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Copyright © BI Research, 2015
Colin White
President, BI Research
TDWI and Teradata Webinar
June 2015
An Analytics Roadmap for Modernizing
the Data Warehouse
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Sponsor
Copyright © BI Research, 2015 3
Speakers
Colin White
President,
BI Research
Kevin Lewis
Professional Services Partner,
Teradata
Copyright © BI Research, 2015
Webinar Overview
The field of business analytics is undergoing massive change as disruptive
technologies such as analytic appliances, non-relational systems, cloud computing
and big data analytics are introduced by vendors. What is often forgotten in the
rush by vendors to market these new technologies is that many organizations are
struggling with performance demands, governance issues and satisfying user
requirements using their existing data warehouse and analytics environment and
may not have the resources to take advantage of new industry developments. Also,
new technology may not solve existing problems and may even exacerbate them if
implemented incorrectly. This web seminar examines the issues in deploying
business analytics today and discusses an analytics roadmap that can help
organizations modernize their data warehouses and take advantage of both
existing and new analytic technologies. Topics that will be covered include:
•Modernizing the traditional data warehouse
•Using new technologies to enhance existing analytic approaches and gain
business benefit
•Finding the right balance for data governance
•An analytics roadmap for the modern data warehouse
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The Traditional EDW Information Supply Chain
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Enterprise DW
Data integration platform
Analytic tools & applications
Operational systems
RT BI services
1. Data must be integrated into the
EDW before it can be analyzed
2. Process can be time consuming
and IT can become a bottleneck
3. EDW initiatives are often IT and
not business driven
4. Results are often not want users
want
5. Leads to short-term LOB
initiatives and data silos that
reinvent the wheel
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Building an EDW is Becoming More Difficult
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Multiple user devices
Multiple output formats
Multiple deployment options
Multiple analytic tools Multiple data sources
Increasing data volumes & data rates
DW historical data
Web & social content
Sensor data
Operational data
Text & media files
Data management
Data integration
Data analysis
Decision management
EDW-Based Information Supply Chain
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Solving the Problem
Need to modernize the EDW environment to leverage technology for
business value
Continue to use analytics to improve existing operational processes
But also at the same time look for new opportunities to address
changes in the business climate and business goals
Invest in investigative computing (i.e., data discovery or analytics
R&D)
Aim to reuse data and analytic components where possible
Adjust governance policies and procedures to support the enhanced
EDW environment
Develop an analytics roadmap based on business needs and goals
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Exploiting New Technology for Business Value
New business
insights
Reduced
IT costs
New
technologies
Enhanced data
management
Enhanced
analytics
New
deployment
options
Enhanced
EDW
DRIVERS
TECHNOLOGIES
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New Insights: Examples
Today
o Situational 1-to-1 marketing:
micro-segmentation, cross-channel
analysis, analysis of influential
factors
o Customer experience and
perception management
o Fraud detection, risk management
and compliance
The Trend
o The “Internet of Things:” the
processing and analysis of sensor
and event data (healthcare,
telecommunications, financial
services, engineering, etc.)
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GE: “Industrial Internet: Pushing the Boundaries of Minds and Machines”
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New Insights Where Next? GE Example
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Quick Wins vs. Strategic Goals: Example
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The Customer Life Cycle
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Customer Analytics: Quick Win Examples
Quick Wins
o Use social media analytics to evaluate customer
perception about products and services
o Analyze web site traffic to explore customer behavior
o Understand how customer buying patterns are
affected by season, weather, store location, store
layout
o Use a temporary data lab to analyze approaches to
market a new product
But …
o How do these short-term gains satisfy the long-term
goals of increasing revenue and reducing costs?
o Do business users understand the results and how to
use them to gain business benefit?
