1.
DATA CULTURESERIES – 8th April 2015
Birmingham

2.
?Who is using Data to drive the future of their business?

3.
?Who is using Predictive Analytics / Machine Learning yet to
change their business model?

4.
UK Business Lead for BI & Advanced
Analytics
4
Jon Woodward : Connect & Follow
4
@JLWoodward
www.linkedin.com/in/jonathanwoodward
#DataCulture
PowerBI
APS
AzureML
Hadoop
DataFactory
DocumentDB
Search
EventHub
Stream Analytics
Revolution R

5.
Industry transformation driving opportunities

6.
2015…We have reached a Tipping Point
Of organizations will
consider cloud
deployment
50%
Of new licence spend will
be for Data Discovery &
Analytics
50%
Of BI & Analytics spend
will be driven by the
Business
50%
Of Users will be touched
by BI and Analytics
50%

7.
Core to Vision
Start Justin
Digital
Work & Life
Experiences

8.
Data…Driving the Experience

9.
UK Economy - data dividend

10.
The Microsoft
data platform
MobileReports
Natural
language queryDashboardsApplications
StreamingRelational
Internal &
externalNon-relational NoSQL
Orchestration
Machine
learningModeling
Information
management
Complex event
processing

11.
Data Culture Series
Data Culture Exec
Session
Data Culture
Summit
4 events – final event 14th May, London
CXO Level – Invite only
10 events; 800-1000 customers
Power User, Analyst, Architect, Developer, DBA, Data Scientist
Final 3 events this fiscal (Birmingham, Reading, London)
Data Culture
Data Science
Deep-Dive
2 events; 100 customers
Power User, Analyst
April 20/21
May 26/27
NEW

12.
Data Culture Summit

13.
Date Location
8 April BIRMINGHAM Data Culture series
12 May READING Data Culture series
19 May LONDON Data Culture series
Summer Break
Date Location
September TBC 2 Day Data Culture Event
Nov London Future Decoded
Jan TBC 2 Day Data Culture Event

14.
#DataCulture

15.
Time
10.00 – 10.30 Intro – Jon Woodward
10:30 – 11:30 Keynote
Allan Mitchell–“When all you have is a hammer everything
looks like a nail..those days are gone”
Andrew Fryer – “DataEthics - Just because you can doesn't
mean you should”
11:30 – 12:30 ImmersionTracks - Overview
12:30 – 13:15 Lunch & Expo
13:15 – 15:00 ImmersionHands on
15:00 - 15:15 Break & Expo
15:15 - 16:30 ImmersionHands on
16:30 – 17:00 Panel and l Close
Microsoft, HP, HortonWorks, KPMG, DataRelish

16.
ALLAN MITCHELLWhenallyouhaveisahammereverything
lookslikea nail..thosedaysare gone

17.
When all you have is a hammer,
everything looks like a nail
Abraham Kaplan,The Conduct of Inquiry:Methodologyfor Behavioral Science,1964, page 28
Give a small boy a hammer and
he will find that everything he
encounters needs pounding
Arthur Bloch, Baruch’s observation – The Complete Murphy’sLaw: A definitiveCollection(1991)

18.
The Dawn of Time (well nearly)
• Relational Databases
• E.F.Codd
• 1970
• Relational Model of Data
• 12 rules (actually 13)

19.
RDBMS – The Advantages
• There are many, tried and tested, used almost everywhere
• Scale well (vertically)
• Provides a basis for high level language
• Relational Algebra and Calculus
• Easy to link relations
• Structural Independence
• “tabular” view
• Isolation of physical/logical

20.
Challenges
• Schema Flexibility
• EAVs
• Column Reuse
• Its free (or nearly)
• Paradigm Shift
• Not everything is relational/ or should be
• The one column , one row XML database <shudder>
• Horizontal Scaling

21.
CAP Theorem
The CAP Theorem states that, in a distributed system (a collection of
interconnected nodes that share data.), you can only have two out of
the following three guarantees across a write/read pair: Consistency,
Availability, and Partition Tolerance - one of them must be sacrificed

22.
CAP Theorem
• Consistency - A read is guaranteed to return the most recent write for
a given client.
• Availability - A non-failing node will return a reasonable response
within a reasonable amount of time (no error or timeout).
• Partition Tolerance - The system will continue to function when
network partitions occur.

23.
Distributed Systems and the CAP Theorem
AvailabilityConsistency
Partition Tolerant
Eric Brewer’s
CAP Theorem
and even better
CAP Twelve Years Later
Myth:EricBrewerOn Why BanksAreBASE
NotACID -Availability IsRevenue
Lara Rubbelke & Karen Lopez

24.
Alternatives
• Document Databases (DocDB, Mongo, Raven)
• Key/Value (Redis, Hbase)
• Graph Databases (Neo4j, Trinity)
• Analytical Search engines (Elasticsearch, SolR)
• Search Engines (Azure Search)
• Hadoop (Hortonworks, Cloudera…)

25.
The Hadoop Ecosystem
Taken from Hortonworks site

26.
Major Advantages
• Schema on read
• Scale horizontally
• Commodity Hardware
• Store data AS IS
• Data Stored in a variety of formats
• There is usually a SerDe to take care of things

