Chief Analytics Officer Fall USA 2017 - Rajiv Sinha - TD Ameritrade

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


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


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.


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


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”


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


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


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


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!


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.


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

This document contains confidential information for use by TD Ameritrade Holding Corporation and its subsidiaries. 13

Data & Analytics

Chief Analytics Officer Fall USA 2017 - Rajiv Sinha - TD Ameritrade