Download - Data Architecture Strategies: Building an Enterprise Data Strategy – Where to Start?

Building an Enterprise Data Strategy –Where to Start?

Donna Burbank, Managing DirectorGlobal Data Strategy, Ltd.

February 22nd, 2018

Follow on Twitter @donnaburbankTwitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2018

Donna Burbank

Donna is a recognised industry expert in information management with over 20 years of experience in data strategy, information management, data modeling, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership.

She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric

technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market.

As an active contributor to the data management community, she is a long time DAMA International member, Past President and Advisor to the DAMA Rocky Mountain chapter, and was recently awarded the Excellence in Data Management Award from DAMA International in 2016.

Donna is also an analyst at the Boulder BI Train Trust (BBBT) where she provides advice and gains insight on the latest BI and Analytics software in the market. She was on several review committees for the Object Management Group’s for key information management and process modeling notations.

She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co-authored two books: Data Modeling for the Business and Data Modeling Made Simple with ERwin Data Modeler and is a regular contributor to industry publications. She can be reached [email protected] is based in Boulder, Colorado, USA.

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Follow on Twitter @donnaburbankTwitter Event hashtag: #DAStrategies

mailto:[email protected]


DATAVERSITY Data Architecture Strategies

• January - on demand Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?

• February Building an Enterprise Data Strategy – Where to Start?

• March Modern Metadata Strategies

• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business

• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape

• June Artificial Intelligence: Real-World Applications for Your Organization

• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset

• August Data Lake Architecture – Modern Strategies & Approaches

• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture

• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit

• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks

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This Year’s Line Up for 2018


A Word From Our Sponsor

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Datawatch


Data Strategy Across The Organization

OFF

ENSE

DEF

ENSE

OPERATIONAL EFFICIENCY ANALYTIC INSIGHT

HR

SALESC-LEVEL

MARKETINGFINANCE

BI

IT


Bridge the Gap: Satisfy IT & Data Workers

Analysts need:Collaboration

A way to share their work

A way to ensure their work is based on trusted data

A means to showcase their contribution of models and analyses

A way for other analysts and teams to contribute back to their creations

IT needs:Centralization

A solution that empowers all users to easily work with data

Visibility and controls for governance and compliance

Reduced burden - domain experts manage domain data

Insight into which data is most valuable so they can focus their efforts

The organization needs:

Socialized data access

Awareness of what data and assets are being created and by whom

All users working and collaborating in the same place

Agility and governance in one solution

Analysis is conducted based on trusted and certified information

Consistency in models across groups, teams, enterprise


Organizational Analytics Goals

PROMOTE COOPERATION

Bring teams together:

Data scientists

Analysts

Data/ETL engineers

IT

QA/Governance

ENHANCE VISIBILITY

Emphasize:

Communication

Automation

Collaboration

Integration

Measurement

IMPROVE PRODUCTIVITY

Help analytic teams:

Rapidly produce insight

Operationalize that insight

Continuously improve analytic operations & performance


What We’ll Cover Today

• The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage.

• While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming.

• From Big Data … • … to Artificial Intelligence …• … to Data Lakes and Warehouses• … the industry is continually evolving to provide new and exciting technological solutions.

• This webinar will help make sense of the various data architectures & technologies available, and how to leverage technology for business value and success.

• A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture.

• Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.

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Global Data Strategy, Ltd. 2018 9

A Successful Data Strategy links Business Goals with Technology Solutions

“Top-Down” alignment with business priorities

“Bottom-Up” management & inventory of data sources

Managing the people, process, policies & culture around data

Coordinating & integrating disparate data sources

Leveraging & managing data for strategic advantage

Copyright 2018 Global Data Strategy, Ltd

Aligning Business Strategy and Data Strategy


How can we Transform our Business through Data?Business Optimization

Becoming a Data-Driven Company• Making the Business More Efficient

• Better Marketing Campaigns• Higher quality customer data, 360 view

of customer, competitive info, etc.• Better Products

• Data-Driven product development, Customer usage monitoring, etc.

• Better Customer Support• Linking customer data with support logs,

network outages, etc.• Lower Costs

• More efficient supply chain• Reduced redundancies & manual effort

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Business TransformationBecoming a Data Company

• Changing the Business Model via Data – data becomes the product.

• Monetization of Information: examples across multiple industries including:

• Retail: Click-stream data, purchasing patterns

• Social Media: social & family connections, purchasing trends & recommendations, etc.

