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How to Create and Manage a
Successful Data Organization
Mario Faria
fariamario@hotmail.com
+1 - (425) 628-3517
@mariofaria

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Who am I ?
• MIT recognition as one of the 1st Chief Data Officers and Lead Data
Scientists in the world (just Google “Mario Faria Chief Data Officer”)
• 20+ years working with Information Technology, Management
Consulting, Financial Services, Retail, CPG and Private Equity
• Proven expertise in Data Management, Data Science, Analytics and
Supply Chain Management
• Speaker at several conferences on the subject in USA, Europe and
Latin America
• Contributor to magazines and publications
• Big Data Advisor at the Bill and Melinda Gates Foundation
• Member of the MIT Data Science Initiative

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Objectives of this webinar
• Provide insights on how you should successfully create a
Data organization
• With that in place, you will be able to work effectively with
Big Data projects

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My mission :
To help the data community
evolve with sustainability

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By being a consultant,
I want to say 3 things ...

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The 3 things:
• Situation : where the market is at this point
• Complication : current issues with data
management and Big Data
• Solution : what I recommend you to do and how
to do it

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Situation

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How we got
here in terms of
Big Data

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Evolution of Business Intelligence

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The 4 driving factors that are
changing the technology industry as
we know it
• Social
• Mobile
• Cloud
• Information

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This brave new world we are living in
• How does success look like in a
world where consumers are now
marketers ?
• Where a trillion data points are
available, alive and transforming
decisions (preference /
purchase) and relationships as
we speak ?
• How to understand, connect and
consistently engage with
consumers and customers
creating loyalty and
recommendations ?

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“The balance of power in the 21st
century is influenced by the ability
to leverage information assets” –
Gwen Thomas, CEO of The Data
Governance Institute

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Data is about
• People
• Technology
• Processes
• Modeling
• Analytics
• Communication
• Decisions
• Actions
A data-driven culture is a disruptive factor for entire industries

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SQL
MAPREDUCE
HADOOP
CLOUDSCALE
MPI
BSP
PREGEL
DREMEL
PERCOLATOR
What is Big Data?

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From Business
Intelligence to
Big Data

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What is Analytics ?
“The extensive use of data, statistical
and quantitative analysis, explanatory
and predictive models, and fact-based
management to drive decisions and
actions” – Thomas Davenport

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Analytic Maturity Curve

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The Four Types of Analytics

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Differences between Big Data
and Traditional BI projects

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Analytics is not just about :
• Large volumes
• Greater scope of information
• Real time access to information
• New kind of data and analytics
• Data influx from new technologies
• Non-traditional forms of media
• Variety of sources
It all of the above, plus a transformation in processes and
culture, and it is a disruptive factor for entire industries

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Analytics is about customer centricity
• Supply Chain forecasting
• Behavioral analysis
• Operations improvement
• Marketing targeting / decisions
• Real-time pricing / promotions
• Customer experience analysis
• Customer insights
• Customer lifecycle management
• Fraud prevention and analysis
• Network monitoring

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Predictive Analytics
• Prediction is powered by the world's most potent,
booming unnatural resource: data
• Predictive analytics is the science that unleashes the
power of data
Dr.Eric Siegel

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The 3 ingredients to make
Advanced Analytics work
• Choosing the right data and managing multiple data
sources
• Having the capability to build advanced models that turn
the data into insights
• Management must undertake a transformational-change
program so that the insights translate into effective action

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Big Data
=
Human Behaviour

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Data Monitoring Centers

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Complication

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Land of Confusion

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Who owns the Data inside an
organization ?

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Some problems, at this point, in
most organizations
• Data is fragmented and scattered
• Silos of information hanging around
• Like the truth, data has many versions
• The Data Lifecycle is a complex process
• Data projects being managed by IT
• A formal process to manage data is a
requirement in order to do Analytics

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The problem : data is an
abstract concept

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The complexity of the Data Life Cycle

35. The
Big Data
Technology
Players

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The evolution path to Big Data

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Confusion between Big Data and
Hadoop
• Hadoop is being wrongly treated as a synonym of
Big Data
• Hadoop is one of the technologies to be used at
Big Data projects
• Hadoop is a great technology for storing
unstructured data in an expensive and scalable
manner, in a high granularity
• What Linux did to Operating Systems, Hadoop is
bringing to Information Management

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The Hadoop Ecosystem : growing
everyday

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The Big Data Fragmented Tech Vendors : data life cycle
process view

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Understanding
Hadoop/MapReduce
Usage
Output/
Input
(records)
Job Input Size
GB PB
Best case scenario

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An analogy of using MapReduce
Traditional usage
MapReduce usage

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The
Big Data
Architecture
Transformation
and Analysis
You may trade off
consistency and integrity
for speed and flexibility

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Big Data Analytics Projects

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And, unfortunately, technology alone will
not change the previous results
To succeed in Data & Analytics, an organization will be
required to change some of its current internal processes

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The catch : just a few companies (users
and consulting) understood the nits and
grits about Data Analytics : it requires you
to moving from a simple data management
vision (tactical) to an information
management vision (strategic)

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Solution

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Find a real object that people
can relate to

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The Data Value Chain

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The Deming Model :
Production Viewed as a System

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What is Data Quality ?
• Quality is a customer perception
• A few dimensions: freshness, coverage,
completeness, accuracy
• It is a never ending job

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Usage of wrong data can destroy
credibility

