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DEVELOPING A MODERN ENTERPRISE

DATA STRATEGY Edd Wilder-James, Scott Kurth

March 2017

TODAY’S SCHEDULE Introduction Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

INTRODUCTION

To view SVDS speakers and scheduling, or to receive a copy of our slides, go to:

www.svds.com/StrataCA2017

Silicon Valley Data Science is a boutique consulting firm focused on transforming your business through data science and engineering.

WE DO DATA RIGHT • We work in cross-functional teams made up of data

scientists, engineers, and solutions architects.

• We combine enterprise know-how with custom methods derived from Silicon Valley best practices.

• We use an Agile Software Development approach to make rapid progress against difficult problems that require flexibility.

• We focus on delivering business value as early as possible, then iterating toward the larger goal.

OUR SERVICES

DATA STRATEGY

AGILE ENGINEERING

AGILE DATA SCIENCE

ARCHITECTURE

Supports investigative work and builds a solid layer for production.

Conducts experiments and responds to the changing environment.

Makes foundational infrastructure readily accessible.

THE EXPERIMENTAL ENTERPRISE

THE DATA VALUE CHAIN DRAW VALUE FROM YOUR STRATEGIC DATA ASSETS

DISCOVER INGEST PROCESS PERSIST INTEGRATE ANALYZE EXPOSE

9 @SVDataScience

WHAT’S ON YOUR MIND? What is preventing your organization from realizing its vision?

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

WHY HAVE A DATA STRATEGY?

11 @SVDataScience

DATA STRATEGY is not for the faint of heart*

* Creating an Enterprise Data Strategy by Wayne Eckerson http://www.enterprisemanagement360.com/white_paper/creating-an-enterprise-data-strategy/

The alternative is to treat data as a cost of business, to be minimized.

Data must serve the strategic imperatives of a business: the key strategic aspirations that define the future vision for an organization.

IS THERE AN ALTERNATIVE?

A modern data strategy is a roadmap to enable data- driven decision-making and applications that helps an enterprise achieve its strategic imperatives.

An effective data strategy helps an enterprise make technology choices, grounded in business priorities, to get the most value from their data.

IS THERE AN ALTERNATIVE?

CONNECTING TECHNOLOGY AND BUSINESS VALUE If you find that:

• you can’t articulate how the cost of your data systems relates to the benefits to your business, or

• you can’t articulate how your technology philosophy enables your business aspirations

then your organization would almost certainly benefit from data strategy.

Poll: • Is the technology

leadership in your organization prioritizes investments to meet the ambitions of the business?

• Can your organization clearly articulate the business impact of the data and technology investments it makes?

ARTICULATING THE BUSINESS IMPACT OF DATA & TECHNOLOGY

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

CONNECTING DATA WITH THE BUSINESS

CLEAN VALIDATE CONTROL PROTECT

CONVENTIONAL DATA STRATEGY “WHAT YOU DO TO DATA”

CONVENTIONAL WISDOM: 10 THINGS A DATA STRATEGY SHOULD INCLUDE* 1. What data should be collected?

2. How long should data be kept?

3. Where should the data be stored?

4. How will data privacy and security be managed?

5. From where can data be accessed?

6. What data can be displayed?

7. What level of detail should be retained?

8. Who is responsible for the data (governance)?

9. How is data integrated?

10. How will data be distributed (virtualization?)

* 10 Key Elements of your Data Strategy by Mike Schiff http://www.tdwi.org//Articles/2012/01/17/10-Elements-Data-Strategy.aspx?Page=1

MODERN DATA STRATEGY “WHAT YOU DO WITH DATA”

TARGET VIP CUSTOMERS ATTRACT NEW CUSTOMERS

AUTOMATE

A NEW ORTHODOXY? FOUR PRINCIPLES OF A SUCCESSFUL DATA STRATEGY*

1. How does data generate value?

2. What are our critical data assets?

3. What is our data ecosystem?

4. How do we govern data?

* The 4 Principles of a Successful Data Strategy by Paul Barth http://www.cioupdate.com/insights/article.php/3936706/The-4-Principles-of-a- Successful-Data-Strategy.htm

EDW Governance Security

NOT ALL DATA IS EQUAL

Conventional data strategy

EDW Governance Security

NOT ALL DATA IS EQUAL

Modern data strategy

WHAT IS A DATA STRATEGY?

