Smarter Analytics: Supporting the Enterprise with Automation

Grab some coffee and enjoy
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the top of the hour!

Smarter Analytics: Supporting the Enterprise with Automation
The Briefing Room

Twitter Tag: #briefr
The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

! Reveal the essential characteristics of enterprise software,
good and bad
! Provide a forum for detailed analysis of today’s innovative
technologies
! Give vendors a chance to explain their product to savvy
analysts
! Allow audience members to pose serious questions... and get
answers!
The Briefing Room
Mission

The Briefing Room
Topics
This Month: ANALYTICS & MACHINE LEARNING
July: INNOVATIVE TECHNOLOGY
August: BIG DATA ECOSYSTEM
2014 Editorial Calendar at
www.insideanalysis.com/webcasts/the-briefing-room

The Briefing Room

The Briefing Room
Analyst: Barry Devlin
Dr. Barry Devlin is among the foremost authorities on business
insight and one of the founders of data warehousing, having
published the first architectural paper on the topic in 1988.
With over 30 years of IT experience, he is a widely respected
analyst, consultant, lecturer and author. His 2013 book,
“Business unIntelligence—Insight and Innovation beyond
Analytics and Big Data,” is available as hardcopy and e-book.
Barry is founder and principal of 9sight Consulting. He
specializes in the human, organizational and IT implications of
deep business insight solutions that combine operational,
informational and collaborative environments. A regular
contributor to BeyeNETWORK and TDWI, Barry is based in Cape
Town, South Africa and operates worldwide.

The Briefing Room
WhereScape
! WhereScape is a data warehousing software company
! It offers WhereScape 3D, software for planning and reality-testing
data warehousing and business intelligence projects;
and WhereScape RED, an integrated development
environment used for building, deploying and managing
data warehouses and data marts.
! WhereScape RED allows developers to automate the data
warehousing life cycle

The Briefing Room
Guest: Michael Whitehead
A data warehousing industry veteran,
Michael Whitehead has spent more
than a decade designing and building
commercial data warehouses for
customers in a wide variety of
industries. Prior to founding
WhereScape, Michael had Asia Pacific
responsibilities for data warehousing
for Sequent Computer Systems, Inc.

Michael Whitehead
June 2014
Smarter Analytics

Why were sales
down this week
versus last
year?
Grocery
Store
with
Class,
Walter
Watzpatzkowski,
15
/1/09

We promoted
ice cream but the
weather was
unreasonably
cold
Grocery
Store
with
Class,
Walter
Watzpatzkowski,
15
/1/09

Our competitor
ran a better
promotion
Grocery
Store
with
Class,
Walter
Watzpatzkowski,
15
/1/09

1990s - Decision support
system
(For the time) large amounts of data, stored in
various inscrutable file formats and database
management systems.
Want actionable information?
Write a program.
One program per analytical problem….
Reporting bureaus
This
model’s
dysfuncBons
created
the
need
for
data
warehousing…

2000s - Enterprise data
warehousing
Separate the refinement of raw data – regardless of
the source – from the delivery of subsets of that
data, to various decision-making constituencies.
Build a solid, scalable information delivery
infrastructure for the corporation.
Support variability, and change, at both ends.
Apply appropriate governance, risk management,
compliance mechanisms.
[And stabilize the supply side of the market, in the
process…]
A
design
paFern
for
stable,
OperaBonalized
informaBon
refining
and
delivery

The economic
conditions led to a
change in
demographics of
the people walking
past my store
Grocery
Store
with
Class,
Walter
Watzpatzkowski,
15
/1/09

2014 - big data technologies
Large amounts of data, stored in
various inscrutable file formats and
database management systems.
Write a program.
One program per analytical problem….
Oh, and batch-oriented.
And integrate-it-yourself.
Instead
of
JCL,
Pig.
Instead
of
CICS
and
Comshare,
Cloudera.
In
what
way
is
this
model
a
leap
forward?

People built
Data warehouses
that don’t support
analytics
Grocery
Store
with
Class,
Walter
Watzpatzkowski,
15
/1/09

2014 – “self service”
technologies
Large amounts of data, stored in
various inscrutable file formats AND
data warehouses.
Create a dataset.
One dataset per analytical problem….
The
newer
tech
is
great.
Is
the
way
it
is
used
a
leap
forward?

Automation is key
for better support
of analytics
Smith
Cannery:
Extension
and
Experiment
StaBon
CommunicaBons
Photograph
CollecBon
(p120)

STEPS
1. Identify attributes
2. Identify business key
3. Index business key and add a unique constraint
4. Create surrogate key with auto sequence generation
5. Index surrogate key
6. Insert zero surrogate key row with values set for each attribute
7. Add a modified timestamp column
8. Write the SQL code to Insert new business keys or Update existing business key
rows. Maintain the modified timestamp
9. Create any other indexes required for querying
10. Decide best practice for index maintenance during load. Keep in situ or drop and
recreate after load.
11. Document procedure
Etc Etc

Really?
1. Identify attributes
2. Identify business key
3. Index business key and add a unique constraint
4. Create surrogate key with auto sequence generation
5. Index surrogate key
6. Insert zero surrogate key row with values set for each attribute
7. Add a modified timestamp column
8. Write the SQL code to Insert new business keys or Update existing business key
rows. Maintain the modified timestamp
9. Create any other indexes required for querying
10. Decide best practice for index maintenance during load. Keep in situ or drop and
recreate after load.
11. Document procedure
Etc Etc

What can be automated?
• Profiling
• Model conversion
• Object creation
• Code generation
• Indexing
• Impact analysis
• Documentation