• Telecom: location information, usage & search data, etc.

• Energy: Sensor data, consumer usage patterns, smart metering, etc.

How do we do what we do

better?

How do we do something different?


Basic Definitions

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Business & Data Strategies

A BUSINESS STRATEGY is a medium to long term business plan which details the aims & objectives of a business and how it means to

achieve them.

A DATA STRATEGY is a medium to long term plan for the improvement, management & exploitation of data across a business, and how it is

to be achieved.


Business & Data Strategy – the Interdependency

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Business Strategy Data Strategy

Sets Requirements for

Informs & Guides

Business Strategy


Getting it Wrong

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What can cause Business and Data Strategies to become Misaligned?

Lack of a clearly articulated business strategy

Absence of Business & IT senior leadership in strategy

formulation & execution

Business fails to take ownership of the data and

hence the data strategy

Data strategy is viewed as a technology roadmap, led by

IT

Lack of cross-business / IT collaboration & communication

Complexity and lack of priority, focus & deliverable

milestones

Not showing short-term results & benefits

Lack of skills and expertise to realize the strategy


Getting it Right

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The Key Features of an Effective Data Strategy

ALIGNED Directly Connected to Business Drivers.

ACTIONABLE with clear activities & milestones

EVOLUTIONARY to meet changing business needs & new technology

UNIQUE to the specific organization


Consumer Energy Company

• For the consumer energy sector Big Data and Smart Meters are transforming the ways of doing business and interacting with customers. • Moving away from traditional data use cases of metering & billing.• Smart meters allow customers to be in control of their energy usage.

• Control over energy usage with connected systems• Custom Energy Reports & Usage• Smart Billing based on usage times

• As energy usage declines, data is becoming the true business asset for this energy company.

• While the Big Data Opportunity is crucial, equally important are the traditional data sources• Data Quality critical for operational and analytic data• Data Governance critical for analyzing data in relation to business processes & roles• With high volumes of data, critical data elements prioritized

Business Transformation through Data


Increasing Restaurant Revenue through Menu Data

• An international restaurant chain realized through its digital strategy that:• While menus are the core product that drives their business…• They had little control or visibility over their menu data• Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing,

restaurant operations, etc.• Menu data was consolidated & managed in a central hub:

• Master Data Management created a “single view of menu” for business efficiency & quality control• Data Governance created the workflow & policies around managing menu data

• Process Models & Data Mappings were critical• Business Process diagrams to identify the flow of information• CRUD Matrixes to understand usage, stewardship & ownership

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Managing the Data that Runs the Business

Product Creation & Testing

Menu Display & Marketing

Supply Chain Point of Sale & Restaurant Operations


Optimizing Customer Experience through Data

• A major Retail Vendor wanted to become a data-driven company• Enhancing the customer experience by mapping the Customer Journey to the Data Lifecycle• Using IoT product data to improve product design & customer service• Optimizing product supply chain & delivery

• Developing a Tactical Data Strategy determined that• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.• Master Data Management was needed to manage customer contact data throughout the Customer Journey.

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Using Data to Build Customer Loyalty & Increase Sales

Customer SupportCustomer Discovery & Purchase

Product Delivery & Tracking

Product Usage Tracking Product Development


UK Environment Agency

• The UK Environment agency worked with Global Data Strategy to develop Data Models & Data Standards in order to support Open Data publication of key environmental measures.

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Supporting Digital Transformation & Open Data Publication

• Land boundaries• Air & Water Quality

• Fish & Wildlife populations• Etc.

• Becoming a Data-driven organisation included:• Digital Transformation – all services online• Open Data – promoting data sharing with public

• Common Data Models & Standards helped create a common lingua franca across the organization:

“Establishing a standard is a really important step in bringing our information together so we can be better joined up, better integrated

and work together more efficiently.”- National River Basin Operations Manager, Environment Agency

• Saving time & money• Supporting Regulation• Enhancing public

reputation

• Improving data quality & consistency

• Increasing collaboration between teams


Managed Care Organization: Telling the Data “Story”

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Data Drives Everything We DoCentered around Serving our Members

Member

Is Assisted by

ProviderStaff

Location

Credentials

Community Living

Visits an Emergency Room at

Would like to transition to

Needs an overnight stay in

Bed

Has Availability for

Is Assisted byIs Certified for

Practices at

Apply to

Support & Certify

Community

Train & Inform

Interacts with


The Importance of MotivationFrom Cruise Ship to Life Raft

With a common motivation, disparate skills, personalities and roles become an asset, not an annoyance


Business Motivation Model

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Corporate Mission Corporate Vision

Goals & Objectives

To provide a full service online retail experience for art supplies and craft products.