Existing data & technology

Possible data & technology

Business strategic

ambitions

Constraints Priorities

Roadmap of investments

Tools to update and assess roadmap

Plan to update capabilities

Modern Role of Data: Represents the new role data and analytics play in the enterprise.

Outcomes, not Operations: A strategic notion of maturity should begin with value creation before addressing underlying operational processes.

Transforming Pragmatically: Changes are grounded in the holistic view of the future state of your enterprise.

A NEW NOTION OF MATURITY

An organization’s ability to derive value from its data defines its maturity.

NEW STAGES, NEW DIMENSIONS

ASSETS

CULTURE

DECISIONS

OUTCOMES

Illustrative

Not just the technology! • People • Processes • Systems

DIMENSIONS OF DATA MATURITY

CURIOUS WHERE YOU FALL?

ASSETS

CULTURE

DECISIONS

OUTCOMES

IllustrativeMaturity Mini-Assessment • 20Q survey (5-10 min)

• Identifies your stage and provides general recommendations

• Creates baseline for future performance and growth

dmm.svds.com

• Infrastructure is holding back growth

• Infrastructure is holding back development

• Analog to digital transformation

• Changing business models

• Unifying fragmented offerings

YOU NEED A DATA STRATEGY WHEN…

BEGIN WITH THE BUSINESS • First understand what drives your business

• Then make the leap from strategy to tactics

Technologists: This can’t be done without the business leaders in the room

Business Leaders: This can’t be done without the technologists in the room

Understand the strategic imperatives of your organization:

• Annual report

• Investor updates

• Talk to leadership

STRATEGIC IMPERATIVES

Break down the strategic imperatives to make them tangible, achievable, and measurable. These become your business objectives.

Business objectives provide the guide for many other analyses in building your data strategy.

BUSINESS OBJECTIVES

REAL ESTATE MARKETPLACE: ZILLOW Business Objectives

• Build and maintain best algorithms for pricing • Use Hedonic pricing method to incorporate multiple attributes

and ‘nearest neighbors’ to create accurate Zestimate® • Deploy sophisticated and adaptive models, at scale (over 110

million homes) and at timely interval (3 times / week) • Use scalable infrastructure (cloud) for rapid analysis

• Build industry’s best real estate data sets • Increase completeness of data by include public data sets such

as construction listings, foreclosure listings, market context • Capture unique data with customer reviews and feedback from

real-estate firms • Manage scale of 110 million properties

and growing

Strategic Imperatives • Provide products and

services to help consumers with every stage of home ownership – buying, selling, renting, borrowing, and remodeling

• Generate more subscription and ad revenue

• Drive more unique users to marketplace

• Become leading real estate and home-related information marketplace on mobile and web

NOTE: Zillow is not an SVDS client.

HEALTH PROVIDER: KAISER PERMANENTE

Business Objectives • Increase data sharing with extended care teams

through secure electronic health record access

• Provide quicker, better diagnoses through evidence- based medicine techniques

• Provide mobile access to scheduling, pharmacy interactions, and other related services

• Improve member satisfaction by analyzing web and mobile user interactions, behavior, and feedback data

• Share access to knowledge, innovation, and population data with the public and other health care leaders

Strategic Imperatives • Provide seamless,

personalized care through an integrated team of care providers

• Enable members to manage their own care through easy-to-use channels

• Transform care and improve outcomes through investments in research and innovation

NOTE: Kaiser Permanente is not an SVDS client.

REAL ESTATE MARKETPLACE: ZILLOW

STRATEGIC IMPERATIVES

• Provide products and services to help consumers with every stage of home ownership – buying, selling, renting, borrowing, and remodeling

• Generate more subscription and ad revenue

• Drive more unique users to marketplace

• Become leading real estate and home-related information marketplace on mobile and web

NOTE: Zillow is not an SVDS client.

