Value Creation for SMBs with Big Data

transcript

VALUE CREATION FOR SMBs

WITH BIG DATA

by: Andrey Sadovykh, Paul-Emile Poisson, Aleksei Papin

1. Big Data Phenomenon

• Understanding sources and trends of the phenomenon

• Overview of the Big Data market and SMB sector

2. Big Data Service Providers

• Current services in the market for SMB (bird’s eye view)

3. SMBs Survey

• SMB Segments

• Understanding Big Data

• Value Creation and Go-To-Market

• SMB Pain Points for Big Data

• SMB Trends

4. Recommendations and Lessons Learnt

Summary

PART #1: BIG DATA

PHENOMENON 3

Big Data = Volume + Variety + Velocity

4 Source : IDC & EMC

Volume = sheer amount of data

Variety = polystructured nature = text, audio and video

Velocity = rate at which it is generated and analyzed

Big data

commonly

characterized

by three

vectors:

Data grows exponentially

By 2020, the digital

universe will amount to:

over 5,200GB per

person on the planet

In December 2012 the size of the digital universe (that is, all the digital

data created, replicated and consumed in that year) was estimated to be

2 837 Exabyte (EB) - Forecasted to grow to 40,000EB by 2020

One Exabyte = 1 000 petabytes (PB)

One Exabyte = 1 000 000 terabytes (TB)

One Exabyte = 1 000 000 000 gigabytes (GB)

Only ½ % of Data is analyzed

In 2012, 2 837EB generated - just ½% actually analyzed.

That still amounts to 14EB (or 14.185 million terabytes)

Not all of data

generated will be

actually useful

Practically all that we do creates data

1. Number of @-mails sent every second : 2,9 million

2. Video uploaded to YouTube every minute: 25 hours

3. Data processed by Google every day: 24 petabytes

4. Tweets per day: 50 million

5. Products ordered on Amazon per second: 73 items

How big is the market?

In March 2012 it was forecasted that big data will become a $17 billion market by 2015 (since updated to $23.8bn by 2016 )

The chances are, though, that big data will take its place in the mainstream

of IT activities.

Source : IDC & EMC

2010 2011 2012 2013 2014 2015 2016

Big Data Pure Players Revenues

• SMBs are 99,8% of enterprises in Europe

• 58,6% of revenues is generated by SMBs

• Easier to penetrate, low entry barriers, a lot of working domains

SMBs generate almost 60% of added value

Small business,

but big revenues

Source : EuroStat

SMBs can leverage Big Data

Traditional analytics means cannot cope

with such volumes, variety and velocity of

Big Data phenomenon is driven by

accelerated growth of the unstructured

Proliferation of Cloud computing and

Software-as-a-Service made it possible for

appearance of affordable data analytics

tools for SMBs.

PART #2: BIG DATA

SERVICE PROVIDERS 11

Data, Platforms, Analytics, Applications

Platforms Visualization / Analytics

Data Providers

Ads Targeting

Marketing Analytics

Fraud Detection / Costs

Findings

Managed services for business specific

statistical models start to appear with

the first results in fraud detection or

climate fine forecast.

Business analytics: companies can

build their own data analytics and

reporting on the web from ready to use

building blocks.

Marketing analytics is a mature industry

with many players addressing SMB

sector

PART #3: SMB SURVEY 14

32 SMBs

participated in our

survey about:

Big Data phenomenon understanding

Value creation and Go-To-Market

SMBs’ pain points

Trends

0 2 4 6 8 10

IT services

Medical services

Media and

E-commerce

Finance

Computer Hardware and

Software Design

Software editor

International phone calls

Mathematical modelling

Real estate

Packaging

Import/Export services

We interviewed SMBs

from different

sectors.

Most interviewed

SMBs are micro

companies and

IT related.

SMB Sectors

4% 17% <200 K€

200 K€ to 500 K€

500 K€ to 1 M€

1 M€ to 3 M€

3 M€ to 10 M€

> 10 M€ Non-IT SMBs

SMB Revenues

Most SMBs associate

Big Data value

with Web Services for

Data Analytics

Web Service for

Data Analytics

Data Processing

Tools for Developers

Not agree Agree

Statistical Models

and Data Integration

bring the most value

to SMBs

19% 19%

74% 78%

Visualization

Statistical

Models

Integration

Not agree Agree

Not all SMBs

managed to

apply Big Data

38% do not use Data

Analytics

Sales increase

preoccupation

prevails 67%

increase

reduction

SMBs struggled to

provide

quantifiable ROI

indications

54% of SMBs

mainly employ

internal resources

for Data Analytics

implementation

consultants

our employees

no data analytics

Who implemented Data Analytics?

SMBs prefer Web

sites to learn

about Big Data

News Web-

Blogs Conferences Press IT Consulting

Information Channels

80% of IT SMBs prefer

to buy through self-

service

web channels

Web-site IT Consulting Sales reps

Procurement Channels

For Non-ITs:

“Sales

representatives

bring value”

Potential pain

points for

Big Data at SMBs

Budget limitations

Risk aversion, need for ROI guarantees

Lack of employees

experienced

in Big Data

Difficulty to formulate right

questions, need for guidance

Security concerns

Extreme variety of data

Need for very custom solution

• 59% consider important budget

limitations at SMBs

• 55% indicate ROI guarantees as highly

desirable

• 59% report lack of personnel

experienced in Big Data and Data

Analytics as a potentiall blocking

• 59% report that SMBs need guidance

when dealing with Big Data

• 59% consider security aspects

important.

• Though, non-ITs are ready to rely on

data centers.

• 55% indicate data variety concerns

• 69% report the need for very custom

solution.

62% hope

to grow revenue by11%

54% think

to grow data storage

by 30%

44% estimate

data traffic grow by 30%

81% estimate that their

infrastructure is ready

SMB Trends for 2014

PART #4:

RECOMMENDATIONS

Concentrate on turn key services.

Clearly explain ROI gains when addressing SMB

market.

Provide scalable self-services to

Start adopting Big Data from marketing analytics.

Cost and risk reduction services

are largely untapped.

Recommendations

BIG DATA COULD BE THE SOLUTION

FOR YOUR BUSINESS TOMORROW…

Contacts

• For further information and

full report please contact:

• Andrey.Sadovykh@hec.edu

• Paul-Emile.Poisson@hec.edu

• Aleksei.Papin@hec.edu

This presentation is prepared in the context of the consulting

project conducted by HEC Paris Business School