+ All Categories
Home > Data & Analytics > 5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri

5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri

Date post: 11-Jan-2017
Category:
Upload: spark-summit
View: 745 times
Download: 2 times
Share this document with a friend
45
Five Reasons Enterprise Adoption Of Spark Is Unstoppable Mike Gualtieri, Principal Analyst February 17, 2016 New York Twitter: @mgualtieri
Transcript

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

That’s 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 haven’t

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

[email protected]

Twitter: @mgualtieri


Recommended