Self Service Data Analytics And Governance For Managers
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Self-Service Data Analytics and Governance for Managers
Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands.
The Self-Service Data Roadmap
Author: Sandeep Uttamchandani
language: en
Publisher: O'Reilly Media
Release Date: 2020-09-10
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can’t scale data science teams fast enough to keep up with the growing amounts of data to transform. What’s the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work. Build a self-service portal to support data discovery, quality, lineage, and governance Select the best approach for each self-service capability using open source cloud technologies Tailor self-service for the people, processes, and technology maturity of your data platform Implement capabilities to democratize data and reduce time to insight Scale your self-service portal to support a large number of users within your organization
Self-Service Analytics Simplified
Author: Arshad Khan
language: en
Publisher: Khan Consulting & Publishing LLC
Release Date: 2019-07-10
Self-Service Analytics Simplified: How to Plan and Implement will introduce you to self-service analytics (SSA), which aims to make business users less dependent on IT for their reporting and analytics needs. This book, which teaches how to plan and implement an SSA project, will appeal to a broad range of users including senior executives, business and IT managers, project managers, data analysts, business analysts, developers, casual users, as well as IT professionals. The topics covered in Self-Service Analytics Simplified: How to Plan and Implement include an introduction to self-service analytics, relationship with BI, benefits for different types of users, readiness assessment, planning, data-related topics including metadata and data pipelining, architecture, tools, requirements, implementation, data governance, security, training, data and user onboarding, and barriers to adoption, as well as challenges, best practices, lessons, and tips.