To be the respected source of art products worldwide, creating an online community of art enthusiasts.

Artful Art Supplies ArtfulArt

C

External Drivers

Digital Self-Service Increasing Regulation Pressures

Online Community & Social Media

Customer Demand for Instant Provision

Internal Drivers

Cost Reduction

Targeted Marketing 360 View of Customer

Brand Reputation Community Building

Revenue Growth

C

Accountability• Create a Data Governance

Framework• Define clear roles &

responsibilities for both business & IT staff

• Publish a corporate information policy

• Document data standards• Train all staff in data

accountability

C

Quality• Define measures & KPIs for

key data items• Report & monitor on data

quality improvements• Develop repeatable

processes for data quality improvement

• Implement data quality checks as BAU business activities

C

Culture• Ensure that all roles

understand their contribution to data quality

• Promote business benefits of better data quality

• Engage in innovative ways to leverage data for strategic advantage

• Create data-centric communities of interest

• Corporate-level Mission & Vision• May already be created or may

need to create as part of project.

• Project-level, Data-Centric Drivers• External Drivers are what you’re

facing in the industry• Internal Drivers reflect internal

corporate initiatives.

• Project-level, Data-Centric Goals & Objectives

• Clear direction for the project• Use marketing-style headings

where possible


The Role of the Data Professional in the Data-Driven Business

• In the current environment of data-driven business, Data Professionals have an opportunity to have a “seat at the table”

• Finding new opportunities to leverage data for business benefit• Creating efficiencies & business process optimization• Integrating data from disparate sources for new business insights• Supporting organizational change

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Business Executive

• Results-Oriented• Optimistic – Identifies opportunities • “I’m busy.”• “What’s the business opportunity?”

Data Architect

• Focused on architecture, data, technology• Often seen as finding problems, not

solutions• “Let me tell you about my data model!”

Data Advisor

• Focused on solutions, business, information• Highlights issues & opportunities around

data• “Less me show you how data can help your

business!”

The world is going to end if your model is not in 3rd normal form!!

If you link your Customer data with your Product usage stats, we can

increase sales.What’s in it for me?

Be More “Data Advisor” and Less “Data Architect”


Find a Balance in Implementing Data Architecture

• Find the Right Balance• Data Architecture projects can have the reputation for being overly “academic”, long, expensive, etc.• No architecture at all can cause chaos.• When done correctly, Data Architecture helps improve efficiency and better align with business priorities

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Focus on Business Value

Business Value

Too Academic, nothing gets done

Too “Wild West”, nothing gets done - chaos


Data Management Maturity Assessment

Current State Future State

Strategy 2.8 4.3

We have a Data Strategy for maximizing the use of data within our organization 3 5

Our Data Strategy is aligned to our Business Strategy. 3 5We have executive and/or senior-level business support and sponsorship for our strategy 2 4We have published a plan to achieve our Data Strategy that includes organization support, process, and IT. 4 4We have organizations and budgets in place to support our Data Strategy and Plan. 3 4Our Data Strategy is published and well understood across lines of business and technology groups. 2 4

Etc…

Data Governance 3.0 5.0

We have a standard, auditable process for resolving data governance issues, such as change management, priority management, conflict resolution, etc. 3 5

We have data stewards who manage key data used across functional groups. They have clearly defined and well understood roles and responsibilities. 4 5We have identified business-critical data elements and define & store them in a commonly-accessible format. 4 5All of our people understand the importance of data to the organization and their personal responsibilities for managing it. 3 5We have a data communications strategy to ensure data policies and standards are reinforced across our organization. 3 5Data related objectives and responsibilities are formally included in job descriptions and/or personal objectives. 1 5Our data governance process has a senior business and IT sponsorship and stakeholder buy-in across the functional leaders. 3 5Etc…Master Data Management 4 5Etc….continue for each capability area 1 4

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Ask a Detailed Set of Questions for Each Functional AreaShow Current State and desired Future State. You don’t have to be a “5” in everything!


Visualizing Current vs. Target Maturity

• A Radar Chart (“Spider Chart”) can be a helpful way to visualize the relative strengths & weaknesses in various capability areas.