27.
What about “The Cloud”
• Game Changer
• Elastic Scale
• Storage where data is born
• Plethora of choices
• Cheap
• PAYG

28.
Internet of Things
• Coined by Kevin Ashton in 1991
• Network of physical objects or things
• Sensors
• Smart Car/Home
• Animals
• Heart monitors
• Healthcare

29.
Flash in the Pan?
• Cisco thinks about 50 billion devices will be connected by 2020, after
coming out with an earlier analysis in January that claimed 8.7 billion
connected devices in 2012.
• A separate analysis from Morgan Stanley feels that number can actually be
as high as 75 billion, and also claims that there are 200 unique consumer
devices or equipment that could be connected to the Internet that have
not yet done so.
• There's no reason to doubt that devices connected to the Internet Of
Things will soon be flooding the mass market. We'll see compact,
connected sensors and actuators make their way onto everyday consumer
electronics, household appliances, and on general infrastructure.

30.
The Data Explosion
• There are 1.2 zettabytes of
data today with an estimated
35 zettabytes by 2020
• There are 5 billion mobile
subscribers today with an
estimated 50 billion by 2020
• People see more than 34
billion bits of information per
day – an equivalent of 2 books
a day online

31.
Final Thoughts……..
• Relational Databases are here to stay
• Other types of data storage exist
• Take the opportunity today to understand your options
• Talk to people about them, read more about them
• Make an informed decision
• Don’t be the child that pounds everything they see

32.
ANDREW FRYER
DataEthics - Just becauseyou can
doesn'tmean you should

33.
Data Ethics
Just because we can doesn’t mean we should

34.
Ethics?

35.
The problem of Ethics and data
• The laws are global data is global
• Law and specifically UK case law lag
technological change

36.
Ethics in Research
ethical behaviour helps protect individuals, communities and
environments, and offers the potential to increase the sum of good in
the world. As social scientists 'trying to make the world a better place'
we should avoid (or at least minimise) doing long-term, systematic
harm to those individuals, communities and environments...' (Israel
and Hay, Research Ethics for Social Scientists, 2006)

37.
The good

38.
The bad
£

39.
The illegal
Retailer repurposing loyalty card data to use as an online dating
service

40.
Risks

41.
Ethical Tests
• Using data for your benefit not your customer
• So what if your use of analytics got out in the wild

42.
Managing Ethics
• Understand the risks
• Educate your users
• Pose ethical dilemmas
• Develop a code of conduct

43.
Tracks

44.
DATA CULTURESERIES – 8th April 2015
Birmingham

45.
Panel
Andrew Fryer Microsoft
Simon Gregory HortonWorks
Andrew Morgan KPMG
Allan Mitchell Data Relish

46.
Free Download of Dave
Coplin – Rise of the
Humans
52
Ask a Question

47.
#DataCulture
Andrew F
Q : Given the cloud first strategy, are MSFT
driving all new features only to the cloud.
Where does on-premise fit..

48.
#DataCulture
Andrew M
Q : Does Data Science really require Data
Scientists

49.
#DataCulture
Simon G
Q : if Hadoop is the answer to Big Data,
where is it heading…what is the future
vision

50.
#DataCulture
Allan M
Q : Data has legs, how do we manage
governance in this new world, where data
is everywhere

51.
Trial : Hadoop (HDInsight, HDP)
57
Get Hands on…
Trial : SQL Server 2014
Trial : PowerBI
Trial : Machine Learning

52.
PASS BA*, London , November
PASS Summit*- US, October 27-30th
PASS BA* – Santa Clara , April 20-22nd
SQL Saturday – Edinburgh , June 12-13th
SQL Saturday - Exeter, April 25th
58
Community Events
58
* See Jen Stirrup for Discount

53.
Come Back for more…
Date Location
16 September READING
10 November LONDON
27 November READING
3 December LONDON
27 January LONDON
24 February LEEDS
24 March EDINBURGH
8 April BIRMINGHAM
12 May READING
19 May LONDON

54.
Data Science 2 Day Workshops
Date Location
20/21 April READING
26/27 May LONDON
The workshop will include information and hands-on lab sessions covering
Predictive Analytics scenarios with Big and Near real-time data and Machine
Learning.
Day 1 & 2
-Intro to Big Data, Predictive Analytics & Data Science
-Azure Machine Learning (ML) Fundamentals
-Data Exploration, Visualization, Transformation, Cleaning Using Azure ML
-Using R in Azure ML
-Building a Classification Model Azure ML
-Building a Regression Model Azure ML
-Metrics and Methods of Classifier Evaluation in Azure ML
-Deploying a Model as a Web Service
-Hands-on Lab Based on Participants’ Background and Interest
Email : jonathan.Woodward@microsoft.com

55.
UK Business Lead for BI & Advanced
Analytics
61
Jon Woodward : Connect & Follow
61
@JLWoodward
www.linkedin.com/in/jonathanwoodward
#DataCulture
PowerBI
APS
AzureML
Hadoop
DataFactory
DocumentDB
Search
EventHub
Stream Analytics

56.
THANK YOU
?