dama new england · 2020. 12. 9. · new england data management community welcome our presenters...

DAMA New England

Aligning Data Management with Digital Strategy for Innovation and Transformation

May 27, 2020

Lynn Noel, DAMA New England - MemberEmail: [email protected]

New England Data Management Community

• A Chapter of DAMA International • The premier educational & professional networking resource for the

enterprise data management community in New England• Host educational events, via Webinars and on-site (when permitted)• Promote learning based on the DMBoK2 knowledge areas• See our Website: DAMANewEngland.org for more information • Membership benefits include:

• Free or discounted admission• Educational and networking events• Growing base of knowledge items available via Member-only section of the Website• Peer-to-peer networking and mentoring opportunities

Hosted by DAMA New England

We welcome you to be part of our growing community!

http://www.damanewengland.org/


WELCOME OUR PRESENTERS


LYNN E. NOEL

Principal, Digital Heritage Consulting Executive Certificate in Digital Business Strategy, MIT Mini-Masters in User Experience Design, Rutgers M.S. Geography, University of Wisconsin-Madison Most recently VP of Digital Strategy, Data Blueprint Director/mentor for IM & analytics, architecture & modeling,

collaboration & content mgmt at Big Five IT, midsize, & startup firms

CONTACT INFO:EMAIL: [email protected]

PHONE: +1-978-985-2707

: /IN/lnoel


AGENDA

• So What is Digital Business Strategy?• Key Questions for Data Managers

• Machine, Platform, Crowd• Core Concepts in Digital Business

• DMBoK 2.0 and Beyond• Digital, Disruption, & Transformation in the DMBoK• Where the DMBoK Can Drag and Drive Digital

• From Disruption to Transformation• Example Client Digital Vision and Strategic Data Roadmap

• Call to Action• Answering Key Questions

• Q & A



“Let’s Talk Strategy First” Is About Digital Business, Not Data

5

• Digital business is the creation of

new business designs by blurring

the digital and physical worlds.

(Gartner Glossary)• A digital business is one that uses

technology as an advantage in its internal and external operations. (Komprise Glossary)

• What makes digital business different from e-business is the presence and integration of things, connected and intelligent, with people and business. (Forbes)

https://www.gartner.com/en/information-technology/glossary/digital-businesshttps://www.komprise.com/glossary_terms/digital-business/https://www.forbes.com/sites/gartnergroup/2014/05/07/digital-business-is-everyones-business/#3e7438577f82


State of Digital Business 2018 Report (IDG)


Every Organization’s Data Needs a Reset for Digital

Annotations in red by Lynn Noel, Digital Heritage Consulting, 2018


"First you mature, then you ripen and rot.“ (Woody Allen)

• Data managers must innovate faster than ever to organize and govern human and AI networks of people and things that produce bigger and less structured data.

• What do we need to know to deliver data management at digital speed and scale? • Who are the new executive sponsors for data management?

• What does a governance roadmap need to do in the first 3-6 months?

• Why and how must data governance guide technical platform governance?

• Where do you start when your data environment seems too big to know?

• How does advanced analytics drive and disrupt data management strategy?


Machine, Platform, Crowd: Digital Business Strategy

For machine intelligence, the counterpart is the human mind. Accountants with spreadsheets, engineers with computer-aided design software, and assembly line workers next to robots are all examples of mind-and-machine combinations.

The counterparts of platforms are products, or goods and services. A ride across town is a product, while Uber is the platform people use to access it. The same is true with accommodations and Airbnb, or news stories and Facebook.

For the crowd, the counterpart is the core– the knowledge, processes, expertise, and capabilities that companies have built up internally and across their supply chains. For example, the core of GE Appliances designs, manufactures, and markets refrigerators and ovens; NASA’s core builds spaceships and tries to better understand our universe; and Microsoft’s core capabilities include developing personal computer operating systems and applications.


Three Great Rebalancings


Rebalancing Around the DAMA Wheel

Data Catalogs

Cloud Analytics

AI & Machine Learning

Text Mining

Low-Code Connectors

GDPR

Bots & AI

Hybrid Cloud

Productized Platforms

Crowd-sourcing

IoT

DevOps & DataOps

Post-SQL

Reference Standards


From Database Pyramid to Digital NetworkEach Business Unit Meets Each Transformation At Their Own Pace

Cloud Analytics with AI

GDPR Security & Privacy

Hybrid Cloud DevOps

Data Governance Catalog

New EDW for MDMCo

mm

on

Tra

nsf

orm

atio

ns

Digital Transformation is Orthogonal to “Database Capability”


