Intelligence for Corporate Survival Slides

By
Paul Ormonde-James
Twitter: @pormondejames
Linkedin http://au.linkedin.com/in/ormondejames
When every shot
counts
Intelligence
for Corporate
Survival
Markus Evans
Consumer Intelligence & Analytics
Conference
Park Hyatt, Melbourne, Australia
22-23 August 2013

Copyright 2005 Paul Ormonde-James, pormonde@cybertreking.com
Everyshotcounts
PaulOrmonde-james
We live in a
digital world

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PaulOrmonde-james Ubiquitous Computing
More data from
more machines

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PaulOrmonde-james Virtualisation
Storage
getting
cheaper by
the minute

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PaulOrmonde-james Cloud Computing
Data now physically
all over the world and
growing at an
increasing rate

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PaulOrmonde-james Social Media….
BIG
Data
More people
communicating with more
people, social data
increasing

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PaulOrmonde-james

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Needle in a hay stack
Finding the right customer data is like
Looking for a needle in a hay stack

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PaulOrmonde-james How do we makes sense of all this???

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The Challenge?
How do I find the
right customers,
at low cost to
make millions?
1 OFFER = 1 ACCEPT

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How do companies understand WHO to market to?? CONTACT
How do companies approach YOU? CHANNELS
How do companies understand YOUR needs SEGMENTS
I’m an
individual
I’m an
individual
I’m an
individual
I’m an
individualI’m an
individual
I’m not

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The Ying & Yang
Data Quality
Analytics
TODAY I will address

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My Experience
Data
Quality

Insight Services Group
PostConnect
Data & Insights
Australia’s largest fact based
address & lifestyle profile
database
• Postal address File
• AMAS data
• National Change of Address data
• Movers data
• Australian Lifestyle survey
• eProducts & insights
• Parcels insights >>

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PaulOrmonde-james
Bringing insights from data across the
whole of Australia Post
Retail
insights
Geospatial
insights
Address
insights
Parcel
insights
Movers
insights
Campaign
insights

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My Experience
Analytics

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The World Bank
Head of Global Business Intelligence, The World Bank
Washington DC
USA
• Manage modeling & analysis teams for global impacts on
banking sectors
• Managed global teams collecting data from 182 countries
• Managed predictive analysis teams on portfolio risks &
cash flows
• Built & managed Global teams for end to end data
control & analysis

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The Analytic 4 P’s
PEOPLE
CUSTOMER
PERFORMANCE

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PaulOrmonde-james Ana Lytics
Ana

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PaulOrmonde-james Business Intelligence – Simple form

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PaulOrmonde-james
Build the
technology
like pieces
of a jigsaw
puzzle
22

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My Experience
Data
Quality

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Data Quality imperitives
24
• The data is consistent -
• The databases are well designed - A well-designed database will
perform satisfactorily for its intended applications, it is extendible
and it exploits the integrity capabilities of its DBMS.
• The data is not redundant -
• In actual practice, no organization has ever totally eliminated
redundant data. In most data warehouse implementations, the data
warehouse data is partially redundant with operational data. For
certain performance reasons, and in some distributed
environments, an organization may correctly choose to maintain
data in more than one place and also maintain the data in more
than one form.
• The redundant data to be minimized is the data that has been
duplicated for none of the reasons stated above but because:
| Copyright – Paul

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• The creator of the redundant data was unaware of the
existence of available data.
• The redundant data was created because the availability or
performance characteristics of the primary data were unacceptable to
the new system. This may be a legitimate reason or it may also be
that the performance problem could have been successfully
addressed with a new index or a minor tuning effort and that
availability could have been improved by better operating procedures.
• The owner of the primary data would not allow the new
developer to view or update the data.
• The lack of control mechanisms for data update indicated the need
for a new version of the data.
• The lack of security controls dictated the need for a
redundant subset of the primary data.
– In these cases, redundant data is only the symptom and not the
cause of the problem. Only managerial vision, direction and a robust
data architecture would lead to an environment with less redundant
data.