REAL ESTATE MARKETPLACE: ZILLOW

BUSINESS OBJECTIVES

1. Build and maintain best algorithms for pricing

• Use Hedonic pricing method to incorporate multiple attributes and ‘nearest neighbors’ to create accurate Zestimate®

• Deploy sophisticated and adaptive models, at scale (over 110 million homes) and at timely interval (3 times / week)

• Use scalable infrastructure (cloud) for rapid analysis

NOTE: Zillow is not an SVDS client.

REAL ESTATE MARKETPLACE: ZILLOW

BUSINESS OBJECTIVES

2. Build industry’s best real estate data sets

• Increase completeness of data by include public data sets such as construction listings, foreclosure listings, market context

• Capture unique data with customer reviews and feedback from real-estate firms

• Manage scale of 110 million properties and growing

NOTE: Zillow is not an SVDS client.

HEALTH PROVIDER: KAISER PERMANENTE

STRATEGIC IMPERATIVES

• Provide seamless, personalized care through an integrated team of care providers

• Enable members to manage their own care through easy-to-use channels

• Transform care and improve outcomes through investments in research and innovation

NOTE: Kaiser Permanente is not an SVDS client.

HEALTH PROVIDER: KAISER PERMANENTE

BUSINESS OBJECTIVES

• Increase data sharing with extended care teams through secure electronic health record access

• Provide quicker, better diagnoses through evidence- based medicine techniques

• Provide mobile access to scheduling, pharmacy interactions, and other related services

NOTE: Kaiser Permanente is not an SVDS client.

HEALTH PROVIDER: KAISER PERMANENTE

BUSINESS OBJECTIVES

• Improve member satisfaction by analyzing web and mobile user interactions, behavior, and feedback data

• Share access to knowledge, innovation, and population data with the public and other health care leaders

NOTE: Kaiser Permanente is not an SVDS client.

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

UNDERSTANDING DATA GAPS

None of these questions make sense unless you ask:

For what?

Commonly-asked questions:

• Do I have gaps in my data?

• How good is my data?

• Is my data clean enough?

NO ONE'S DATA IS PERFECT

FOR WHAT? • Do I have gaps in my data?

• How good is my data?

• Is my data clean enough?

• Do I have gaps in my data?

…for understanding customer purchase behavior

• How good is my data?

…for predicting quarterly sales

• Is my data clean enough?

…for automating production

• What are you trying to achieve as a business [with data]? These are your business objectives.

• How do you plan to achieve it [with data]? These are your use cases.

UNDERSTAND YOUR BUSINESS GOALS

UNDERSTAND YOUR AUDIENCE Who is going to use this analysis and how?

• CDO? Heads of Business Units? Data Science Directors? DBAs?

• Project assessment? Operational dashboard? Continuous improvement plan?

Understanding stakeholders and expectations will dictate the level of technical analysis required.

UNDERSTAND YOUR AUDIENCE What are the dimensions of requirements that matter to your audience?

• For a technical application, it might be depth, breadth, latency, frequency.

• For an executive perspective, it might be higher-order requirements like ease of integration or coverage.

What are the questions your audience needs answered? Select the dimensions that provide visibility into those questions.

• Start with an effective catalog of your data.

• Organize the data to be effective. Think about how data is produced AND how it gets used in your organization.

• By data source?

• By entity?

• By organization?

• By data owner?

UNDERSTAND YOUR DATA

LINK IT ALL TOGETHER

Business Objectives

Use Cases

Requirements

Data

VISUALIZE YOUR GAPS

SO… WHAT IS A ”GAP”? Two schools of thought:

• Purists: If a requirement isn’t met, it’s a gap.

• Pragmatists: If you can still get the job done, it isn’t a gap.

Both views can be valuable ways of looking at your analysis.

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

THE DATA PLATFORM ARCHITECTURE

51 @SVDataScience

WHY BIG DATA? 1. New Capabilities

2. Economic Scalability

Edmunds.com wanted to reduce time- to-market by speeding creation of attribute data for new car models.