Data Governance for Core, Crowd, and Customer

Automated Rules Enforce Data Governance

Machine Learning Drives Ontology Development

Enterprise Data Governance Platforms Scale Out

Mobile Platforms Offer Data Access and Availability

Customer Portals Offer a Degree of Self-Governance

Crowdsourcing Builds Glossaries and Taxonomies


Data Architecture for Productized Application Platforms

Deep Learning Algorithms Design Data Architectures

Data Virtualization Adds an Abstraction Layer

Apps and Platforms Standardize Data Models

Big Data Architecture for Data Lakes and Beyond

Data Architecture Scoped During the Platform Selection Process

“Out-of-the-box” core governance vs. ‘must-have” crowd customization


Data Modeling and Design for Migration and Transformation

Big Data can extract models from high-volume/variety data

Semantic machine learning drives descriptive modeling

Modeling platforms provide iterative round-trip visualization

Productization & hybrid cloud automate performance tuning

Modeling, metamodeling, reference modeling standards emerging rapidly

Self-service analytics gives everyone their own data modeling capability


Data Storage and Operations for the Hybrid Cloud

AI produces and requires Big Data beyond human scale

Deep learning may use gigabyte files and petabyte storage

Cloud platforms are virtualized, elastic, scalable, multi-tenant

Platform innovations like hybrid flash memory (ROM+RAM)

DataOps processes and lifecycles evolving from DevOps and Agile

Operations teams move from DBAs to “contextual recommenders”


Data Security, Privacy and Ethics for the World of GDPR

Machine learning good for fuzzy problems in cybersecurity

Systemic bias: AI and ML algorithms are only as secure and ethical as the humans and datasets used to train them

Platform ecosystems can federate access and identity

84% of 600 executives surveyed actively looking at blockchain

79% of global executives rank cyber attacks and threats as one of their organization’s highest risk management priorities in 2020

Security responsibility is diffusing across the entire organization


Data Integration and Interoperability for Platform Scale

ETL Logic Must be Decoupled for Phased Transformations

AI-Driven Data Cataloging for Migration and Integration

Interoperable Platform Ecosystems that Merge and Spread

APIs and Embeddables with Low- to No-Code Connectors

Crowdsourcing Datasources Fragments Interoperability

Social Data Sharing Becomes a De Facto Integration Layer


Document and Content Management for the Digital Workplace

Embedded AI is Everywhere in the Digital Workplace

Content Management Systems are Data Lakes for Text Mining

Remote Work Makes the Digital Workplace the New Normal

Productivity Makes Collaboration Platforms Transparent

Video, Especially Live Video, is the Fastest Growing Content Format

“Alexa, send me the link” Personalized Content Push Meets AI Voice Search


Reference and Master Data for Digital Customer Experience

Automated Master Data Enrichment Needs Good Data Quality

Match and Merge Are Essential AI/ML Capabilities

Schema-On-Read Data Lakes Require MDM and Governance

Multidomain MDM Platforms Move from Collecting to Connecting

Omnichannel Customer Experience Demands 360 Master Data

Reference Standards for Digital Metadata Emerging Rapidly


Data Warehousing for Real-Time Transactions and Analytics

ETL Batch Jobs No Longer Scale to Real-Time Processing

ML and Data Science Have Converged with EDW in the Cloud

Real-Time Makes OnLine Analytical Processes Transactional

Cloud Data Warehousing Offers Massively Parallel Analytics

Crowdsourced Data Collection Outpaces the In-House EDW

Open Data Sources and Standards Proliferate


BI & Analytics Far Beyond the Central Data Warehouse

Data Science Leveraging AI, Machine Learning, Deep Learning

Embedded AI in Self-Service Analytics

Self-Service Analytics Platforms Require a Learning Curve

The Data Has Left the Warehouse and It’s Loose in Excel

Crowdsourced Analytics Outstrip and Conflict with Core BI Systems

Change Management for Both Canned Reports and Real-Time Analytics


Metadata Drives a Rich Digital Business Experience

Semantic Data Catalogs for Automated Lineage

OCR + Metadata = Search Within Documents

Metadata Management Platforms Aid Discoverability

Blockchain Platform as an Enabling Technology

Crowdsourcing Business Glossaries and Taxonomies

Reference Standards for Digital Metadata Emerging Rapidly


Data Quality for Volume, Variety, and Velocity

Big Datasets suffer from noise, missing values, inaccuracies

AI/ML anomaly detection can improve and correct data quality

Platform Go-Lives Require Clean Data Loads for Testing

Data Catalog Platforms Combine Quality, Metadata, MDM

Crowdsourcing platforms like Amazon's Mechanical Turk can be as reliable as more traditional centralized methods