What it will look like?
The new data warehouse

The new data warehouse
Five Key Changes
Pooling – new types of data, staged differently
than we’ve staged pampered data, in the past.
A multi-engine “logical” data warehouse:
NoSQL à Not Only SQL
Support for discovery, prototyping and
evaluation of analytics
Support for continuing data integration,
through to the “end use” tier
Automation of the data warehousing platform’s
core functionality
Back
to
best-‐of-‐breed,
customer-‐specific
IntegraBon
models

Conclusion
Let’s not stuff it up (again)
• Data people – challenge
ourselves to do more, faster
• Analysts – don’t give up on the
data people

The Briefing Room
Perceptions & Questions
Analyst:
Barry Devlin

un
^
Business Intelligence:
Smarter Analytics: Supporting
the Enterprise with Automation
Dr Barry Devlin
Founder & Principal
9sight Consulting
Bloor Briefing Room
10 June 2014
Copyright © 2014 9sight Consulting, All Rights Reserved

Analytics (and big data ) emerged for business with
social media and web logs
§ Understanding and tracking sentiment
– What do you think? How do you react?
– Basic analytics and BI activity on a new
data source
§ Real-time insight into and influence
on website activities
– Why did you abandon your cart?
– What would you most likely buy
on getting a cross-sell?
– Deep, real-time analytics and BI
with operational integration
30 Copyright © 2014, 9sight Consulting

The Internet of Things adds urgency to a new
automation of analytics and BI
§ Extends existing processes
– Micro-management of supply chains and
extension all the way to the consumer
– Sourcing and delivery
§ Creates completely new business models
– Often depending on analytics
– Motor insurance à encouragement & prevention
– Hospital care à health monitoring

The biz-tech ecosystem reflects the complexity of
today’s business.
Speed of decision and appropriate action
Business
Information
Technology
Customer interaction
and technical savvy
Competition Mobile devices
Information abundance
and variety
Market flexibility
and uncertainty
Externally-sourced
information

The architecture for the biz-tech ecosystem consists of
information pillars.
§ Single architecture for all types of
data/information
– Mix/match technology as needed
– Relational, NoSQL, Hadoop, etc.
§ Integration of sources and stores
– Instantiation gathers measures,
events, messages and transactions
– Assimilation integrates stored info.
– Reification virtualizes access
§ Data flows as fast as needed and
reconciled when necessary
– No unnecessary storage or
transformations
– (Contrast layered data architecture)
Reification
Process-mediated
(data)
Assimilation
Context-setting (information)
Transactional
(data)
Transactions
Human-sourced
(information)
Machine-generated
(data)
Instantiation
Measures Events Messages

Information pillars can be mapped to today’s BI and
analytics tools and environments.
§ Process-mediated data
– Traditional computing
– Via data entry, cleansing processes
– Relational databases
§ Machine-generated data
– Output of machines and sensors
– The Internet of Things
– NoSQL, Streaming, (RDBMS)
§ Human-sourced information
– Subjectively interpreted record of
personal experiences
– From Tweets to Videos
– Hadoop, Enterprise Content
Management
EDW BI
Process-mediated
(data)
Assimilation
Oper.
Analytics
Pred.
Analytics
Context-setting (information)
OLTP
Transactional
(data)
Transactions
Human-sourced
(information)
Machine-generated
(data)
Instantiation
Measures Events Messages

From BI to Business unIntelligence
§ Information, knowledge and meaning
– Understanding real world context
§ Process, predefined and emergent
– Automating the creation and use
of information
§ Beyond bounded rationality
– How decisions are really made
§ http://bit.ly/BunI-Technics : 25% discount
with code “BIInsights25”

Dr Barry Devlin
Founder & Principal
9sight Consulting
Thank you!
Additional resources
§ All articles and white papers available
at: http://bit.ly/9sight_papers
§ Blogs at: http://bit.ly/BD_Blog
§ Follow me on Twitter: @BarryDevlin
Copyright © 2014 9sight Consulting, All Rights Reserved
36

Questions (1)
1. The Enterprise Data Warehousing architecture of the 2000s (I would
say 1990s) was driven by the business need for consistency /
reconciliation of data from many sources. It’s perhaps suboptimal for
timeliness (real-time data) and maintenance (multiple layers of ETL
function). How can the sort of automation you’re proposing help in
these two areas?
2. You compare 1980s and 2014 approaches asking how this model is a
“leap forward.” One difference is users’ (data scientists) skills with
technology. Wouldn’t automation disempower such users?
3. What would a warehouse that “supports Analytics” look like?
4. You say “Automation is the key for better support of analytics,” but
how does automation support the agility and flexibility needed for
analytics?
5. A big idea in analytics is “model on read.” Automation typically
requires/provides “model on write.” How do you address these very
opposite needs?

Questions (2)
6. Your pooling tier reminds me of the “Data Lake” – of which I’m not a
big fan! Why would I want to bring “pampered data” ( I assume
traditional data) through this pool? Seems like an additional /
unnecessary step?
7. What engines (other than SQL) do you envisage? Which do / will you
support?
8. Can you describe what the linkage between the different engines
means? If integration how is it done?
9. What data integration support do you envisage in the “end use” tier?
10. Overall, how do you see your existing products evolving to implement
the various aspects of this architecture? Does the relational database
remain the core component, or do you envisage a more central role for
Hadoop, as in Cloudera’s Enterprise Data Hub?

This Month: ANALYTICS & MACHINE LEARNING
July: INNOVATIVE TECHNOLOGY
August: BIG DATA ECOSYSTEM
www.insideanalysis.com/webcasts/the-briefing-room
The Briefing Room
Upcoming Topics
2014 Editorial Calendar at
www.insideanalysis.com

THANK YOU
for your
ATTENTION!
The Briefing Room

Smarter Analytics: Supporting the Enterprise with Automation

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