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Determine Relative Strengths

Significant Gap in Data Governance

Metadata Management meets Target Maturity

We’re “overdoing it” for Data Architecture


Master Data Management

Data Governance

Strategy

Mapping Business Drivers to Data Management Capabilities

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Business-Driven PrioritizationStakeholder Challenges

Lack of Business Alignment• Data spend not aligned to Business Plans• Business users not involved with data

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360 View of Customer Needed• Aligning data from many sources• Geographic distribution across regions

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Data Warehousing

Business Intelligence

Big Data Analytics

Data Quality

Data Architecture & Modeling

Data Asset Planning & Inventory

Data Integration

Metadata Mgt

Business Drivers

Digital Self ServiceIncreasing Regulation

Pressures

Online Community & Social Media

Customer Demand for Instant Provision

Internal Drivers

External Drivers

Targeted Marketing

360 View of Customer

Revenue Growth

Brand Reputation

Community Building

Cost Reduction

Integrating Data• Siloed systems • Time-to-Solution • Historical data

3

Data Quality• Bad customer info causing Brand damage• Completeness & Accuracy Needed

4

Cost of Data Management• Manual entry increases costs• Data Quality rework• Software License duplication

5

No Audit Trails• No lineage of changes• Fines had been levied in past for lack of

compliance

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New Data Sources• Exploiting Unstructured Data• Access to External & Social Data

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1 7

1 2 3 4 5 6

71 2 3 4 5 6

1 72 3

1 72 3

1 2 6

72 3

53 4

1 2 3 4

63 5

2 3 5 7

Shows “Heat Map” of Priorities


Speak with a Wide Variety of Stakeholders

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• It’s important to speak with a wide range of roles across the organization.

• Business & IT• Cross-functional teams (Marketing,

Finance, Analytics, etc, etc.)

• Understand key opportunities & challenges.

• Recruit allies & volunteers (and identify those you still need to convince. )


Stakeholder Matrix

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• Create a simple stakeholder matrix outlining the key stakeholders, their roles, involvement, influence, impact, etc.

• Keeping track of “who’s who”: Create a simple stakeholder matrix outlining the key stakeholders, their roles, involvement, influence, impact, etc.

RACI *:R: ResponsibleA: AccountableC: ConsultedI: Informed


Data Source Inventory• Document key data sources across the organization• …as well as who is using them (i.e. key departments & stakeholders)• Data models & other architecture tools can help document the technical structures & metadata

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Data Sources Leadership Sales Finance Marketing Support R&D HR Legal Compliance

Relational DatabasesMySQL XOracle X X X X X X X XSQL Server X XSybase XEtc.

BI ToolsTableau X X X X X XQlik X X XEtc.

Open DataData.gov – agricultural data X X X

Etc.


Industry Trends: Data Platforms are Currently in Use?

• A wide range of technologies are currently in use:• Relational databases most common

o Both Cloud & On-Premises• Spreadsheets ubiquitous

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“Which of the following data sources or platforms are you currently using? [Select all that apply]

Relational Databases are still clearly the

leader.

Spreadsheets are ubiquitous

More Legacy platforms (44.6%)

than Big Data (42.2%)

From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017


Industry Trends: Emerging Technologies

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“Which of the following do you plan to use in the future that you are not using currently? [Select all that Apply]”

Many looking to Big Data Platforms

Movement to the Cloud is popular

Uncertainty is common.

• For those looking at new technologies, there is a wide range of responses.

• Big Data Platforms a leader• Move to Cloud RDMBS• Graph Database• Real-time Streaming• Internet of Things (IoT)

• Many are still uncertain, indicating the vast rate of change and wide array of choices available.

From Emerging Trends in Data Architecture, DATAVERSITY, by Donna Burbank & Charles Roe, October 2017


Implement “Just Enough” Data Governance• Each type of data has its own type of governance model & sharing paradigm• As a general rule, the more the data is shared across & beyond the organization, the more formal governance needs to be

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Core Enterprise Data

Functional & OperationalData

Exploratory Data

Reference & Master Data

Core Enterprise Data• Common data elements used by multiple

stakeholders across Bus, LOBs, functional areas, applications, etc.

