Event Director, Portfolio Optimization and Offering ... · cloud platform to SCALE data & AI...

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


Use your data3

Trust your data

Know your dataDataOps delivers business-ready data fast

ORGANIZE


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


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.


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


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


8

Prepare Build Run Manage

Months - Quarters

Hours - Days

DataOps Consistently Delivers High Quality Data Fast


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


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


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


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.


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


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


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


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


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


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.

U.S. Government Users Restricted Rights — use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM.

This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates.

Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. This document is distributed “as is” without any warranty, either express or implied. In no event, shall IBM be liable for any damage arising from the use of this information, including but not limited to, loss of data, business interruption, loss of profit or loss of opportunity. IBM products and services are warranted per the terms and conditions of the agreements under which they are provided. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions.

IBM products are manufactured from new parts or new and used parts. In some cases, a product may not be new and may have been previously installed. Regardless, our warranty terms apply.”

Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice.

Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary.

References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in which IBM operates or does business.

Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or their specific situation.

Notices and disclaimerscontinued

20 Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation

It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer follows any law.

Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products about this publication and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products.Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. IBM does not warrant the quality of any third-party products, or the ability of any such third-party products to interoperate with IBM’s products. IBM expressly disclaims all warranties, expressed or implied, including but not limited to, the implied warranties of merchantability and fitness for a purpose.

The provision of the information contained herein is not intended to, and does not, grant any right or license under any IBM patents, copyrights, trademarks or other intellectual property right.

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