This document summarizes a presentation on data governance and stewardship. It discusses how poor data quality impacts 40% of business initiatives and defines governance and stewardship. It then outlines a 3-step process to implement governance and stewardship: 1) Assess the current state of data usage and quality, 2) Develop a plan with sections on definitions, standards, roles, and security, and 3) Implement the plan across systems, processes, and people while learning and adapting. Tools are recommended to assist with data entry and track progress.
Xiomi's applied very basic supply chain concepts which makes it's business model competitive in highly competitive mobile handset market. PPT explains how xiaomi able to diminish its operating cost and sell products just above margins.
Automated Testing For Protecting Data Pipelines from Undocumented AssumptionsDatabricks
Untested, undocumented assumptions about data in data pipelines create risk, waste time and erode trust in data products. Automated testing has been one of the biggest productivity boosters in modern software development and essential for managing complex codebases.
Supply Chains to Admire Analysis 2022_2022 presentation.pptxLora Cecere
Supply Chains to Admire is a data-driven analysis based on public reporting of manufacturing and retail companies. The research evaluates which public companies drove improvement while outperforming their peer groups on performance metrics and value for the ten-year period of 2012-2021. The 25 winners are a testimonial to supply chain resilience.
My presentation at Software Architect Indonesia Community (SARCCOM) Tech Talk hosted by Bank Central Asia on October 11th, 2017. Topic is about big data at Bukalapak, one of the largest e-marketplace in Southeast Asia.
Xiomi's applied very basic supply chain concepts which makes it's business model competitive in highly competitive mobile handset market. PPT explains how xiaomi able to diminish its operating cost and sell products just above margins.
Automated Testing For Protecting Data Pipelines from Undocumented AssumptionsDatabricks
Untested, undocumented assumptions about data in data pipelines create risk, waste time and erode trust in data products. Automated testing has been one of the biggest productivity boosters in modern software development and essential for managing complex codebases.
Supply Chains to Admire Analysis 2022_2022 presentation.pptxLora Cecere
Supply Chains to Admire is a data-driven analysis based on public reporting of manufacturing and retail companies. The research evaluates which public companies drove improvement while outperforming their peer groups on performance metrics and value for the ten-year period of 2012-2021. The 25 winners are a testimonial to supply chain resilience.
My presentation at Software Architect Indonesia Community (SARCCOM) Tech Talk hosted by Bank Central Asia on October 11th, 2017. Topic is about big data at Bukalapak, one of the largest e-marketplace in Southeast Asia.
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring.
Learning Objectives:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Case Studies illustrating data quality success
Data quality guiding principles & best practices
Steps for improving data quality at your organization
From Compliance to Customer 360: Winning with Data Quality & Data GovernancePrecisely
Winning football teams will dominate opponents both defensively and offensively. Similarly, the most successful businesses will best utilize enterprise data for effective “defense” (e.g., compliance, such as GDPR and CCAR) as well as “offense” (increased customer engagement and revenue).
View our on-demand webcast and discover how integrated data quality and data governance tools help you confidently achieve regulatory compliance, as well as revenue-building initiatives requiring a 360-degree view of your customers.
Data management experts Ian Rowlands, Product Marketing Manager of ASG and Harald Smith, Director, Product Management of Trillium Software discusses how Trillium Software for data quality, integrated with ASG’s Enterprise Data Intelligence solution, helps you pinpoint where data quality impacts your business, ensuring your enterprise data can be trusted to drive regulatory compliance as well as better business decisions.
View this on-demand webcast to learn how to:
• Improve data quality by leveraging data lineage maps
• Gain insight into where data quality gaps may exist, which may impact regulatory compliance and customer engagement initiatives
• Understand how changes may affect critical data elements and data quality
Data Governance: From speed dating to lifelong partnershipPrecisely
Data Governance, like a relationship, requires a strong foundation and commitment to make it work. This presentation explores the main reasons why Data Governance initiatives fail and the key components that are necessary to build a sustainable program with long-term engagement within your organisation. Learn about the Data Integrity Framework, data governance tools, and how to establish a structured decision tree to drive prioritisation.
Data & The Machine Summit on self-service analytics about the application of data and machine learning across the enterprise.