We developed a new capability to automatically extract vehicle features from specification guides and categorize the features into appropriate vehicle classes.

DATA PLATFORMS FOR NEW CAPABILITIES

Existing revenue streams: • Ads • Price quotes (leads)

Shopping is the focus: • Need real-time

inventory • Accurately described

VINs

DATA PLATFORMS FOR ECONOMIC SCALABILITY at NetApp

NOTE: NetApp is not an SVDS client. http://blogs.wsj.com/cio/2012/06/12/netapp-cio-uses-big-data-to-assess-product-performance

UP VS. OUT — SAAS EDITION

$, €

, ¥ , £

Users

Revenue

Cost to serve

Scale-out cost

Profit

Loss

UP VS. OUT — ENTERPRISE EDITION $,

€ , ¥

, £

Data Resource Usage

Scale-up cost

Scale-out cost

UC1

UC2

UC3

UC4

UC5

57 @SVDataScience

BIG DATA … it’s really about agility

• Linear scale-out cost

• Opex vs. capex

• Ease of purchase

BUYING AGILITY

59 @SVDataScience

Scale-out systems move us from managing scarcity to promoting utility.

• Architectural factors • Schema on read • Rapid deployment • Mirror production setup • Executes faster

• Programmer factors • Fun to program • Concision • Easier to test • Faster to write

DEVELOPMENT AGILITY

WHAT IS DOCKER? • Container technology: bundles every part of an

application • Provides isolation for each application without the

overhead of running a virtual machine • Ships only the parts that are needed—leaves out the

operating system

WHY SHOULD BUSINESS CARE? • Better use of server resource than virtual machines • A fast and reliable way of deploying applications

• It’s the ideal packaging mechanism for scale-out distributed systems

• Easy for developers to work in an environment identical to production • Sharing containers leads to innovation

WHAT IS APACHE KAFKA?

• Scale-out fault-tolerant messaging system • Comes from LinkedIn • Supported by Confluent

USE CASES

• Stream processing • Log aggregation • Creating decoupled evented architectures

WHY SHOULD BUSINESS CARE?

• Scalability in a critical area of distributed applications • Online reliability, compared to alternatives • Will be a core building block of distributed data

architecture

WHAT IS APACHE SPARK?

• In-memory distributed computing platform • Comes from Berkeley AMPlab • In production with early adopters, now integral to

every commercial Hadoop distribution • Doesn’t need Hadoop, but runs easily on top

USE CASES

• Managing a major retailer’s inventory across a diverse network of entities in near real time

• Managing and processing event streams for online gaming

• Supporting data science initiatives across massive data sets at a media analytics company

WHY SHOULD BUSINESS CARE?

• Enables use cases Hadoop didn’t provide, all in one platform • streaming, interactive analytics, machine learning,

graphs

• Fast • Iteration time down, more productive

• Use existing cluster investment • Sits on HDFS, can run under YARN

(or use Amazon S3, or Cassandra)

WHY SHOULD BUSINESS CARE?

• SparkSQL • Use SQL skills and tools, e.g. Tableau • Dataframes integrate external data sources into one

context: RDBMS, Hive, JSON…

• Developer-friendly • Concise and fluid to program • Language integration: Scala, R, Python, Java

WHAT ARE NOTEBOOKS? • Interactive documents that contain a program and

its output • Long history: Mathematica

• Particularly successful with data science • Projects to watch

• Jupyter — https://jupyter.org/ • Apache Zeppelin —

https://zeppelin.incubator.apache.org/

WHY SHOULD BUSINESS CARE? • Easy collaboration and sharing of data science

• Think “Docker for analysis”

• Easy access to data and compute resource • A building block for more self-service analytical

capabilities

Commercial version of Notebooks + Spark is the Databricks Cloud

@SVDataScience

ENTERPRISE DATA ARCHITECTURE

Towards a production

DATA PLATFORM

Data Management Security, Operations, Data Quality, Meta Data Management and Data Lineage

