This document discusses governed self-service analytics and proposes a blended approach that combines centralized IT-led analytics with decentralized business-led analytics. It advocates developing an organizational framework that allows businesses more autonomy through self-service tools while still maintaining governance and using centralized data assets. The key aspects of the blended approach include extending global data models to support local needs, incorporating new data sources into certified global datasets, and providing support and training to empower business users.

1. Governed Self-service Analytics
Presenter: Frank Silva
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2. Predictive and prescriptive analytics, search, embedded analytics, collaboration, self-service data preparation,
big data, data lakes, search-based and visual-based data discovery, data Visualization, predictive modeling, data
mining, statistical modeling, business intelligence, data warehousing, smart data preparation, reporting, dash
boarding, storyboarding, threaded discussions, annotations, automated pattern detection, embedded advanced
analytics, search-based natural-language query generation
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3. What is Self-service Analytics?
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4. Centralized, IT-centric
Centralized, economies of scale, governance, standards, best practices,
consistent data, enterprise-wide, certified data, performance
De-centralized, Business-centric
Shorter time to insight (speed), flexibility, freedom, Local needs
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5. Centralized, IT-centric
Centralized, economies of scale, governance, standards, best practices,
consistent data, enterprise-wide, certified data, performance
De-centralized, Business-centric
Shorter time to insight (speed), flexibility, freedom, Local needs
(A blended approach)
Develop organizational, architectural, and
technological framework that combines these two
models in to a coherent whole.
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6. Self-service Analytics – Does it work well?
• Good approach as far as business ownership, high demand, shorter time to insight (speed), flexibility, freedom, control,
and local needs are concerned.
• Although tools have become easier to use, it is still not easy to create a self-service environment.
• Self service can backfire if users fine the tools too complex. Most users settle down with just basic functionality of the
tools.
• Conversely, too little functionality creates the opposite backlash – users find tools too limiting and stop using them.
• Self-service analytics requires a lot of hand-holding. Not all power users are skilled enough to perform data blending,
modeling and perform data validation.
• Many users don’t have time, patience, or skill to develop reports, dashboards and stories, create metrics, dimensions,
hierarchies, engaged in threaded discussions.
• Many require one-on-one training and more importantly time to master BI tools.
• Inability to develop ‘certified data’ – data that has been profiled, cleansed, transformed, and optimized for performance.
• Failure in imposing governance (data, process and tools) and best practices across organization.
• Cross-functional or enterprise reporting is impossible (conformed dimensions and facts, drill-across, organizational KPIs)
• Lack of central Administration (Licensing, scaling, installations, security, support, training) 6

7. Comprehensive strategy
to
develop organizational, architectural, and
technological framework that combines
these two models in to a coherent whole.
A blended approach
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8. • Make data warehouse solutions fast to deploy and easy to manage through agile methods.
• Use incremental agile approach for building EDW
• Use best practices in data warehousing - star schema, data profiling, cleansing, transformations,
blending, loading, optimizing for performance
• Global data models, conformed dimensions and facts certified for reporting.
• Build a data dictionary
• Provide support for users
• Continues improvements
• Extend functionality of BI components (SDK, APIs, mashup)
• Two-way communication with users
Centralized/IT Approach
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9. • Business Analysts or power users (carefully select the right candidates – capable for data preparation as
well as developing analytical components for business).
• Extend global models to support unique and localized requirements.
• Edit existing global models and augment or blend with new data from local files or remote source.
• Use BI tool’s build-in data preparation features to profile, format and model
• Develop reports, dashboards, storyboards and engage in threaded discussions
• Work with extract mode for sources that are not in global model.
• Minimize working with uncertified sources.
• Casual users consume the reports and also share views with others.
• Casual users can become analysts and power users. They can rely on power users to help them make the
transition.
De-centralized/Business Approach
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10. Centralized/IT
Enterprise Data Warehouse (EDW), global models, certified data, enterprise needs
De-centralized/Business
Shorter time to insight (speed), flexibility, freedom, local needs
Data warehousing best
practices.
Global data models,
conformed dimensions
and facts. Metadata.
Incorporate the new
sources and data into
the global model
making the capabilities
universally available.
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11. • Strategy formulation, business alignment and adoption strategy. Effective
strategy should ensure that enterprise objectives, business strategy,
investments, and analytics strategy are aligned.
• Analytics program management – technology, tools, processes and people.
Decide on what is best for your organization. Think Big, Act Small!!!
• Governance, data stewardship, standards, best practices, security architecture,
project methodology. Stick to basics.
• Best practices and process for incorporate the new sources and data into the
global model making the capabilities universally available.
• Be collaborative - User forums, discussions, lunch and learn sessions, Analytics
portal. Listen to voice of customer.
• Coaching and training of business users on effective use of self-service
analytics tools.
• Benchmark your analytics environment for continues improvement.
Blend the two extremes with
Analytics Center of Excellence
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12. Use of Hadoop in a Blended Analytics Model
Staging
Global Model
Certified Data
Incorporate the high value
data into the global model
making the capabilities
universally available.
Volume
Velocity
Variety
Veracity
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Landing
Exploration
Data in
original form

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