When talking about how the future of Big Data will look like, this conversation often turns straight to Artificial Intelligence and Deep Learning. However, today data science is all too often a process where new insights and models get developed as a one-time effort or deployed to production on an ad-hoc basis i.e. they commonly require regular babysitting for monitoring and updating. According to Gartner, the number of useless Data Lakes will be of 90% in 2018. Furthermore, only 15% of Big Data Products are mature enough to be deployed into Production - Who is responsible to make Big Data successful and Business relevant within an enterprise?

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The CxO Responsibilities in Big Data and AI
Santiago Cabrera-Naranjo
Principal Manager, Think Big Analytics
#CTOhamburg17

2. Last year humanity generated more data than in whole
human history.

3. By 2020 more than 50 billion things, ranging from cranes to
coffee machines, will be connected to the internet.

4. This means 44 zettabytes of data will be loaded annually.

5. To prepare for this onslaught, some IT leaders are urging the
creation of Data Lakes.

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• A central repository
with trusted,
consistent data
• Cost Efficiencies by
offloading analytical
systems and archiving
cold data
• Derive value quickly
with easier discovery
and prototyping
• A laboratory for
experimenting with
new technologies
and data
Goals for a Data Lake
© 2017 Teradata

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The Ecosystem Landscape of Enterprises has become complex.
(Cloud, On-Premise, Open-Source, Legacy, Geography, Regulation)

9. 1. Data Driven Businesses need Business driven Data
Projects.
2. Lack of DevOps Mentality along the whole
analytical lifecycle.
3. Lack of skills and capability of organization.
Where are Companies failing?

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Gartner Predictions
49%
believe key inhibitor
to adoption is figuring
out how to get value
from hadoop
“Most companies lack the
skills to deploy such
technology.”
“Companies don’t have a good idea as
to what they’re doing and why.”
The Big Data Dilemma
© 2017 Teradata

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Gartner Predictions
49%
believe key inhibitor
to adoption is figuring
out how to get value
from hadoop
“Most companies lack the
skills to deploy such
technology.”
NUMBER OF FAILED
BIG DATA PROJECTS
60%
“Projects do not move beyond piloting
and experimentation.”
“Companies don’t have a good idea as
to what they’re doing and why.”
“Not establishing data
governance and
management.”
The Big Data Dilemma
© 2017 Teradata

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Gartner Predictions
49%
believe key inhibitor
to adoption is figuring
out how to get value
from hadoop
“Most companies lack the
skills to deploy such
technology.”
NUMBER OF FAILED
BIG DATA PROJECTS
60%
“Projects do not move beyond piloting
and experimentation.”
NUMBER OF USELESS
DATA LAKES BY 2018
90%
“Lakes are overwhelmed with
information assets captured for
uncertain use cases.”
“Companies don’t have a good idea as
to what they’re doing and why.”
“Not establishing data
governance and
management.”
The Big Data Dilemma
© 2017 Teradata

13. This conversation often turns straight to Artificial Intelligence
and Deep Learning
What about Data Science?

14. Today data science is all too often a process where new
insights and models get developed as a one-time effort or
deployed to production on an ad hoc basis, and require
regular babysitting for monitoring and updating
Analytic Operations Mentality

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Is there a way to monitor and evaluate performance in
development as well as to compare models?
Can your system deploy models to production with
ongoing validation of performance?
Can you stage models into production system for testing
without disturbing system operation?
© 2017 Teradata

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Does your system easily handle hot hand-offs so new
models can seamlessly replace a model in production?
Do you have an automated fall back?
Are your models functioning in a precisely specified and
documented environment?
© 2017 Teradata

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Ecosystems today are heterogenious.
Open Source is huge and complex.
© 2017 Teradata

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Big Data or Pokémon?
and much more ...

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TIME-TO-MARKET
YEARS
complexity of existing
processes
Gartner Predictions
There is currently lack of confidence in terms of delivering
sustainable Data Products

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TIME-TO-MARKET
YEARS
complexity of existing
processes
BIG DATA PRODUCTS
IN PRODUCTION
15%
lack of iteration and alignment with
business requirements
Gartner Predictions
There is currently lack of confidence in terms of delivering
sustainable Data Products

22. 22 © 2017 Teradata
TIME-TO-MARKET
YEARS
complexity of existing
processes
BIG DATA PRODUCTS
IN PRODUCTION
15%
lack of iteration and alignment with
business requirements
STATIC DATA
PRODUCTS
BECOME OUTDATED
6 months
market adjustments and
environmental changes
Gartner Predictions
There is currently lack of confidence in terms of delivering
sustainable Data Products

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“Data is the new oil” – The Economist
Current Data-Centric Approach

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“Data is the new oxygen” – TechTrends
Management Approach

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Data Platform Advanced AnalyticsAnalytic Ecosystem
CxO Responsibility 1: Data Driven Businesses leverage
on Business Driven Projects
Customer Journey
Customer Acquisition Customer Experience
Cross-Sell/
Up-Sell
Customer
Churn
Customer Journey Capability Assessment & Roadmap ServiceRoadmap
Definition
.
.
.
.
.
.Omni Channel
Customer Interaction Management: CIM
Real Time Personalisation: RTIM, Celebrus…
Strategic
Priorities
Experience Root Cause
Analysis
Experience Issue
Identification
Convert Browsers to Buyers
Marketing Spend Optimization
Sales Process Optimization
Online Search Optimization
Omni-Channel Abandoned
Cart
Next Best Offer Proactive Churn Prediction
Churn Root Cause Analysis
Customer Lifetime Value
Active Customer Experience
Mgement
Customer
Journey
Orchestration
Capabilities
Insight Driven
Business
Outcomes
Data Foundation
Data Platforms Analytic Ecosystem Advanced Analytics

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Curated
Pipelines
Data Lab
Production
Data System
Data Science
Workbench
Data
Sources
Model Management
Environment
Business
Applications
&
Operational
Processes
CxO Responsibility 2: Define a dynamic but high automated
Infrastructure to generate business value
Mainframe
Others
Model
(Dev, Test, Validate)
Wrangle
Promote Model
to Production
Catalog Models
Run Production
Pipeline &
Update Models
Promote to
Scoring
(Packaging)
• Feature Enrich
• Scoring
• Logging
Your Goal, your first success story with Big Data

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CxO Responsibility 3:
Embrace new Models of
Organization
Companies will require cross-functional
teams with the right software and
discipline to enable data scientists,
engineers, product managers, and
domain experts to all work together to
create a continuous cycle that drives
value to the business.
© 2017 Teradata

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Culture eats Strategy for Breakfast, Lunch and Dinner…
Gartner PredictionsSource Chart: Spotify Engineering

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Gartner Predictions
Culture eats Strategy for Breakfast, Lunch and Dinner…
Source Chart: Spotify Engineering

30. 30 © 2017 Teradata
Gartner Predictions
Culture eats Strategy for Breakfast, Lunch and Dinner…
Source Chart: Spotify Engineering

31. 31 © 2017 Teradata
Gartner Predictions
Culture eats Strategy for Breakfast, Lunch and Dinner…
Source Chart: Spotify Engineering

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Gartner Predictions
Culture eats Strategy for Breakfast, Lunch and Dinner…
Source Chart: Spotify Engineering

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“Innovation is not just about technology per se. It is
more about new models of social organisation...”
© 2017 Teradata

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Demo: Kubernetes and Containers
Dmitry Rodionov
Senior Data Engineer, Think Big Analytics
#CTOhamburg17

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