o Are the results consistent across projects? �
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New Technologies
Enhanced Data Management
o New data sources (big data)
o Analytic relational DBMSs
o Non-relational systems (e.g., Hadoop,
HBase, MongoDB, CouchDB)
o Improved price/performance
New Deployment Options
o Integrated H/W & S/W appliances
o Cloud computing
o Mobile devices (mobile first strategy)
Enhanced Analytics
o Investigative computing
o Predictive & prescriptive analyses
o Enhanced visualization
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The Risk of New Technologies
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Modernizing the EDW
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Traditional Enterprise DW Investigative computing platform
Data refinery
Data integration platform
Operational real-time environment
RT analysis platform
New internal & external structured & multi-structured data
Real-time streaming data
Analytic tools & applications
Operational systems
RT BI services
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Key New Components
Data refinery
Investigative computing platform
Analytic tools & applications
Investigative Computing Platform
o Used for exploring data and
developing new analyses and models
o May also be used for prototyping new
analytics-driven LOB applications and
for temporary analytic solutions
o May employ an RDBMS or non-
relational system such as Hadoop
Data Refinery
o Ingests raw detailed data in batch
and/or real-time into a managed data
store
o Distills the data into useful information
and for use by other systems
o May also be used for data archiving
and for creating a “queryable” archive
o Key use of non-relational systems
today
New internal & external data,
RT streaming data
EDW data
Operational data
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The Role of Investigative Computing
Enables users to blend new types of data with existing
information to discover ways of improving business
processes and to look for new business opportunities
Allows users to experiment with different types of data
and analytics before committing to a particular solution
May employ a RDBMS or non-relational solution
running on premises or in the cloud
Represents a shift in the way organizations build analytic solutions:
o Increases flexibility and provides faster time to value because data does not
have to be modeled or integrated into an EDW before it can be analyzed
o Extends traditional business decision making with solutions that increase the
use and business value of analytics throughout the enterprise
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Investigative Computing Extends the EDW
Descriptive BI
Diagnostic BI
Predictive BI
Prescriptive BI
Business requirements
Modeling
Data preparation
Model deployment
Business & data
understanding
Data warehouse
Data refinery
Selected hypotheses
Improved understanding
Production EDW Investigative Computing
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Customer Analytics: Production EDW
Descriptive: How many of customers churned in the last month?
Descriptive: How many of these were profitable?
Diagnostic: Why did these profitable customers churn?
Predictive: How many profitable customers are likely to churn next
month?
Prescriptive: How can we reduce this profitable customer churn
rate?
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Customer Analytics: Investigative Computing
Business and data understanding: What is the difference between a
highly profitable customer and an average customer?
Data preparation and modeling: What are the characteristics of a
highly profitable customer?
Deployed predictive model: Will this new customer be profitable?
How much revenue is this new customer likely to generate?
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Data Governance Considerations
No longer practical to rigidly control and govern all forms of data and
analytics – a modernized EDW allows different levels of governance to
be applied based on security, compliance, quality and retention needs
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Data Governance
Business Agility
analytic &
data silos
traditional
EDW
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Summary: Gaining Value from New Technologies
Managers don’t have to be technical experts, but they need to:
o Understand the fundamental principles well
enough to appreciate the business
opportunities, communicate with technologists
and evaluate proposals for projects
o Be willing to invest in data and experimentation
and supply the required resources
o Keep the BI and the data science team on track
Understand how to gain competitive advantage (or parity) from new
DW technologies in the context of the corporate strategy and that
of competitors
Maintain momentum over competitors
Collaborate with, and examine projects in other organizations
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Developing an Analytic Roadmap Combining Proven Practices with Modern Concepts
Kevin Lewis, Teradata Strategy and Governance
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Incorporate new technologies and
concepts into your data strategy
Abandon everything we’ve learned over the past 50 years and
start over
• Modern data warehouses require the integration of new capabilities and new thinking
• But the key strategic principles still apply and are more important than ever
• How do you create a roadmap that incorporates proven principles with the latest concepts and technologies?
Source: “Making the Most of IT”
The Challenge – Leveraging Technology for Business Value
© 2014 Teradata
DO DON’T
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What We Hear - Symptoms of a Bad Strategy
“Databases are not optimized for the right workload”
“Data modeling and other project activities take way too long”
“Big Data changes everything; we can just put the data out there for everyone”
“Business and IT seem to have different priorities.”
