Advertise Internally to get mindshare

 Push analytical tools out to the point of expertise and consumption  Advanced Visualisation techniques to help digestion of data

Intel s Big Data Journey