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• The data follows business rules -
• As an example, a loan balance may never be a negative number.
This rule comes from the business side and IT is required to
establish the edits to be sure the rule is not violated.
• The data corresponds to established domains -
• These domains are specified by the owners or users of the data.
The domain would be the set of allowable values or a specified
range of values. In a human resource system, the domain of sex is
limited to "male" and "female." "Biyearly" may be accurate but still
not an allowable value.
• The data is timely -
• Timeliness is subjective and can only be determined by the users
of the data. The users will specify that monthly, weekly, daily or
real-time data is required. Real-time data is often a requirement of
production systems with online transaction processing (OLTP). If
monthly is all that is required and monthly is delivered, the data is
timely.

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• The data is well understood -
• It does no good to have accurate and timely data if the
users don.t know what it means.
• Naming standards are a necessary (but not sufficient)
condition for well-understood data.
• Data can be documented in the meta data repository, but
the creation and validation of the definitions is a time-
consuming and tedious process.
• This is, however, time and effort well spent.
• Without clear definitions and understanding, the
organization will exhaust countless hours trying to determine
the meaning of their reports or draw incorrect conclusions
from the data displayed on the screens.

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The data is integrated -
• If the data is integrated, meaningful business information can
be readily generated from a combination integration generally
requires the use of a common DBMS.
• There is an expectation (often unfulfilled) that all applications
using the DBMS will be able to easily access any data
residing on the DBMS.
• An integrated database would be accessible from a number
of applications.
• Many different programs in multiple systems could access
and, in a controlled manner, update the database. Database
integration requires the knowledge of the characteristics of
the data, what the data means, and where the data resides.
• This information would be kept in the meta data repository.

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• The data satisfies the needs of the business -
• The data has value to the enterprise. High quality data is
useless if it's not the data needed to run the business.
Marketing needs data on customers and demographic
data, Accounts payable needs data on vendors and
product information.
• The user is satisfied with the quality of the data
and the information derived from that data -
• While this is a subjective measure, it is, arguably, the
most important indicator of all. If the data is of high
quality, but the user is still dissatisfied, you or your boss
will be out of a job.
• The data is complete -
• All the line items for an invoice have been captured so
that the bill states the full amount that is owed. All the
dependents are listed for an employee so that invoices
from medical providers can be properly administered.

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• There are no duplicate records -
• A mailing list would carry a subscriber, potential buyer or
charity benefactor only once. You will only receive one
letter that gives you the good news that "You may
already be a winner!“
• Data anomalies -
• From the perspective of IT, this may be the worst type of
data contamination. A data anomaly occurs when a data
field defined for one purpose is used for another.
• For example, a currently unused, but defined field is
used for some purpose totally unrelated to its original
intent.
• A clever programmer may put a negative value in this
field (which is always supposed to be positive) as a
switch.

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My Experience
Analytics

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• “Analytics is the combustion engine of
business, and it will be necessary for
organizations that want to grow, innovate
and optimize efficiency,”
• “Given its far-reaching impact, it is one
of the few software markets that thrive
even in adversity.”
• Gartner analyst Rita Sallam

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Decline of the Business Intelligence Stack
33
• Mega vendors IBM, SAP, Oracle, Microsoft and SAS still own
the dominant share of the business intelligence market. SAP
leads in business intelligence platform revenue
• Mega vendors own two-thirds share today versus one-third in
2007. But the area of analytics is still an open playing field.
Data discovery vendors such as QlikTeck, Tableau and Tibco
Spotfire grew more than the others
• “A stack-centric mentality and single vendor
standardization policies won't cut it,” she said. “Understand
that your organization needs a portfolio of analytic
capabilities.” Gartner
• Companies now differentiating between the idea of
selecting a single business intelligence vendor and
establishing business intelligence standards.

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• Standardization is becoming less about using the same
toolset and more about employing specific tools,
metrics, and processes for certain capabilities and use
cases.
• This shows up in a decline in the number of users
identifying vendors as their business intelligence
standard.
• All the mega vendors experienced a drop, some by as
much as 19 percent in only three years.