Analytics

Lo w

L at

en cy

A cc

es s

Data Ingest

Data Repository

Persistence

Offline Processing

Real-Time Processing

Batch Processing

Data Services

External Systems

Data Acquisition

Internal External

CHOICES: TOOLS

Graph Document Key-Value Columnar Social networks

Ontologies Knowledge, Property

Logging Document archive

Web content

Shopping Cart Session Data

Sensors Network devices

Internet of Things

Technical Use Cases

CHOICES: DATABASES

Graph Document Key-Value Columnar Social networks

Ontologies Knowledge, Property

Logging Document archive

Web content

Shopping Cart Session Data

Sensors Network devices

Internet of Things

CHOICES: DATABASES SPECIALIZED

Graph Document Key-Value Columnar Social networks

Ontologies Knowledge, Property

Logging Document archive

Web content

Shopping Cart Session Data

Sensors Network devices

Internet of Things

CHOICES: DATABASES GENERAL PURPOSE

CHOICES: VELOCITY SVDS R&D TRAINS Batch:

• Using FFT transformed frequency data, identify the train based around fundamental frequencies of train whistle.

• Construct the decision tree for train classifier based on minimum and maximum fundamental frequencies

Real-Time:

• Apply FFT to audio signal

• Extract min and max fundamental frequencies

• Classify the train into local or express

• Send data to the Event Detector to alert the APP

• Store results in HBase

80 @SVDataScience

[Amazon] do services because they've come to understand that it's the Right Thing. There are without question pros and cons to the SOA approach, and some of the cons are pretty long. But overall it's the right thing because SOA-driven design enables Platforms. … You wouldn’t really think that an online bookstore needs to be an extensible, programmable platform. Would you?

+Steve Yegge

CHOICES: SERVICES

https://plus.google.com/112678702228711889851/posts/eVeouesvaV X

CHOICES: DATA RESILIENCY

Hard Failure: If the data source is broken, so is the app.

Stovepipe: One-to-one relationship from data source to product.

Multi-sourced: Redundancy of overlapping data sources makes your products more resilient.

Graceful Degradation: If a data source breaks, there is a backup and your app continues to function.

Production data services abstract the probabilistic integration of overlapping data sources. We call this model a Data Mesh.

CHOICES: EXTERNAL SYSTEMS Applications, visualization, business intelligence

üIncremental revenue

üTime to market

üEconomically viable implementation

üCost avoidance

üBrand benefit

üEcosystem friendliness

DEFINING SUCCESS

@SVDataScience

BREAK

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

IDENTIFYING STRATEGIC WORKLOADS

HOW SVDS DOES DATA STRATEGY • We work with your stakeholders to analyze and articulate a data

strategy.

• The data strategy provides an actionable roadmap that generates immediate value and serves as the foundation for future capability investments.

• We work to understand your current business and technology landscapes in order to unlock untapped business opportunities.

• Our collaborative approach ensures that your business, product, and technology teams become effective advocates within your organization.

BUSINESS MODEL TRANSFORMATION

PRODUCT RESEARCH & RECOMMENDATION COMPANY

A product research and recommendation company is transforming their core business from content and information services to a referrer of high-value transactions to partners.

SVDS devised a data strategy that enables new analytical capabilities core to their retail ambitions, addressing critical accuracy and timeliness issues with unstructured data.

Based on this data strategy, they are building a solution for near real-time product inventory that increases their value to partners in a complex, multi-tier market.

PERSONALIZED USER EXPERIENCE

MEDIA & ENTERTAINMENT COMPANY

A media and entertainment company seeks to deliver personalized content directly to users on digital entertainment devices.

SVDS developed a data strategy and architecture that enables real-time data ingestion, deeper customer insight, and highly-personalized content recommendations.

The data strategy and architecture design now serve as the foundation for iterative, new product development and guide technology investments and acquisitions.

