Big data introduction - Big Data from a Consulting perspective - Sogeti

Big Data from a Consulting
perspective
Edzo Botjes
Business Analyst, Sogeti Consulting Services
Amersfoort 2013 05 28

3Titel | Onderwerp | Plaats | Datum |
DATA is the NEW OIL

4Big Data a Start | People Consulted | Amersfoort | 2013 05 28 |
People Consulted
Big Data experts
IT
Data Experts
Business
Information
Architects
Big Data experts
Business
Data Experts
Information
Management
Architects
Business
Big Data experts
VINT
Big Data expert
R20
Desk Research

5Big Data a Start | Content | Amersfoort | 2013 05 28 |
What were the questions from
The management team?
Content
Conclusion / Answers
Actions to take as MT

6Big Data a Start | What were the questions | Amersfoort | 2013 05 28 |
What were the questions
• Question 1
- What is Big Data?
• Question 2
- Big Data in current organization?
• Question 3
- What is the future role of the Information
Management department in the subject
Big Data?

Content

Why Big Data is a MT subject
Source: http://www.myforrester.net/big-data-webinar ; http://www.airlineleader.com/pdfs/Airline%20Leader%20Issue%2014.pdf

Why Big Data is a MT subject
Source: https://www.bmelv.de/SharedDocs/Downloads/Verbraucherschutz/Internet-Telekommunikation/SaferInternetDay2013Ksker.pdf?__blob=publicationFile page 14
“Big Data, was ist das?", Dr. Holger Kisker, VP and Research Director Forrester. February 2013

10Big Data a Start | What is data | Amersfoort | 2013 05 28 |
What is data / information ?

From data to wisdom
Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf page 6
"Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem. et al. June 2012

Role of insight
"Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem. et al. June 2012
Source: http://cci.uncc.edu/sites/cci.uncc.edu/files/media/pdf_files/MIT-SMR-IBM-Analytics-The-New-Path-to-Value-Fall-2010.pdf page 4

13Big Data a Start | Definition | Amersfoort | 2013 05 28 |
Definition of Big Data ?

The Attack of the exponentials
Source: http://www.slideshare.net/medriscoll/driscoll-strata-buildingdatastartups25may2011clean slide 4
"Building Data Start-ups: Fast, Big and Focused" by Michael E. Driscoll CTO Metamarkets, May 2011

3 V’s that define Big Data (or 4?)
VALUE
Source: http://www.slideshare.net/multiscope/data-pioneers-sander-duivestein-vint-future-of-data slide 9
“The future of data” by Sander Duivestein , June 2012

Big Data definition at Goldman Sachs et al.
BIG DATA
==
Transaction
+
Interaction
+
Observation
Source: http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/
"7 Key Drivers for the Big Data Market" by Shaun Connolly at the Goldman Sachs Cloud Conference May 2012

Big Data Definition by Edzo
BIG DATA
==
Real time data
+
Real time analysis
(graph data)
+
Real time reaction
(feedback loop)
Source: Edzo Botjes

18Big Data a Start | Examples | Amersfoort | 2013 05 28 |
Examples of the 3 V's

Examples of Size and Source
Source: http://hortonworks.com/blog/7-key-drivers-for-the-big-data-market/
Source: http://blog.vint.sogeti.com/wp-content/uploads/2012/07/VINT-Sogeti-on-Big-Data-1-of-4-Creating-Clarity.pdf

Examples of Big Data Analytics
"Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al.. June 2012

Examples Big Data

Examples of Big Data in the real life
Source:http://www.computerworld.com/s/article/9233587/Barack_Obama_39_s_Big_Data_won_the_US_election http://www.infoworld.com/d/big-data/the-real-story-of-how-big-data-analytics-helped-obama-win-212862
http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ http://commons.wikimedia.org/wiki/File:Target_logo.svg http://www.rfgen.com/blog/bid/285148/Tesco-Improves-Supply-Chain-with-Big-Data-Automated-Data-
Collection http://www.computerweekly.com/news/2240184482/Tesco-uses-big-data-to-cut-cooling-costs-by-up-to-20m http://img.dooyoo.co.uk/GB_EN/orig/0/7/7/7/3/777389.jpg

Big Data ready?

