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5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri

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  • Five Reasons Enterprise Adoption Of

    Spark Is Unstoppable

    Mike Gualtieri, Principal Analyst

    February 17, 2016 New York

    Twitter: @mgualtieri

  • #Customers

  • REASON

    ADOPTION1. Customer experience is a top

    priority for enterprises.

  • 2015 Forrester Research, Inc. Reproduction Prohibited 4

    52%

    53%

    53%

    54%

    58%

    64%

    64%

    65%

    66%

    73%

    75%

    0% 10% 20% 30% 40% 50% 60% 70% 80%

    Better leverage big data and analytics in business decision-making

    Create a comprehensive strategy for addressing digital technologies like mobile,social & smart products

    Create a comprehensive digital marketing strategy

    Better comply with regulations and requirements

    Improve differentiation in the market

    Increase influence and brand reach in the market

    Address rising customer expectations

    Improve our ability to innovate

    Reduce costs

    Improve our products /services

    Improve the experience of our customers

    A strong majority of business leaders prioritize improved customer experience and products.

    Base: 3,005 global data and analytics decision-makers

    Source: Global Business Technographics Data And Analytics Online Survey, 2015

  • For you For all For segments For you

    Demographic

    Relationships

    Hyper-Personal,

    Real-Time

    Relationships

    Personal

    RelationshipsMass

    Relationships

    Cu

    sto

    me

    r E

    xp

    eri

    en

    ce

    1800 1900 1950 2000 2015

  • Customers want and increasingly expect

    to be treated like celebrities.

  • Learn individual customer

    characteristics and

    behaviors (understanding)

    Detect customer needs and

    desires in real-time

    (context)

    Adapt applications to serve

    an individual customer

    (experience)

    Celebrity experiences must:

  • 2015 Forrester Research, Inc. Reproduction Prohibited 8

    Fortunately, every industry is graced with more data Richer transactional data from portfolio of hundreds of

    business applications

    Usage and behavior data from web and mobile apps

    IoT device sensor and event data

    Social media data

    Log data

    Data economy firms buying and selling data

  • Using your best estimate, what is the size of

    all data stored within your company?

    Source: Forrester Research, September 2015

    Base: 100 US Managers and above currently using Hadoop for processing and analyzing data.

    Enterprises have plenty of data from both internal and

    external sources

    10-49 Terabytes

    5% 50-99 Terabytes

    12%

    100-500 Terabytes

    54%

    Greater than 500

    Terabytes29%

    Internal business

    data49%

    External source data

    51%

    What % of the data available is from internal business applications (ERP and business

    applications) versus external sources (social, IoT)?

  • 2015 Forrester Research, Inc. Reproduction Prohibited 10

    Learn Model Detect Adapt

    Four kinds of analytics are necessary

    Predictive

    Analytics

    Streaming

    Analytics

    Descriptive

    Analytics

    (Advanced Analytics)

    Prescriptive

    Analytics

    Batch Real-time

    Most firms invest here They must invest here too

  • 2015 Forrester Research, Inc. Reproduction Prohibited 11

    Source: Forrester Research

    Thats why use of advanced analytics is surging

    What is your firm's/business unit's current use of the following technologies?

    Source: Forrester's Global Business Technographics Data And Analytics Survey, 2015 and 2014

    Base: 1805 (2015), 1063 (2014)

    19%

    19%

    24%

    31%

    34%

    22%

    22%

    35%

    31%

    43%

    53%

    54%

    50%

    50%

    69%

    39%

    42%

    42%

    42%

    42%

    43%

    43%

    46%

    48%

    52%

    54%

    55%

    56%

    57%

    69%

    Non modeled data exploration and discovery

    Search/interactive discovery

    Streaming analytics

    Metadata generated analytics

    OLAP

    Advanced visualization

    Text analytics

    Location analytics

    Predictive analytics

    Process analytics

    Embedded analytics

    Web analytics

    Dashboards

    Performance analytics

    Reporting

    2015

    2014

    Most of your

    competitors

    still havent

    started!

  • #Hadooponomics

  • REASON

    ADOPTION2. Hadoop and friends makes

    analytics of all kinds cost-effective at scale.

  • #

  • 100%Number of enterprises that

    Forrester estimates will adopt

    Hadoop and friends!

  • Hadoop is designed for volume.

  • Spark is designed for speed.

  • 2015 Forrester Research, Inc. Reproduction Prohibited 18

    Spark and Hadoop can coexist in the same cluster.

  • #Perishable

  • REASON

    ADOPTION3. Perishable insights must be captured and used before they

    expire (or rot).

  • Perishable insights can have exponentially more

    value than sleepy, after-the-fact traditional

    historical analytics.

  • All data is born fast!

  • 11001

    0011

    011

    00

    1

    01

    00

    10

    011

    011

    00

    1

    0100

    1100110

    110

    1

    01

    00

    10

    011

    011

    00

    1

    Custo

    mer

    Data

    Tra

    nsactio

    ns

    Data

    Ware

    hosue

    IoT

    But, analytics is usually done much later.

  • #WhyWait

  • How can you prevent this dude from fleecing

    you right now?

  • What offers should you make to your customer if

    they are within proximity of your store right now?

  • Resilient Distributed Datasets (RDD) is a

    generalized data structure that can cache data in-

    memory and spool to disk if necessary.

    58,000x

  • 2015 Forrester Research, Inc. Reproduction Prohibited 30

    Spark data processing jobs run exponentially faster when the data set fits in memory.

  • 2015 Forrester Research, Inc. Reproduction Prohibited 31

    Why not just pop your data in-memory?

  • Planning, implementing, or expanding the use of

    in-memory data platform.

    73%

    Base: 1,805 global data and analytics decision-makers

    Source: Forrester Global Business Technographics Data And Analytics Online Survey, 2015

  • #MMLA

  • REASON

    ADOPTION4. Massive Machine Learning

    Automation (MMLA) is the future of data science.

  • Massive Machine Learning Automation (MMLA)

    is the only competitive way forward.

  • Data scientists have slogged through the same iterative process for 20 years

  • LEARNING AUTOMATION

    MASSIVE MACHINETools and technologies that automate through

    configuration rather than coding the process of

    data preparation, model building using statistical

    and machine learning algorithms, model

    evaluation, and model monitoring at scale.

  • The seven characteristics of massive machine learning automation.

  • REASON

    ADOPTION5. Spark community is diverse

    and innovating fast.

  • 2015 Forrester Research, Inc. Reproduction Prohibited 41

    Learn Model Detect Adapt

    Only the analytical enterprise can compete and win in the age of the customer

    Predictive

    Analytics

    Streaming

    Analytics

    Descriptive

    Analytics

    (Real-time)

    Prescriptive

    Analytics

    (Continuous Batch)

  • #Insights

  • I need

    insights.

    You shall have

    none - until you

    build a continuous

    analytics pipeline.

  • 2015 Forrester Research, Inc. Reproduction Prohibited 44

    Generate industrial strength analytics with Spark and Hadoop

  • forrester.com

    Thank you

    Mike Gualtieri

    mgualtieri@forrester.com

    Twitter: @mgualtieri

    mailto:mgualtieri@forrester.com

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