ACTION PLAN & ROADMAP

OUR METHOD FOR DATA STRATEGY

IDENTIFY STRATEGIC IMPERATIVES

DEFINE BUSINESS OBJECTIVES

DEFINE DATA REQUIREMENTS

IDENTIFY GAPS IN CURRENT SYSTEMS & TECHNOLOGY

MAP BUSINESS OBJECTIVES TO USE CASES

RATIONALIZE USE CASES INTO WORKLOADS

USE CASE

IDENTIFY YOUR STRATEGIC WORKLOADS

USE CASE

1 WORKLOAD

A WORKLOAD

WORKLOAD

B WORKLOAD

USE CASE

3 WORKLOAD

B WORKLOAD

AN EXAMPLE DATA STRATEGY FOR THE DOGS

NOTE: PetSmart is not an SVDS client. This is a fictional example based on public information. http://risnews.edgl.com/retail-news/PetSmart-Leverages-Analytics-for-Personalized-Experience91783

AN EXAMPLE DATA STRATEGY FOR THE DOGS

We've been investing in new capabilities to help us capture and use customer and pet data, and this year, we will deliver on new methods to use this data to drive growth.

— David Lenhardt PetSmart CEO

“

STRATEGIC IMPERATIVES

BUSINESS OBJECTIVES

USE CASES

WORKLOAD

Our strategy: “To be the preferred provider for the lifetime needs of pets.”

Connect with pet parents in a personalized way

Attract and retain our most valuable customers

Provide innovative products & services at fair prices

Drive consistent execution in our stores

AN EXAMPLE

Connect with pet parents in a personalized way

Deliver personalized recommendations and offers

Recommendation Engine

Recommend new pet products based on past

purchases at point of sale

Recommend upcoming store/community events

based on customer preferences

STRATEGIC IMPERATIVES

BUSINESS OBJECTIVES

USE CASES

WORKLOAD

BUSINESS OBJECTIVES

Illustrative

Connect with pet parents in a personalized way

Learn from consumer interactions

Optimize consumer journeys based on insights

Deliver personalized content to customers

. . .

STRATEGIC IMPERATIVES

BUSINESS OBJECTIVES

USE CASES

WORKLOAD

USE CASES

Deliver personalized content to customers

1. Identify customers 2. Profile behaviors

4. Anticipate behaviors

. . .

3. Understand context

5. Optimize personalization

Illustrative

STRATEGIC IMPERATIVES

BUSINESS OBJECTIVES

USE CASES

WORKLOAD

WORKLOADS Data Value Chain Example Workloads

Acquire • Capture mobile app transactions• Accessing streaming web activity data

Ingest • Flexible data ingestion• Ingest unstructured data

Process • Data validation• Omnichannel data integration

Persist • Heterogeneous data storage• Scalable data storage

Analyze • Probabilistic data integration• Predictive modeling

Expose • Service based data access• Interactive visualization

STRATEGIC IMPERATIVES

BUSINESS OBJECTIVES

USE CASES

WORKLOAD

TECHNICAL WORKLOADS

Acquire

Ingest

Process

Persist

Analyze

Expose

1. Identify customers Technical Workload

Customer data (Acquire, Ingest, Persist)

• Acquire multiple data sources & formats • Flexible data ingestion • Flexible & scalable data storage and