When data quality is private or advantageous information to workers, there can be a disincentive to report or to correct errors


“We Are Transforming:” Example Digital Strategy Vision

Data Management Strategy is Not Visible at this Level of Enterprise Strategy


Rolling the DAMA Wheel Across a Real-World Digital Roadmap

Hybrid cloud

integration

Data loads

for testing

Midyear Board reporting

Annual Audit

Incentive comp

from new LOS

Dual entry? Reconciliation

Decouple ETL logic

Customer-facing data quality

Metadata

enrichment

EOY Board reporting

KPIs on new GTM Strategy

Compliance Changes

Customer KPIs

New Process KPIs

Banking Example: Data Strategy Can Become An Action Layer for Enabling Digital Roadmaps

Redesign BPM Rules


From Maturity to Innovation and Transformation

• Data managers must innovate faster than ever to organize and govern human and AI networks of people and things that produce bigger and less structured data.

• What do we need to know to deliver data management at digital speed and scale? • Who are the new executive sponsors for data management?

• What does a governance roadmap need to do in the first 3-6 months?

• Why and how must data governance guide technical platform governance?

• Where do you start when your data environment seems too big to know?

• How does advanced analytics drive and disrupt data management strategy?


New Executive Sponsors for Data Management

Has your C-suite changed to reflect the changing times? (EY 2019)


Rebalance (Assess)

Customer Data

Employee Data

Owner Data

Innovate (Roadmap)

Customer Experience

Digital Workplace

Innovation & Transformation

Transform (Pilot)

Top Customer Challenge

Top Employee Challenge

Top Owner Challenge

First 90 Days of a Digital Data Governance Roadmap

Lynn Noel, Digital Heritage Consulting, 2020


Data Governance Guides Technical Platform Governance


Reference ModelsData Catalogs

Models: Where to Start When Data is Too Big to Know3.1 Data

Modeling Tools

Drawing

Rubber-banding

Forward-engineering

with DDL

Reverse engineering

Metadata

Sharing

3.2 Lineage Tools

Attribute source

structures

Impact analysis

Integration Tools

3.3 Data Profiling

Tools

Data exploration

Metadata validation

Data quality analysis

Data model validation

3.4 Metadata

Repositories

Stores descriptive

model

Sharing

Viewing and

navigation

3.5 Data Model

Patterns

Reusable

Elementary patterns

Assembly patterns

Integration patterns

3.6 Industry Data Models

Prebuilt for an industry

domain

Broad and detailed

Reference model for

customization

Data catalogs are “toolboxes” that include

varying bundles of these tools


The Data Experience Drives Digital Data Strategy

Manage, measure, and optimize customerdata to drive digital

transformation for an omnichannel experience

Manage, measure, and optimize the way

employees use data to deliver on the

enterprise mission

Manage, measure, and optimize data

governance and compliance for owners

as stewards of customer data

Tailor Each Roadmap Project to the Operating Model

Transform the Data Experience for Customers, Employees, and Owners


• “A business manifesto should be an essential tool in communicating the intentions and motives of the digital workplace, according to Gartner, Inc. Organizations can struggle with communicating and implementing broad policy changes like those generated by digital workplace initiatives. ” (Gartner 2015)

Stairway to Heaven: Gartner Calls for a Digital Manifesto

The Cloud

On-Premise Systems

Hybrid Integration

http://www.gartner.com/it-glossary/digital-workplace


A key wisdom: “The future masters of

technology will have to be light-hearted and intelligent. The

machine easily masters the grim and the dumb.”

Marshall McLuhan

Disruptive Innovation

Ca. 2000 A.D.

Original Manifesto for Agile Software Development


Manifesto for Digital Business

We are evolving better ways of doing business by being human organizations and helping these organizations work with machines. Through this work we have come to value:

• Business equally with Technology• Crowds equally with Core Leaders• Machine learning equally with Human Learning• Structured data equally with Unstructured content• Analysis equally with Synthesis• Configuration equally with Customization • Customers equally with Employees

That is, while future vision may be emerging from the items on the left, we work to respect and to balance with items on the right

during times of rapid organizational and societal change.

Lynn Noel, Digital Heritage Consulting, 2015


• Just a reminder, our chapter meets regularly, and we continue to update our schedule of events and Website content…visit often

• Chapter elections will be coming up – we invite you to join the chapter and get involved!

• If you are interested in joining our Chapter, please contact Karen Sheridan or any board member via our website or email.

Thank You For Attending!

If you are not on our mailing list, please email [email protected]. We’d love to stay in touch!


Q & A
