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DigitalEventExperience
IBM DataOps POV—Julie LocknerDirector, Portfolio Optimization and Offering ManagementData and AI
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
The AI LadderA prescriptive approach to accelerating the journey to AI
COLLECT - Make data simple and accessible
ORGANIZE - Create a business-ready analytics foundation
ANALYZE - Build and scale AI with trust and transparency
INFUSE - Operationalize AI throughout the business
AI
MODERNIZEMake your data ready for anAI and hybrid cloud world
One Platform, Any CloudTalent &
Skills
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
Use your data3
Trust your data
Know your dataDataOps delivers business-ready data fast
ORGANIZE
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
COLLECT ANALYZEData Prep for self service and
testing
Data Governance and Curation
Data Quality
Master Data Management
Data Integration
Data Replication
Data Virtualization
Know
Catalog & Metadata Management
Trust Use
ORGANIZE: Critical information architecture capabilities
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
5
Prepare Data Pipelines
Data OperationsDiscover, understand, ingest,integrate, assess quality, clean data
Build Run Manage
Months - Quarters
Need: Business users need access to high quality data fast. Issue: Data pipelines are the primary source of bottlenecks.
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
6
12%13%
15%15%
17%17%18%19%19%
22%
23%32%
Issues with tools slowing down compl iance and governance processesIssues with the ability to report or communicate the value of data/information integration & governance to executive
Issues with executives seeing value in data/information integration & governanceDistributed technology/systems
Lack of up-to-date tools/technologies to address data/information integration & governance needs
Current tools too technical and do not serve business usersCosts of managing and storing data
Lack of skills and resources internally to address data/information integration & governance needsDistributed personnel/teams
Inconsistent data quality and/or lack of data quality metricsProcesses are too manual
Volume and increasing number of regulations we need to comply with
Top Challenges With Current Data/Information Integration & Governance Capabilities(N=209)
Source: IBM Offering Management ResearchQ. Please indicate how challenging (if at all) each of the following is for you/your team with your current data and information integration and governance capabilities.
Recent Survey Results - Top Challenges
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
Introducing DataOps
“DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.”
Gartner
7
13
Data Prep for self service and
testing
Data Governance and Curation
Data Quality
Master Data Management
Data Integration
Data Replication
Data Virtualization
Know
Catalog & Metadata Management
Trust Use
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
8
Prepare Build Run Manage
Months - Quarters
Hours - Days
DataOps Consistently Delivers High Quality Data Fast
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
DataOps Impact – Know Your Data in Minutes Data Inventory Case Study
Reduction in business glossary creation time
Number of technical assets across multiple clouds discovered in less than 5 mins
85% 200,000
Financial Services, Retail Examples, Healthcare Payer
2 HourROI
Uncovered Protected Health InformationPHI / PII exposure
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
DataOps Impact - Trust Your Data
Data Quality Case Study International Bank
13Per hour (manual)
50Per min (automated)
Data records update speed
6% 93%Data quality score
2 yearsNet promoter score
230x
With DataOps
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
With DataOps
With DataOps
DataOps Impact – Use Your DataData Integration Use Case Leading European Retailer
Customer affinity analysis
Inventory stock positions
>3 weeks
< 2 minutes
Data change delay on reporting systems
20 days
< 1 day
~24 hours
< 4 hours
DataOps Impact
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
DataOps Maturity Model
• Know: Spreadsheets• Trust: Emails• Use: Hand coding
• Know: Departmental / LOB Catalog• Trust: Data Quality Program• Use: Data Virtualization, Data Integration and Data Replication
• Know: Enterprise Catalog• Trust: Data Governance Program with Data Stewardship and Business
Glossary• Use: Self Service Data Prep and Test Data Management
• Know: Enforced and Enriched Catalog• Trust: Compliance, Business Ontology and
Automated Classification• Use: DataOps for All Data Pipelines
Advanced DataOps
Developed DataOps
FoundationalDataOps
No DataOps
Increased business valueand speed in Delivering business-ready data.
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
DataOps Interoperates with Peer Organizations
DataOps Team- Data engineering and DevOps skills- Member of the Chief Data Office
- Interlocks with
§ Application development
§ IT security and compliance
§ Data science
§ Lines-of-business
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
DataOps
DataOps Methodology Automates Data Management Best Practices and Processes
DataOps Methodology…
— Prioritizes data pipelines with business value
— Leverages Data Engineering and DevOps skills to design Data Flows
— Instruments and measures key DataOps KPIs
— Automates
§ data and metadata ingestion
§ data classification
§ data quality assessment and remediation
§ test data management
§ enforcement of privacy and governance policiesInventory and
categorize dataPublish data and use
Deliver quality and governance
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
IBM DataOps Technology Automates Data Pipelines
Auto-discovery
Auto-classifier
Auto-detectsensitive
Auto-analyzedata quality
Auto-assign business
Infused machine learning Knowledge catalogEnterprise IT
Systems of record
Data lakes and DW
Public cloud
Private cloud
Data Consumers
Dashboard and KPIs - CDO,CXO
Find, prepare and shape- Data scientist
Build and train models- Data scientist
Find, refine and analyze- Business users
Prepare, test, and iterate- Application developer- Application tester
Data governanceobjectives
Data qualityobjectives
Governance, risk and compliance SMEs
Data qualitySMEs
Data curationobjectives
Data curatorsSMEs
Infused industry knowledge
Data integration and data movement, entity managementData engineers
15IBM DataOps / © 2020 IBM Corporation
DataOps Workflow and Collaboration
Analyze & InfusePlug and play 45+ data, analytics and AI apps.
Manage your favorite open source capabilities along side IBM’s market leading differentiators.
Organize Automate DataOps to deliver business-ready data fast.
Catalog and govern all enterprise data, models, rules, and insights through a common experience
OpenShiftLeverage the leading open source hybrid cloud platform to SCALE data & AI workloads.
CollectVirtually connect, manage and query data & AI
assets no matter where they live.
Run on ANY CloudDecoupling enterprise data, analytics and AI will
prevent lock in and accelerate polyglot eco-systems.
IBM Cloud Pak for DataSimplifies, unifies and automates the AI Ladder
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
Comparing the two scenarios. Which one is yours?
17
Single iterationMonths-QuartersOne outcome, costly if wrong
Multiple iterationsDays-WeeksMultiple outcomes, more chances for success
Without DataOps With DataOps
80%Data Prep
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
Getting Started
1. Assess Your Team’s DataOps Maturity
– IBM DataOps Center of Excellence
2. Determine Business Priorities and Align Value to Data Pipelines
– DataOps Garage Design Thinking Workshop
3. Kick off Your 1st DataOps Pilot Project
– DataOps Garage MVP Build Workshop on Cloud Pak for Data
Use your data Know your data
Trust your data
Think 2020 / DOC ID / May, 2020 / © 2020 IBM Corporation
Notices and disclaimers
19 Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation
© 2020 International Business Machines Corporation. No part of this document may be reproduced or transmitted in any form without written permission from IBM.
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Notices and disclaimerscontinued
20 Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation
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