Analytics Best Practices: A Business-driven Playbook for Creating Value through Data Analytics

Deliver enterprise data analytics success by following Prashanth's prescriptive and practical techniques.

Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale.

So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are:

• Practicality. This book offers prescriptive, superior, and practical guidance.
• Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization.
• Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies.

Dr. Southekal proves why he is one of the leading thinkers on data and analytics today. 'Analytics Best Practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data.

Douglas B. LaneyPrincipal, Data & Analytics Strategy, Caserta, and author of "Infonomics"

Author: Prashanth H. Southekal
Publisher: Technics Publications
Published: 04/22/2020
Pages: 208
Binding Type: Paperback
Weight: 0.81lbs
Size: 9.25h x 7.52w x 0.44d
ISBN13: 9781634628273
ISBN10: 1634628276
BISAC Categories:
- Computers | Data Science | Data Analytics
- Computers | Data Science | Machine Learning
- Computers | Data Science | Data Warehousing

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