Your Big Data profile: what does that look like?
Big Data is concerned with exceptionally large, often widespread bundles of
semi structured or unstructured data. In addition, they are often incomplete
and not readily accessible.
“Exceptionally large” means the following, measured against the
extreme boundaries of current standard it and relational databases:
petabytes of data or more, millions of people or more, billions of records or
more, and a complex combination of all these.
With fewer data and greater complexity, you will encounter a serious Big
Data challenge, certainly if your tools, knowledge and expertise are not fully
up to date. Moreover, if this is the case, you are not prepared for future data
developments. Semi-structured or unstructured means that the connections
between data elements are not clear, and probabilities will have to be
determined.
Further to read:
B. Ten Big Data management challenges: what are your issues?
C. Five requirements for your Big Data project: are you ready?
"Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al. June 2012
Are you Big
Data ready?
Or to big a
leap?
“Big”

25Big Data a Start | Tips | Amersfoort | 2013 05 28 |
Most important Tip (s)

Tips
• Never, Ever, start without a Business Case and thus a
business sponsor.
• Added value of Big Data is combination of “External”
Sources. Think outside the box, outside your silo.
• Maturity is key.
- Start with identifying
- then go optimizing, scale to BI, BI++ and
- then to real time added value Big Data
feedback loops

Maturity (Big Data is young and quick)
The notion that opportunities to capitalize on Big Data are simply
lying there, ready to be seized, is echoing everywhere. In 2011, the
McKinsey Global Institute called Big Data “the next frontier for
innovation, competition, and productivity” and the Economist
Intelligence Unit spoke unequivocally of “a game-changing asset.”
These are quotes taken from titles of two directive reports on Big
Data, a topical theme that is developing vigorously, and about
which the last word has certainly not been uttered.
McKinsey states it very explicitly:
This research by no means represents the final word on big data;
instead, we see it as a beginning. We fully anticipate that this is a
story that will continue to evolve as technologies and techniques
using big data develop and data, their uses, and their economic
benefits grow (alongside associated challenges and risks).
•“Innovation”
•“Competition”
•“Productivity”
"Creating clarity with Big Data", ViNT research report 1 of 4 by Jaap Bloem et al. June 2012

28Big Data a Start | Questions | Amersfoort | 2013 05 28 |
• Question 1
- What is Big Data?
• Question 2
• Question 3
Big Data?

29Big Data a Start | Current Organization | Amersfoort | 2013 05 28 |
Big data in current organization
CRM
Internal R&D
Internal BI
Social Media
Data
Virtualization

• Question 1
- What is Big Data?
• Question 2
• Question 3
Big Data?

31Big Data a Start | Role | Amersfoort | 2013 05 28 |
Vision / Role

Information Management Role
be an advising guide
Bring together
Create
innovation environment
Bring
success to production

Facilitate Execute
Be a leader
Bring together
Create
innovation environment
Bring
success to production
Source:
http://www.alfredoartist.com/Optimized%20Images/Rodriguez-InSearchOfGold.jpg
http://resources1.news.com.au/images/2011/07/27/1226102/848013-gold-prospecting.jpg
http://www.refinedinvestments.com/wp-content/uploads/2012/10/gold-mining400x282.jpg
http://en.rockscrusher.com/wp-content/uploads/2011/04/gold-mining-plant.jpg

Not the Information Management Role
1.Employ Data scientists
2.Develop new data analyses technique’s
3.Be a business sponsor
1.Facilitate the gold finding process (POCs)

Bring data scientist in touch with business
2.Be owner of the gold mining process (projects)
3.Have and Execute a vision on data governance and data
virtualization. (reduce future costs on projects, POCs and
changes etc.)

• Question 1
- What is Big Data?
• Question 2
• Question 3
Management division in the subject
Big Data?

Content

37Big Data a Start | Actions | Amersfoort | 2013 05 28 |
Big Data Actions
Data Board
Data Governance
Data Virtualization
Create a Network

Goals of the Data board
• Role of a Steering Committee / Governance
• Once a month (2 months) meeting
• Advice to POCs, brainstorm for POCs, Assist
breaking silos, create a platform for governance
issues
(Possible KPI.. 3 POCs per year?)
• Great Variety inside Organization and outside (for
example a professor, young people, R&D and
business and more experienced internal employees)

Subjects – Data Governance
• Where is what data ?
• Who owns the data ?
• Who owns the application that stores the data ?
• Who can access the data ?
• Who is responsible of data quality (and how) ?
• What are the legal implications and boundaries ?

Subjects – Data Virtualization
• Future enormous cost reduction
• Improvement of MI
• Faster data centric solution
• Lower cost of projects
Source: http://res.sys-con.com/story/may11/1849158/data%20virt%20image_0.jpg

Subjects – Create a Network
Create connections with and between:
• Universities
• External experts / stakeholders
• (Small) specialized companies
• Internal experts / stakeholders
Source: http://learnthat.com/files/2008/06/people-network1.jpg

Content

Big Data in the Enterprise
Data Board
Data Governance
Data Virtualization
Create a network
Facilitate Execute

Big data introduction - Big Data from a Consulting perspective - Sogeti

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