How to Achieve Self-Service Analytics with a Governed Data Services Layer (UK)
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The document discusses achieving self-service analytics through a governed data service layer, highlighting challenges faced by businesses in accessing and utilizing data effectively. It emphasizes the need for a balance between self-service and governance to ensure reliable and trustworthy analytics, while introducing a data catalog to help users navigate data availability and quality. Lastly, it suggests that data virtualization can support effective self-service initiatives by providing necessary governance and management infrastructure.
How to Achieve Self-Service Analytics with a Governed Data Services Layer (UK)
- 1. How to Achieve Self-Service Analytics with a Governed Data Service Layer (UK) Paul Moxon SVP Data Architectures & Chief Evangelist, Denodo 5th February 2020
- 2. Paul Moxon SVP Data Architectures & Chief Evangelist, Denodo Speakers
- 3. 1. Data Challenges 2. Self-Service Initiatives 3. Governed Self-Service 4. Demo 5. Q&A 6. Next Steps Agenda
- 5. 5 The Economist, May 2017 The world’s most valuable resource is no longer oil, but data.
- 6. 6 Data – Like Oil – Is Not Easy To Extract and Use
- 7. 7 The Data is Somewhere in Here…
- 8. 8 Business Dependency on IT to Deliver Data IT DepartmentBusiness “You’re too slow, too expensive, and never deliver what I want.” “You can’t make up your mind, keep adding features, and never see the big picture.” Casual User: “Just forget it.” Power User: “Just give me a data dump.” BU Leader: “We’ll do it ourselves.” “I’d rather be doing something else than taking your order.” “You’ll come crawling back to us soon.”
- 10. 10 • Let business users access the data that they need and stop IT being a bottleneck • That’s the vision as sold by many BI tool and Data Prep vendors • i.e. give me the tools and access to the data and stand back ☺ The Promise of Self-Service
- 11. 11 Self-Service to the Rescue! Not so fast! • No backlogs and waiting • Better tools • Faster time to insight IT Department • Happy users • Offload reports • Focus on data Business Users
- 12. 12 Challenges of Self-Service Initiatives • Too many reports • Duplicate reports • Conflicting data • Users don’t trust reports • Data extract hell NO STANDARDS OR GOVERNANCE A TOWER OF BABEL
- 14. 14 Needs of Different User Communities 60% of employees Data Consumers Data Explorers 30% of employees 8% of employees Data Analysts CASUAL USERS POWER USERS Data Scientists 2% of employees ‘WHITE GLOVE’ SERVICE SELF SERVICE TopDownBottomUp
- 15. 15 Casual Users • Want answers…not data • Pre-integrated, pre-calculated, curated results • Usually not technical users • *Might* understand SQL, etc. – but not always… • Want to use the mechanisms and tools that they are familiar with • e.g. Reports or dashboards • e.g. Excel (load data into Pivot Table) • Do not want to ‘wrangle’ the data to get the results • Do not make life difficult for them! Data Consumers Data Explorers
- 16. 16 Power Users • They want data! • Want to analyze data, look for patterns, similarities, etc. • Typically technical – most understand SQL and how to query data • Want to use existing tools to access data • BI tools (Tableau, Qlik, etc.) • Analytics tools and languages (Statistica, SAS, Python, etc.) • Don’t mind wrangling data, but don’t want this to be time consuming • Often have their own data sets they want to integrate Data Analysts Data Scientists
- 17. 17 The Challenge of Self-Service Governance Standards Architecture Operations Centralization Self-Service Speed Agility Innovation Decentralization Create a data- driven enterprise that balances dueling imperatives. Create a data-driven organization that balances dueling imperatives.
- 18. 18 Power User Sandboxes Data Virtualization – Governed Self-Service Systems of Record Data Lake Data Warehouse Common (or Core) Views Curated Views (Virtual Data Marts)Customer VIews Promotion Views
- 19. 19 Common (or Core) Data Views • Foundation of architecture to support casual and power users • Base views and derived views built on top of data sources • Hosted on ‘central’ Denodo server(s) • Owned and maintained by IT (CoE Team) • Available to all users as needed
- 20. 20 Curated Data View (Virtual Data Marts) • Views created for specific users or applications • ‘Fit for purpose’ curated data sets • Virtual Data Marts • Hosted on central Denodo server(s) • Owned and maintained by IT (CoE) • Available to all users as needed
- 21. 21 Power User Sandbox • Sandbox environment for a ‘power user’ • Local copy of Denodo running on desktop • Dedicated Virtual Database (VDB) running in shared Denodo sandbox server • Dedicated Virtual Database running on central Denodo server • Integrate local data with common or curated data from central Denodo server • Keeps ‘shadow’ data off central server • Provides guard rails for accessing common or curated views • IT can control what power users can and cannot do via Resource Manager policies or constraints in ‘self-service views’
- 22. 22 The Need for a Consumer Data Catalog • Data Virtualization Platform delivers (a lot of) data to users • How do users know what data is available? • What is the schema of the data? • Where did the data come from? (Lineage) • Is it an ‘approved’ data set? • Can they get a sample of the data? • Can they select only some of the data? (rows and columns)
- 23. 23 Data Catalog with Data Access
- 24. Demo
- 25. 25 What We Will Do in the Demo 1. Customer Birthday Cards Project • As a ‘casual user’ (Joe Smith), we’ll browse the data catalog to find the customer information that we need • Use Excel to get the data for the customer (name, address, DoB) to send them a birthday card on their birthday 2. Customer Sentiment by Household Demographic Project • As a ‘power user’ (Mary Weaver), browse the data catalog to find the customer data that we can use for our sentiment analytics • Access the customer data from the ‘corporate’ Denodo Server using our Denodo ‘sandbox’ environment • Combine this with our local Twitter data (CSV file) to perform sentiment analysis
- 26. Summary
- 27. 27 Key Takeaways FIRST Takeaway Self-service analytics can unleash the power and creativity of your users SECOND Takeaway Ungoverned self-service is a recipe for chaos and untrusted analytics THIRD Takeaway Data Virtualization provides a governance and management infrastructure necessary for successful self-service initiatives FOURTH Takeaway Coupled with a ‘consumer’ data catalog, Data Virtualization enables ‘self-service with guard rails’
- 28. Q&A
- 29. 29 Next Steps Access Denodo Platform in the Cloud! Take a Test Drive today! www.denodo.com/TestDrive GET STARTED TODAY
- 30. Thanks! www.denodo.com info@denodo.com © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.