processing

Identity resolution • Probabilistic data integration

Data cleansing • Data validation

Householding • Probabilistic data integration

Relationship context • Detailed views of entities

Life-time Value • Feature engineering

Illustrative

2. Profile behaviors Technical Workload

360 degree view of customer • Detailed views of entities

Views of historical transactions • Time series analysis

Determination of ‘favorites’ • Predicting customer behavior

Map to archetype • Stream processing

Evaluate previously unseen transactions and classify

• Stream processing

Update archetypes • Feature extraction • Analyze customer behavior

TECHNICAL WORKLOADS

Acquire

Ingest

Process

Persist

Analyze

Expose

Illustrative

3. Understand context Technical Workload

Characterize temporal customer behavior

• Feature engineering • Analyze customer behavior

Determine goal of next interaction

• Predictive modeling

Categorize content needs • Predictive modeling

TECHNICAL WORKLOADS

Acquire

Ingest

Process

Persist

Analyze

Expose

Illustrative

4. Anticipate behaviors Technical Workload

Score product offers with likelihood to respond

• Integrate internal systems • Service based data access

Score content options with likelihood to respond

• Integrate internal systems • Service based data access

Identify next best action • Third party structured data integration

• Business rules execution

TECHNICAL WORKLOADS

Acquire

Ingest

Process

Persist

Analyze

Expose

Illustrative

5. Optimize personalization Technical Workload

Apply business rules, constraints to personalization options

• Business rule execution

Select optimal personalization to achieve goal

• Optimization execution

TECHNICAL WORKLOADS

Acquire

Ingest

Process

Persist

Analyze

Expose

Illustrative

PRIORITIES DIMENSIONS OVERCOME YOUR ASSUMPTIONS

FOCUS ON THE VALUE

DEVELOPMENT HORIZONS Illustrative

TECHNICAL WORKLOAD PRIORITIZATION

TECHNICAL WORKLOAD STRATEGIC VALUE

TECHNICAL FEASIBILITY

ACCESSIBILITY OF REQUIRED SKILLS

ARCHITECTURAL FIT

PROD ROLL- OUT EFFORT

Real time recommendations 10

Omnichannel data integration 10

Predictive modeling 9

Unstructured text analytics 8

Behavioral analytics 7

Data quality monitoring 6

Pattern recognition 5

Heterogeneous data storage 3

Data ingestion 3

Illustrative

DEFINE YOUR ROADMAP

Plan Prove Pilot Production

We define a project plan to build a specific capability.

For each capability, we describe a project to build technical workloads that implement use cases that address high-priority business objectives.

Silicon Valley Data Science employs an agile development processes as we work with our clients from planning and proof-of-concepts to pilot implementations and finally full scale production systems.

PROJECT ACTION PLAN

Plan Prove Pilot Production Agile Build Process

Illustrative

PATH FORWARD

Horizon I Horizon II Horizon III Horizon IV

2-3 months

5-6 months

3-4 months

0 months

Illustrative

DEVISING A PROJECT PLAN: INPUTS & APPROACH

Technical Workload AssessmentData Gaps

Project Roadmaps

Workload Rationalization Development Horizons

RINSE REPEATLATHER

MAKE SURE IT’S FLEXIBLE • Technology moves incredibly fast, and competitive

landscapes are highly dynamic.

• Your data strategy should be a living document, revisited often and revised as conditions change.

MAKE SURE IT’S ACTIONABLE • If it isn’t clear how you’re going to execute your

strategy, then you don’t have the right one.

• Must work within the realm of the possible and practical.

FROM IDEA TO PRODUCTION We identify the business goals, distill those into use cases, and then work in short, iterative cycles to achieve tangible gains.

Plan Prototype Pilot Production

What can we do with data?

MODERNIZING DATA TECHNOLOGY HEALTH MANAGEMENT COMPANY

Aging data infrastructure and brittle application integration was inhibiting growth and business insight for a health management company. Their data strategy focused on creating a concrete roadmap for migrating to a new data platform so that technology and infrastructure are no longer a barrier to growth and transparency. Based on this data strategy, they are building a new data platform in stages that allows them to add new products and services to capture more market opportunity.

Case Study: Data Strategy Major Pharmaceutical Company

Defined Data Strategy that will help enable business growth and enable expansion into new markets

Challenge • Ongoing need to improve

discovery and better predict new targets for drug development

• Difficulty to integrate new data sources into identification & discovery processes

• Inability to connect business strategy & aims with specific, tangible projects

Solution • SVDS devised a data strategy with a

concrete roadmap for migrating to a new data platform

• Recommended data technology & architecture which supports highest value projects

• Outlined cultural, technological, organizational, and collaboration challenges & objectives

Results • Identified specific opportunity areas

to increase GTM efficiency • Prescribed Common Data and

Analytics Platform for Commercial and R&D operations

• Recommended projects for Predictive Modeling & Data Exploration

DATA STRATEGY CHECKLIST ¨ Identify your business objectives ¨ Go from objectives to tactics ¨ Include all stakeholders in the conversation ¨ Look at how technology can support strategic

workloads ¨ Exploit patterns and reuse ¨ Prioritize the possibilities to figure out where to start ¨ Define your roadmap with an end-point in mind ¨ Lather, rinse, repeat

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

THE CHIEF DATA OFFICER

118

DO YOU NEED EXECUTIVE HELP?

