Post on 22-Jan-2018
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries.
Build a Fabric for Data Driven Culture October 1st, 2017
Modernization of Data and Analytics
1
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries.
Introduction
Rajiv Sinha
Managing Director, TD Ameritrade
Data, Analytics, Technology Platform, Architecture (DATA) and
Marketing Systems
With the firm for 3.5 year
Ford Motor Company - 14 years
Consulting – 5 years
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries.
Case for Change
Democratization of Advanced Analytics – driven by abundance of data and easy to
use tools “Citizen Analysts”
Digital transformation – enabled by data and context-aware insights
Hype and practical use of of Artificial Intelligence - “Age of the Bots”
Customer engagement is happening through different channels – Voice, Social,
Chat etc.
To realize value – analytics is increasingly being built and operationalized at the
edge of the enterprise
Analytics driven transformation is challenging all aspects traditional
approaches to build, deploy and sustain analytics.
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Keys to success - Holistic approach towards modernization
Systemic Issues
Strategic Opportunities
Structure + Op. Model
Technology + Data
Governance + Security
Skills
Think strategically and holistically – right balance between differentiating
opportunities and BAU.
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries. 5 10/11/2017
Structure + Operating Model - Analytics Lifecycle
Validation
Vetting out the solution from business
value perspective – in market testing
Includes visualization of the data
output from
Output: Business case/requirements
for production , do additional
experimentation or shut down
experimentation
Experimentation
Define & build
Analytics/mathematical models
Types of analytics
technologies/packages
Highly Iterative
Data Organization
Understand the data elements
Determine the business rules
Structure the data for
analytics
Determine Data needs
Identify Source of data
Ingest data into the sandbox
Discovery
Business Hypothesis or Ideas
Business Case and Requirements for Production
Legacy IT engagement criterion
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries. 6 10/11/2017
Structure + Operating Model – Federated Model
• IT vs CoE ?
• Centralized vs Business Unit Centric ?
Business Units
Center of
Expertise
IT
• Not a new model
• Tighter collaboration
• Engage technology
partners upfront
• CoE should drive cross
business unit governance
• Manage at the core –
provide flexibility at the
edges
• Evolve tee operating model
- radical changes will kick in
the “corporate immune
system”
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Technology and Data
• Data pipelines for rapid accessibility
• Just enough standardization – build to last vs. speed to market
• Fit for purpose platform and technology capabilities – “One size fits none”
• Automations and frameworks to drive self-services without creating chaos
• Discovery and Experimentation environment with latest data
• Data warehouse becomes one component of the overall data ecosystem
• Continuous experimentation – Technology, Operations Processes , etc.
• Changing role – solution delivery to strategic partner – play advisory role
• Create transparency around investments, prioritization – drive stakeholder
value
• Build and Accelerate a cloud strategy – analytics innovation is coming to the
cloud first, in addition alleviates infrastructure complexity
• Build an analytics collaboration environment
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries. 8 10/11/2017
Data and Analytics Platform – Reference View
Marketing
SQL Server, PosGres, Oracle, Excel, SharePoint Lists, Text, Social, Unstructured
ETL ELT
Retail
Institution
al
Trader
Order Mgmt +
Back Office
Ops +
Risk
ClickStream
Log files
(security etc.)
Chat, IVR,
Notes, emails..
Internal Data Sources
Hosted Solutions
(CRM, Social)
Data Aggregators &
Services(CRM,
Postal Address)
External Data Sources
Services Streaming
Data Management Platform
Reference &
Meta Data
Data Quality
Master Data
Production Data Platform
Netezza
EDW +
User
Database
DMY
(Hadoop)
Analytics Server
(Statistical Models, Data Mining etc.)
Discovery Data Platform
Netezza
Sandbox
Section of
DMY
(Hadoop)
Analytics Server +
Visualization
(Statistical Models, Data
Mining etc.)
Production Data
Platforms
Exploratory & Discovery Platforms
Appropriate Access
Controls and Masking
Production
Processes
Data Movement
Decisioning Platorm
Event Detection +
Correlation
Real time Decision
Engine + Rules Engine
BI & Visualization Platorm
Business Object +
Tableau
TDA
Products Associate
Desktop (Web)
Ad-hoc External
Usage (University,
SAS, Cloud)
IC Desktop
(Through App +
Alerts)
Client Facing App
Consumption
Data to External
Vendors + hosted
Solutions
Other Internal
Applications SII/PII Data will be
managed
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Security and Governance
Data Accessibility will be key
Restriction based access control might not be enough
Build additional capability around tokenization, discovery, monitoring
and audit
Two speed governance – Regulatory, Compliance, Financial Reporting
vs. Product and Marketing
Govern not just the data, but also derived data outputs from analytics
.
If data is not readily accessible, analysts communities find a way of getting to
the data needed - bypassing security procedures and policies
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries.
Skills
Right skills are critical to success however, is the single most impediment
• Work with HR to focus on recruiting – both experienced and college graduates
• Establish vendor partnerships
• Training programs
• Extended collaboration among teams – “Fusion Teams”
• Invest in Technologies that are simpler to use
• Rotation policy between the various operating groups
• Experiment! Experiment! Experiment!
This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries.
How do you eat the elephant ? - Analytics strategy
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• Business opportunity driven and NOT technology driven
Directly support the business goals
Should be outcome oriented to reliably deliver more of whatever the business needs
• Allow to assess the particular needs of the business
Rationalize the investments needed to drive kinds of disruptions that may be attractive
and when
Articulates the resource needs to keep the lights on and BAU work
• Actionable – Should work within the realm of what is possible and practical
• Technology is moving incredibly fast, and disruptive opportunities are highly dynamic.
Strategy needs to be flexible.
Living document, should evolve as conditions change
• Key driver to prioritize investments typically driven by value, but in the context of a
changing business and technology landscape.
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Components of a analytics strategy Should solve for both the current systemic issues as well as emerging business needs
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Business Outcomes
Governance + Security
Strategy
Technolgy + Architecture
Do we understand what strategic
objectives are we solving for? Can we
articulate the value of deployed analytics
solutions to the firm?
Can we secure our data, while
providing access to the users at
the speed and latency they need.
Risk Averse vs. Risk (smart)
Acceptance?
Is the architecture flexible and fit for
purpose? Does investments in
technology provides the best cost for
value and at the same time reduce
operational complexity?
Roadmap: Sequence of tactics that solves for a specific use case or a set of related
use cases and builds the strategy over a period of time