“We have a lot of
data issues and needs, but we don’t know where to
start”
“The business lead says they’re not seeing the return on the investment”
What We Hear – Symptoms of a Bad Strategy
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Strategic Principles Successful Data Strategies…
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Analytic Discovery and Analytic Roadmap
This is not a technology distinction!
Analytic Discovery
(Investigative Computing)
Efforts
Prospecting –
Effort to find the gold
Mining/Production
Projects to mine the gold
Analytic Roadmap Projects
(Solutions for Production)
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Q4 Q1 Q2 Q3 Q4
2015
Business Initiatives
Information
Systems
Enablement (Governance and Readiness)
2014
Legend
Status Review
Steering Committee
Source and load Item, Orders, Social Media
Update Inventory and Demand Forecast loads
Source and load Procurement Data
Build out Treasury; Transportation Model
Projects that
deploy data
Marketing Excellence
Finance Optimization
Logistics - Visibility Business drivers
Applications
Social Media Marketing
Customer Forecasting
Demand Triggers
Trade Promotion Enhancement
Integrated Margin Management
Projects that
deliver apps
Develop BICC Deployment Plan and Standards
Establish Arch. Roles & Council (IA, DI, DA, BI Architects)
EDM phase 1 EDM phase 2 (DQ & Metadata)
BICC Development (BI Delivery; DM)
BICC Growth and Sustainment
Projects that
deploy capability
BI Platform Consolidation Establish Analytic
Sandbox
Hadoop Production Environment Set-up
Sunset BU 1- Marketing Data Mart
BO Visual Intelligence Teradata Capacity Expansion
Projects that
deploy
technology
The Roadmap in Layers
29 © 2014 Teradata
What You Can Do Tomorrow -
Program Planning
Enablement
Translate the roadmap into a program plan
Business Information Application System
Identify
business
initiatives
Identify data
needed for
business
initiatives
Identify
applications to
make use of
the data
Identify
infrastructure
needed to
leverage data
and support
applications
Identify actions needed to prepare or mature the organization
Start by taking a slice through all of these
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Strategic Analytic Roadmap
•Proactively planned
•Linked to strategy
•Role of data asserted in advance
• Includes formal IT projects
Analytic Discovery and the Roadmap
Develop BICC Deployment Plan and
Standards
Logistics - Visibility
BI Platform Consolidation Business Objects SP5
upgrade
Q4 Q1 Q2 Q3 Q4
2015
Business Initiatives
Information
Systems
Marketing Analysis
Enablement (Governance and Readiness)
Marketing Excellence
Finance Optimization
Customer Forecasting
Demand Triggers
Integrated Marketing Management
2014
Item, Sales Orders, Shipments,
Promos, Sales Forecast,
Consumption
Inventory, Demand F’cast,
Commodity Market
Sunset BU 1 Marketing Data
Mart BO Visual Intelligence
Legend Roadmap Status
Review
Steering
Committee
Meeting
Establish and Maintain PMO; Prioritization Framework
Enterprise Data Mgmt. Phase 1
(DI/DA Standards/ Practices)
Applications
Procurement, Production,
Internal Files
Finance Actuals and Plans,
Transportation
Enterprise Data Mgmt. Phase 2 (DQ & Metadata)
BICC Development (BI
Delivery; DM) BICC Growth and Sustainment
Marketing Data Mart
Consolidation
Analytic Discovery
• Limited size
• Quick turnaround
• IT support needed
but formal IT
projects typically
not needed
Results of Analytic Discovery can feed strategy, individual projects, or be directly acted upon
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For More Information…
Animation Video White paper – “The 7 Common Data Mistakes” Analytic Roadmap Overview video Forbes Blog
http://www.teradata.com/Data-and-Analytics-Strategy/
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Questions?
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Contact Information
If you have further questions or comments: Colin White, BI Research
Kevin Lewis, Teradata