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Demographics Drive Data Discovery
35
• Another area that is changing the business intelligence market is
demographics.
• Millennials (ages 20 to 30) now comprise 20 percent of the
workforce, but their ranks will swell to 40 percent by 2020.
• “The graduating high school class of 2011 spent all of their school
years with pervasive access to the Internet – they don’t know a
world without information at their fingertips,” said Sallam.
• “You tell them to go to the library to use the card catalog, and
they look at you if you told them to go use an abacus to calculate
the square root of 1,058.”
• These younger employees are driving the consumerization of IT,
which includes how business intelligence is delivered. They want
business intelligence to be as intuitive, social and collaborative as
the tools in their personal life.

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Demographics Drive Data Discovery
36
• Thus traditional reporting and ad hoc query are flat or declining,
whereas data visualization in dashboards and interactive visualization are
experiencing growth. “BI is becoming more visual as data discovery needs
intensify,” Sallam said.
• The data discovery market will rise from $591 million currently to $1
billion by 2013, Sallam said.
• Data discovery tools typically include a mix of in-memory analytics, data
mashup capabilities, dashboards, self-service delivery, light footprint and
speed of deployment. These tools have become so popular that
established vendors are copying them.
• Demographics are also contributing to the growth of mobile business
intelligence. Gartner predicts that 33 percent of analytics will be
consumed on handhelds by 2013.
• By the end of this year, 55 percent of organizations using business
intelligence either have or plan to deploy mobile BI. As a result, mobile
BI projects will outnumber traditional workstation projects by four to one

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The Rise of Predictive Analytics
37
• Gartner surveys show most users still focus on measurement
of the past, with only 13 percent of users making extensive
use of predictive analytics. Less than 3 percent use
prescriptive capabilities such as decision/mathematical
modeling, simulation and optimization.
• Gartner advises organizations to develop a plan to support
new data volume, variety and velocity requirements. By
being able to correlate, analyze and present insights from
structured and unstructured information, organizations will
be able to personalize customer experiences and exploit
new opportunities.
• “Those that can do advanced analytics on top of Big Data
will grow 20 percent more than their peers,” said Sallam.
“The explosion of data volume, as well as its variety and
velocity, will enable new, high-value advanced analytic use
cases that drive growth and productivity.”

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How is it done

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The how is important
• Regardless of methodology, most processes for creating
predictive models incorporate the following steps:
– 1. Project Definition: Define the business objectives and desired outcomes
for the project and
– translate them into predictive analytic objectives and tasks.
– 2. Exploration: Analyze source data to determine the most appropriate data
and model
– building approach, and scope the effort.
– 3. Data Preparation: Select, extract, and transform data upon which to
create models.
– 4. Model Building: Create, test, and validate models, and evaluate whether
they will meet
– project metrics and goals.
– 5. Deployment: Apply model results to business decisions or processes.
This ranges from
– sharing insights with business users to embedding models into applications
to automate
– decisions and business processes.
– 6. Model Management: Manage models to improve performance (i.e.,
accuracy), control access, promote reuse, standardize toolsets, and
minimize redundant activities.

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PaulOrmonde-james What does research show us

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• Barriers to Usage. A host of barriers can prevent
organizations from venturing into the domain of
predictive analytics or impede their growth. This
“analytics bottleneck” arises from:
– 1. Complexity.
• Developing sophisticated models has traditionally been a slow,
iterative, and labor intensive process.
– 2. Data.
• Most corporate data is full of errors and inconsistencies but most
predictive models require clean, scrubbed, expertly formatted data
to work.
– 3. Processing Expense.
• Complex analytical queries and scoring processes can clog
networks and bog down database performance, especially when
performed on the desktop.

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• 4. Expertise.
– Qualified business analysts who can create sophisticated models are
hard to find, expensive to pay, and difficult to retain.
• 5. Interoperability.
– The process of creating and deploying predictive models
traditionally involves accessing or moving data and models among
multiple machines, operating platforms, and applications, which
requires interoperable software.
• 6. Pricing.
– The price of most predictive analytic software and the hardware to
run it on is beyond the reach of most midsize organizations or
departments in large organizations.
Fortunately, these barriers are beginning to fall, thanks to advances
in software, computing, and database technology.

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We are known for our
unique offering – enabling
smarter connections.

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PaulOrmonde-james
Conclusions.....
46

By
Paul Ormonde-James
Twitter @pormondejames
Linked in
Email:
pormonde@cybertreking.com
When every shot counts
What will you do
to differentiate?

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