• Highly governed• Highly published & shared

Functional & Operational Data• Lightly modeled & prepared data for

limited sharing & reuse• Collaboration-based governance• May be future candidates for core data

Exploratory Data• Raw or lightly prepped data for

exploratory analysis• Mainly ad hoc, one-off analysis• Light touch governance

Examples• Operational Reporting• Non-productionized analytical model data• Ad hoc reporting & discovery

Examples• Raw data sets for exploratory analytics• External & Open data sources

Examples• Common Financial Metrics: for Financial & Regulatory Reporting• Common Attributes: Core attributes reused across multiple areas

(e.g. Customer name, Account ID, Address)

Master & Reference Data• Common data elements used by multiple stakeholders

across functional areas, applications, etc.• Highly governed• Highly published & shared

Examples• Reference Data: Procedure codes, Country Codes, etc• Master Data: Location, Customer, Product


Identify What Data Needs to Be Governed

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And What to Leave Alone

Launch of New Product – Marketing Campaign requires better customer information

Customer Product

RegionVendor

Partner

Identify Key Business Driver

Filter Data Elements Aligned with Business

Driver

Focus Governance Efforts on Key Data

What?Why? How?

Structured Warehouse for Financial Reporting

Exploratory Analytics & Discovery

Lightly governed

Social Media Sentiment Analysis

Financial Reporting

Highly governed


Crowdsourcing Governance & Metadata Definitions• Many data governance projects (& vendors) are embracing the concept of “crowdsourcing”. i.e. The

Wikipedia vs. Encyclopedia approach• Open editing• Popularity & Usage Rankings• Dynamically changing

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Encyclopedia Wikipedia• Created by a few, then published as read-only• Single source of “vetted” truth• Static

• Created by a by many, edited by many• Eventual consistency with multiple inputs• Dynamic

For Standardized, Enterprise Data Sets For Self-Service Data Prep & Analytics


Finding the Right Balance

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• When implementing successful data governance in today’s rapidly-changing, self-service data landscape, it is important to find a balance between:

Standards-based Governance

The two methods work well together, using the right approached depending on the data usage.

Collaboration-based Governance

• Well-suited for enterprise-wide data standards • Well-suited for self-service data

preparation & analytics


Applying a Structured Data Governance Framework

Organization & People

Process & Workflows

Data Management & Measures

Culture & Communication

Vision & Strategy

Tools & Technology

Business Goals & Objectives

Data Issues & Challenges


Building the Data Governance Framework

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Vision & Strategy Organization & People

Processes & Workflows

Data Management & Measures

Culture & Communications Tools & Technology

Is there a clear understanding of the strategic goals of your organization & the need for enterprise data governance?

Who are the key data stakeholders within and outside your organization?

Do business process design and operations management take data needs into account?

Has key data been identified, defined and analyzed?

Has the importance of data been communicated across the organization? Is there a data communications plan?

Is there a coherent data architecture in place to define and guide how data is captured, processed, stored and used?

How does your organization rely on data – now and in the future?

Who are the primary data producers, consumers & modifiers?

Are there any specific data management / improvement processes in place?

Have data models been built –conceptual / logical / physical?

Is the value of good data management understood and championed by senior managers?

What primary IT systems and platforms are used to store and process key data?

What impact are data problems currently having on your organization?

Are individuals formally accountable for data ownership?

Are there issue and workflow management processes to address data problems?

Has the relationship between business processes and data been mapped?

Do all employees and third parties receive data awareness and improvement education and training?

Do design gateways exist to ensure data needs are taken into account in new & modified platforms?

Do you have a data governance policy?

Are employees trained in good data management practices?

Has there been any analysis of the efficiency and effectiveness of how data is managed within operational business processes?

Are data shortcomings known, measured & recorded?

Are there communication channels for communicating best practice in data management?

What specialist data management tools are currently in use?

What are the overall expected benefits of better data governance?

Are there any channels through which data shortcomings can be highlighted and investigated?

How does the business and IT interact to manage data improvement?

Are there are formal standards & rules specifying how data should be managed and improved?

Are there internal success stories that could be used to promote better data management across the organization?

What metadata is captured and stored?


Measuring Data Improvements

• KPIs & Measures aligned with concrete business drivers• Helps prioritize efforts• Assists with the “Why do I Care?” issue• Basis for showing benefits and results

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Align Data Quality Metrics to Business Improvements

KPI Current Target Status Business Benefits TypeNumber of duplicate customer records

2,000,000 1,000 • Correct # of customers for sales estimations• Better single view of customer for integrated social media campaign

• Reduce cost of physical mailing by $20K

• Cost savings• Brand Reputation• Marketing Innovation

Incorrect Salutation (Mr,Ms, etc.)

5,000 1,000 •Customer satisfaction & Brand reputation harmed by incorrect salutation.