To download a free PDF, go to: www.svds.com/CDOreport

EMERGENCE OF THE CDO • Started with heavily regulated industries such as

government and finance

• Now becoming common in “disruptable” industries such as retail and telecommunications

RESPONSIBILITIES OF THE CDO Centralization:

• Data from internal silos

• Data from external APIs and real-time streams

• The organization’s priorities

RESPONSIBILITIES OF THE CDO Evangelization:

• Technical chops, business savvy, and the diplomacy skills to translate between the two

RESPONSIBILITIES OF THE CDO Facilitation:

• Coordinate stakeholders across the organization

• Free up resources and lower barriers

• Offer tools and training to help others succeed

CHALLENGES FOR THE CDO Building technical bridges:

• Working with data in different silos, formats, etc.

Mining for business value:

• “If you don’t have good business questions it doesn’t matter what kind of technology you have.” — Joy Bonaguro

UNDERSTANDING THE CDO “While technology is inevitably involved when working with data, the defining goal of the CDO is not technological, but business-oriented. The ideal CDO exists to drive business value.”

— Julie Steele

DECIDING TO HIRE A CDO Know why you want one:

• Are you part of a regulated industry? • Do you need to move from being product-centric

to customer-centric? • Could you add products or services? • Could your current processes and outcomes be

optimized even further? • Are there insights in one part of your company

that could benefit others?

DECIDING TO HIRE A CDO Look for the right skill set:

• Technical chops

• Business savvy

• Diplomacy and political skills

• Executive-level experience

THE AVAILABILITY GAP “The spike in demand for Chief Digital Officers has been felt globally. In Europe, the number of search requests for this role has risen by almost a third in the last 24 months. The United States has seen the same growth in half that time.”

— Russell Reynolds Associates

PREPPING FOR SUCCESS Companies that are eager and prepared for real change will be the most appealing to qualified CDO candidates.

TODAY’S SCHEDULE Introduction

Why Have a Data Strategy?

Connecting Data with the Business

Understanding Data Gaps

The Data Platform Architecture

Break

Identifying Strategic Workloads

The Chief Data Officer

The Experimental Enterprise

THE EXPERIMENTAL ENTERPRISE

“…let's seek to understand how the new generation of technology companies are doing what they do, what the broader consequences are for businesses and the economy.”

– Marc Andreesen

DIGITAL NERVOUS SYSTEM

133 @SVDataScience

Data is your business.

Disruptive Change

Cloud Computing

Customer Content

Internet of Things

User Experience

SAAS & Apps

Business Intelligence Consumer IT

Regulation

Employees Partners

Contractors Suppliers

FROM: Innosight Executive Briefing Winter 2012

SILICON VALLEY’S DATA MACHINE

UP VS. OUT $,

€ , ¥

, £

Data Resource Usage

Scale-up cost

Scale-out cost

UC1

UC2

UC3

UC4

UC5

139 @SVDataScience

The legacy of big data is business agility.

• Make it cheap

• Failure as a feature

• Ask good questions

• Make it quick

• Both learning and adaptation

• Enable the feedback loop

• Don’t break things

• Make operations a platform for innovation

• APIs, platforms, simulation

BUILD FOR EXPERIMENTS

THE EXPERIMENTAL ENTERPRISE

Supports investigative work and builds a solid layer for production.

Conducts experiments and responds to the changing environment.

Makes foundational infrastructure readily accessible.

LEAD A DATA REVOLUTION • You can only win with situational awareness

• New architectures offer new opportunities

• Creation of data-driven value requires new approach

• Create an Experimental Enterprise

• Business must lead, and understand the potential of the technology

To view SVDS speakers and scheduling, or to receive a copy of our slides, go to:

www.svds.com/StrataCA2017

THANK YOU

Ask how we can help info@svds.com

Edd Wilder-James (@edd)

Scott Kurth (@ScottWKurth)

March 2017