58% of Fortune 500 companies report conflict between data scientists and data security & compliance teams due to access restrictions. Learn more about how complying with privacy regulations in cloud environments, and how data governance is transforming from dictated to decentralized to delegated.
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
Expert Strategies to Enhance Data Quality With Data Cleansing ServicesAndrew Leo
Explore proven strategies for improving data quality through effective data cleansing techniques. Enhance decision-making, streamline operations, and gain a competitive edge in the digital age. Delve into insightful blogs on optimizing data quality, discover practical tips, industry best practices, and innovative approaches to elevate your data management processes. Empower your business with reliable insights and stay ahead of the curve with our expert guidance.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Integrity: From speed dating to lifelong partnershipPrecisely
Governance has little to do with governance…it’s about delivering and demonstrating value. It’s one thing for your colleagues to intellectually believe in the value of data, good data, and governed data, but it’s another thing entirely to have them emotionally engaged and excited to be involved. In this presentation from the CDO Sit-Down series, Shaun Connolly, Vice President of International Strategic Services, shares his thoughts and experience on approaches to win over reluctant leaders and business teams and describe the key components of successful programs.
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
A Business-first Approach to Building Data Governance ProgramPrecisely
Traditional data governance programs struggle to make the connection between critical policies and processes and its impact on business value and results. This leaves data management and governance practitioners having to continually make the case for data governance to secure business adoption.
Watch this on-demand webinar to learn about the proven methods to identify the data that matters, connect governance policies to business objectives, and quickly deliver value through the life of the program.
Why there are so many problems with streamlining data strategy ? What are the major problems ? How do you solve them ?
Using an approach based on Agile and Lean Concepts to achieve the goal of actionable data & analytics
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Introduction to Data Governance and Stewardship by Brett Stineman
1. Introduction to Data Governance and Stewardship
Salesforce World Tour, Boston
April 2016
Brett Stineman, Product Marketing Dir., Data.com
bstineman@salesforce.com
@jbstineman
2. Why is data important?
40% of business initiatives
due to poor
data quality
Source: Gartner, Inc.
3. Why is data important?
40% of business initiatives
due to poor
data quality
Source: Gartner, Inc.
5. 3 Steps to Governance & Stewardship
Assess your
current state1.
Develop
your plan2. Implement
your plan3.
6. QUANTITATIVE
QUALITATIVE
+
Assess your current state
Who is using the data?
What is the data quality?
How is data being created, changed?
How is the data being used?
Which data is being used?
What are the business needs?
7. Develop your plan
SECTIONS:
Data Definitions
Quality Standards
Roles and Ownership
Security and Permissions
Quality Control
Remember the 80/20 rule
Focus on the data that has the
highest business value
Expand the plan over time
8. Implement your plan
SYSTEMS
PROCESSESPEOPLE
Share stewardship
across depts, roles
Tools to assist with
data entry
Keep them flexible
Learn and adapt
Automate data enrichment
Track progress
9. • Data health ratings
• Field level details
• Match rate and where
to improve
Visit appexchange.salesforce.com search: Data.com
11. Upcoming Chicago Community Events
Presented by Salesforce Community Leaders
Midwest Dreamin’
July 21st & 22nd at Navy Pier
Call for Presenters is OPEN! Closes 4/1/16
Sponsorship Opportunities Available, spaces are limited!
To learn more go to: MidwestDreamin.com or
@Midwest_Dreamin
Trailhead on the Road
May 7 – 9 am to 2 pm
Illinois Technology Association
20 N. Upper Wacker Dr. #1200
Registration Opens SOON!
Free to attend, space is limited
Editor's Notes
Remember the 80/20 rule:
Focus your plan on the data that has the highest business value
Expand the plan over time
Don’t be part of the 2/3 of companies that lack a coherent approach to data quality. (Experian)
Data health ratings provide an overall assessment of CRM data quality
Field level details show where there are data gaps that need to be addressed
Match rate and firmographic details show how Data.com can improve customer data
GDoc resources for Assessment App:
https://docs.google.com/a/salesforce.com/document/d/1vMAgZwdtbgrOX1ECbqLZMHh6nhRyenUF5bsfddTMA1Q/