•Targeted marketing campaigns by gender.

• Brand Reputation• Campaign Effectiveness

Incorrect address/location 10,000 500 • Lower return rate on physical mailings• Better targeted marketing by region.

• Cost Savings• Campaign Effectiveness

Missing Sales Rep Assigned 500 100 • Ability for Sales to execute on customer leads• Revenue growth

• Sales Effectiveness

Etc.

Business Driver: Improving Customer Data for Marketing Launch Campaign


Look for Business Value “Levers”

• Identify areas that will derive the highest business value by addressing.

• Is this supporting the new marketing campaign for a high visibility product launch?

• Or are you “re-arranging the deck chairs on the Titanic” – i.e. focusing valuable time and effort no low-value activities

• As with any areas of the business that have value, it is helpful to build a model or architectural design around the key areas of business value.

Identify “Quick Wins”LoadEffort

Fulcrum

Identify areas where data can be the fulcrum.


Defining an Actionable Roadmap

• Develop a detailed roadmap that is both actionable and realistic• Show quick-wins, while building to a longer-term goal• Balance Business Priorities with Data Management Maturity• Focus on projects that benefit multiple stakeholders• Mix core architecture with “new shiny things”

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Maximize the Benefit to the Organization

Initiatives H1 '17 H2 '17 H2 '18 H2 '18

Strategy Development

Governance Lineage for Privacy RulesBusiness Glossary Population & Publication

Data Warehouse Metadata

Customer Analytics Pilot –Social Media integration

Open Data Publication

IoT Integration

Ongoing Communication & Collaboration

Customer Product Location

Integrated Customer View

Marketing

Sales

Customer Support

Executive Team

Global Data Strategy, Ltd. 2018 42

Key Steps to Creating a Data Program

• The following steps should be included when creating a data program. The order is less important than ensuring that they are completed.

Steps to Success

Secure Senior Executive Support

• Identify a Data Champion among senior leadership.

Define Vision, Drivers & Motivations

• Define business-driven vision for the program.

Build the Business Case

• Outline key benefits of data program & risks of not doing so

Deliver “Quick” Wins

• Short, iterative, business-driven projects deliver short-term value, building towards long-term gain.

Identify Business-Critical Data

• Focus on the data that has the highest impact on the business.

Identify & Interview Stakeholders

• Elicit feedback from key stakeholders – listen & communicate.

Create Organization

• Define an organizational structure that aligns with your way of working.

Communicate

• Build a communication plan from initial feedback phase throughout all phases of the program.

Assess IT Maturity

• Assess the maturity of the IT organization across all aspects of data management.

Map Business Priorities to IT Capabilities

• Create a realistic “heat map” aligning business goals with data management capabilities.


Summary

• Aligning Data Strategy & Data Architecture with business drivers & goals is key to success • Adapt your data architecture for both innovative & legacy technologies• Orchestrate the people, process, technology, & culture required to support your data

architecture through a robust Data Governance program• Design data quality and metadata into your data architecture from the outset, and how to

design core KPIs and metrics to track success• Build “quick wins” into your roadmap to provide business value through every stage of your

architecture development

Where to Start? - With Business Drivers


About Global Data Strategy, Ltd.

• Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology.

• Our passion is data, and helping organizations enrich their business opportunities through data and information.

• Our core values center around providing solutions that are:• Business-Driven: We put the needs of your business first, before we look at any technological solution.• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s

size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, and we attract high-

quality professionals with years of technical expertise in the industry.

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Data-Driven Business Transformation

Business StrategyAligned With

Data Strategy

www.globaldatastrategy.com

http://www.globaldatastrategy.com/


Related Article

• Related article on DATAVERSITY, Sept 2017:• Data Management vs. Data Strategy: A

Framework for Business Success

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To Read More

http://www.dataversity.net/data-management-vs-data-strategy-a-framework-for-business-success/


DATAVERSITY Data Architecture Strategies

• January Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?

• February Building an Enterprise Data Strategy – Where to Start?

• March Modern Metadata Strategies

• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business

• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape

• June Artificial Intelligence: Real-World Applications for Your Organization

• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset

• August Data Lake Architecture – Modern Strategies & Approaches

• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture

• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit

• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks

46

This Year’s Line Up for 2018 – Join Us Next Month


Questions?

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Thoughts? Ideas?

Download - Data Architecture Strategies: Building an Enterprise Data Strategy – Where to Start?

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