Slides: Metadata Management for the Governance Minded

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This document discusses how metadata management automation can help with data governance efforts. It notes that data governance depends on quality metadata and that automation can help with metadata collection, analysis, and delivery processes. The document outlines what to look for in a metadata management tool, how such a tool can increase metadata usage, and the various roles that would benefit from improved metadata automation, such as data stewards, users, and managers.

Daten & Analysen

www.octopai.com
Metadata Management Automation
for the Governance Minded
Introductions
Robert S. (Bob) Seiner
KIK Consulting & Educational Services (KIKconsulting.com)
The Data Administration Newsletter (TDAN.com)
Non-Invasive Data Governance & Non-Invasive Metadata Governance
Gal Alon
Octopai
Senior Director of Business Development

www.octopai.com
Metadata Management Automation
for the Governance Minded
Topics for Today!
● Data Governance’s dependency on quality metadata
● Automation of metadata collection and management processes
● What to look for in a metadata management tool and how it will help
● How a great tool will lead to increased use of your metadata assets
● People that will benefit from improved metadata automation and delivery

www.octopai.com
Metadata Management Automation
for the Governance Minded
Data Governance’s dependency on quality metadata
● If you plan on being non-invasive, automation is important
● Manual tasks require formal accountability
● Data understanding, usage and value improve with quality metadata
● The metadata will not govern itself

www.octopai.com
Metadata Management Automation
for the Governance Minded
DG
depends
on
metadata

www.octopai.com
Where Metadata Management fits into DG
Source: www.dataversity.net

● Extract and Centralize all data layers from across your environment
○ Reporting - Physical layer + Semantic layer + Presentation layer
○ Cross systems visibility - even if they are from different vendors
(e.g. Tableau, Power BI, Informatica, Oracle, SSIS etc.)
○ Include Logical and Calculated data items
● And…make sense of it
● And…inject analyzed metadata into your governance tool
How to take Data Governance
to the Next Level with Automation
www.octopai.com

One source of the truth for the business
● Eliminate futile tasks, focus on core value of governance projects
● Save time and money (replace manual with automation)
● Increase accuracy with continuous, up-to-data metadata
● Common business language
● Improve self-service BI
● Increase company-wide consistency & data integrity
What happens when you automate
Metadata Management?
www.octopai.com

www.octopai.com
Metadata Management Automation
for the Governance Minded
What to look for in a metadata management tool and how it will help
● Effective metadata definition, production, usage.
● Efficient metadata analysis and navigation.
● Automation to enhance metadata collection.
● Metadata presentation and delivery.

www.octopai.com
Metadata Management Automation
for the Governance Minded
Automation of metadata collection and management processes
● Database Sources
● ETL Tools
● Data Warehouse and MDM Platform and Tools
● Reporting and Analysis Tools

www.octopai.com
Metadata Management Automation
for the Governance Minded
How a great tool will lead to increased use of your metadata assets
● Resources for consistent tool management.
● Automation decreases resource requirement.
● Automation results in accurate metadata collection.
● Automated Metadata Analysis results in improved metadata consumption.

DB Sources
Marketing
CRM
ERP
Finance
HR
Other Sources
ETL Tools
Informatica
DataStage (IBM)
SSIS (MS)
Others
(ODI, Talend, etc.)
Data Warehouse
Oracle
Others
(Hadoop, etc.)
SQL Server(MS)
Teradata
Vertica HP
Reporting & Analysis
Tools
Cognos (IBM)
BO (SAP)
Power BI
Tableau
OBIEE (Oracle)
Others (Qlik, etc)
SSAS-OLAP,
TABULAR
Complete data lineage to and from reports
OCTOPAI AUTOMATED METADATA MANAGEMENT

Boosting Data Governance with automated
metadata management
DG + BI teams are using Octopai to locate their data instantly, and toquickly and accurately
visualize and understand the entire data journey for various use cases:
www.octopai.com
Data Governance Business Intelligence Operations
Data
Catalog
Lineage
for Audit
Data
Discovery
Automated
Business
Glossary

www.octopai.com
Metadata Management Automation
for the Governance Minded
People that will benefit from improved metadata automation and delivery
● Data Definers – Integration and Project Management
● Data Stewards – Data Producers and Users
● Data Users – Business and Technical Communities
● Data Managers – Owners and Subject Matter Experts

www.octopai.com
Metadata Management Automation
for the Governance Minded
Thank You for Attending!

Get in touch for a free trial!
gala@octopai.com
www.octopai.com

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Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

Data Catalogs Are the Answer – What is the Question?