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A Few Quality Programs
TDQM
TIQM

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More and more, Data Leaders are being hired
to think strategically think about all the steps
from getting raw data and making it useful to
business users

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Foundations of the Data team
responsibilities
• Data Strategy
• Data Analytics
• Data Insights
• Data Architecture
• Data Governance
• Data Quality
• Data Acquisitions
• Data Operations
• Data Policies
• Data Security
• Data Protection

55. Chief
Data
Officer
/
Head
of
Analy6cs
/
Data
Scien6sts

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Chief Data Officer (CDO) /
Chief Analytics Officer (CAO) /
Lead Data Scientist

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Chief Data Officer (CDO) /
Chief Analytics Officer (CAO) /
Lead Data Scientist
• A new profession that is becoming very common in
corporations
• He/she is a corporate officer who is the business
leader for enterprise-wide data processing and data
mining.
• The CDO typically reports to the CEO or the COO
and is a member of the executive management team
of a company or business unit.
• CDOs leverage their organization's data assets to
support the business strategy. He/she manages
enterprise-wide data administration and is the
champion of enterprise information management
• CIOs are very concerned with this new role, because
of the threat to their current power

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The role of a Chief Data Officer or
Lead Data Scientist
A data scientist is the one
who looks for insights
The insight is operationalized
in BI/DW products, by data architects
The insight is shared
with the enterprise
The CDO or Lead Data Scientist is the
executive responsible and accountable for
the data life cycle inside the organization,
managing the people involved in the data
activities, such as acquisitions, analytics,
processes, governance, quality, technology
and budget

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Why should not IT be managing
this transition ?
Because data projects are business
projects, not IT projects and the CDO/Data
teams are the bridge between IT and
Business Units

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The Chief
Data
Officer
Role

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The 3 Architectures a Company needs
to succeed
Business
Architecture
Technology
Architecture
Data
Architecture

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Data/Information Architecture

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Why do you need a Chief Data Officer ?

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Why do you need a Chief Data Officer ?
• Data is about business, it's not about
IT
• Data is an economic asset, so you
need a senior person to handle the
data initiatives.
• As an economic asset, data needs:
control, show value and monetization
• There is now way you can do
Advanced Analytics unless you have
some data management practices in
place.

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“Organizations are about to be
swamped with massive data
tsunamis. The Chief Data Officer
is responsible for engineering,
architecting, and delivering
organizational data success” –
Peter Aiken, PhD

67. Data
Science
The
process
of
taking
raw
data,
producing
informa6on
from
data,
and
using
this
informa6on
to
guide
ac6ons
that
will
bring
financial
benefits
to
business

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A Chief Data Officer
is the executive
responsible to
manage these areas

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• A good CDO can implement a data organization
with success
• A great CDO has the ability to turn raw data into
large revenue streams for the business
• Components such as technology and
methodologies are important, but they are just
enablers
• The CDO focus is delivering enterprise value to the
business (not writing code or SQL scripts)
From good to great CDO

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The evolving CDO role will challenge structure, scope and power
relationships between executive committee members.
The scarcity of information leader talent will require executive
leaders to develop it as much as hire it.

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At the end, on Big Data, a CDO and the
team should
• Support the data initiatives, using the assets from
different sources, with quality as a requirement
• Drive business insights, so the users can act
promptly
• Execute his/her tasks fast, in real-time if possible

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The main drivers for
Data/Big Data projects
• Make more money
• Reduce current costs
• Improve efficiency

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What it takes to make Big Data projects
drive results
• Data – understand what they have and
how to be creative when it comes to
using internal and external data
• Models – focus on developing models
that predict and optimize
• People – transform their organizations
with tools and effective training so that
managers can take advantage of Big
Data's insights.

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Data, Information, Analytics, Business
Intelligence and Performance Management

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To start an Analytics Team inside, there are 4
main things to consider
People
Technology
Process to
implement the
Practice
Methodology for
the Delivery

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From good to great, an analytics team
must have:
• Passion for analytics and data
• Never stop learning
• Always be there for tough analytics
questions
• Ask questions until everything makes sense
and you are satisfied with the answers and
analyses
• Learn how to develop prototypes quickly
• Be an advocate for building a strong
foundation in corporate analytics
• Be a "bridge builder" between IT and
business users

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Looking ahead in the near future …

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Which companies will thrive in 2015?
• The ones which will understand how to adapt faster to
this new scenario
• The ones which will have successful Analytics
implementations
• The ones with great human capital, which understand
how to leverage their resources and with proven
methodologies to embrace this change

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Is your company going to lead,
influence or follow when using data
and analytics to drive results ?

82. What does it
take to succeed in
this data journey ?

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Major points on how to structure
a data governance program
• Upper management buying and support
• Do not reinvent the wheel : use and abuse of best
practices that already exist
• Communicate always and be transparent
• Quick wins
And …

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Hire the best and most eager
resources you can find

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“Successful people shoot for the stars,
put their hearts on the line in every
battle, and ultimately discover that the
lessons learned from the pursuit of
excellence mean much more than the
immediate trophies and glory”
Josh Waitzkin, The Art of Learning

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Thank you
Mario Faria
Data Strategy Advisor
http://www.linkedin.com/in/mariofaria/
Founder of the Digital Mad Men
www.slideshare.com/fariamario
Twitter : @mariofaria
fariamario@hotmail.com
+1 (425) 628-3517

88. Q&A