DATAVERSITY

Data Catalogs Are the Answer – What Is the Question?

DATAVERSITY

Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Data Modeling Fundamentals

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Showing ROI for Your Analytic Project

DATAVERSITY

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

How a Semantic Layer Makes Data Mesh Work at Scale

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

DATAVERSITY

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends - A Look Backwards and Forwards

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

2023 Trends in Enterprise Analytics

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

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Data at the Speed of Business with Data Mastering and Governance

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Data Catalogs Are the Answer – What Is the Question?

Data Modeling Fundamentals

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Slides: Metadata Management for the Governance Minded

2. www.octopai.com Metadata Management Automation for the Governance Minded Introductions Robert S. (Bob) Seiner KIK Consulting & Educational Services (KIKconsulting.com) The Data Administration Newsletter (TDAN.com) Non-Invasive Data Governance & Non-Invasive Metadata Governance Gal Alon Octopai Senior Director of Business Development

3. www.octopai.com Metadata Management Automation for the Governance Minded Topics for Today! ● Data Governance’s dependency on quality metadata ● Automation of metadata collection and management processes ● What to look for in a metadata management tool and how it will help ● How a great tool will lead to increased use of your metadata assets ● People that will benefit from improved metadata automation and delivery

4. www.octopai.com Metadata Management Automation for the Governance Minded Data Governance’s dependency on quality metadata ● If you plan on being non-invasive, automation is important ● Manual tasks require formal accountability ● Data understanding, usage and value improve with quality metadata ● The metadata will not govern itself

5. www.octopai.com Metadata Management Automation for the Governance Minded DG depends on metadata

6. www.octopai.com Where Metadata Management fits into DG Source: www.dataversity.net

7. ● Extract and Centralize all data layers from across your environment ○ Reporting - Physical layer + Semantic layer + Presentation layer ○ Cross systems visibility - even if they are from different vendors (e.g. Tableau, Power BI, Informatica, Oracle, SSIS etc.) ○ Include Logical and Calculated data items ● And…make sense of it ● And…inject analyzed metadata into your governance tool How to take Data Governance to the Next Level with Automation www.octopai.com

8. One source of the truth for the business ● Eliminate futile tasks, focus on core value of governance projects ● Save time and money (replace manual with automation) ● Increase accuracy with continuous, up-to-data metadata ● Common business language ● Improve self-service BI ● Increase company-wide consistency & data integrity What happens when you automate Metadata Management? www.octopai.com

9. www.octopai.com Metadata Management Automation for the Governance Minded What to look for in a metadata management tool and how it will help ● Effective metadata definition, production, usage. ● Efficient metadata analysis and navigation. ● Automation to enhance metadata collection. ● Metadata presentation and delivery.

10. www.octopai.com Metadata Management Automation for the Governance Minded Automation of metadata collection and management processes ● Database Sources ● ETL Tools ● Data Warehouse and MDM Platform and Tools ● Reporting and Analysis Tools

11. www.octopai.com Metadata Management Automation for the Governance Minded How a great tool will lead to increased use of your metadata assets ● Resources for consistent tool management. ● Automation decreases resource requirement. ● Automation results in accurate metadata collection. ● Automated Metadata Analysis results in improved metadata consumption.

12. DB Sources Marketing CRM ERP Finance HR Other Sources ETL Tools Informatica DataStage (IBM) SSIS (MS) Others (ODI, Talend, etc.) Data Warehouse Oracle Others (Hadoop, etc.) SQL Server(MS) Teradata Vertica HP Reporting & Analysis Tools Cognos (IBM) BO (SAP) Power BI Tableau OBIEE (Oracle) Others (Qlik, etc) SSAS-OLAP, TABULAR Complete data lineage to and from reports OCTOPAI AUTOMATED METADATA MANAGEMENT

13. Boosting Data Governance with automated metadata management DG + BI teams are using Octopai to locate their data instantly, and toquickly and accurately visualize and understand the entire data journey for various use cases: www.octopai.com Data Governance Business Intelligence Operations Data Catalog Lineage for Audit Data Discovery Automated Business Glossary

14. www.octopai.com Metadata Management Automation for the Governance Minded People that will benefit from improved metadata automation and delivery ● Data Definers – Integration and Project Management ● Data Stewards – Data Producers and Users ● Data Users – Business and Technical Communities ● Data Managers – Owners and Subject Matter Experts

15. www.octopai.com Metadata Management Automation for the Governance Minded Thank You for Attending!

16. Get in touch for a free trial! gala@octopai.com www.octopai.com

Slides: Metadata Management for the Governance Minded

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