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Data Science Best Practices




1. Introduction

2. The Four Pillars of Success        in Analytics

3. Analytics Sophistication

4. Data Science Practice

4.1. DS-BuDAI® Methodology

4.2. Business Understanding/Use (Business focused)

4.3. Data Understanding/ Use (Data focused)

4.4. Analysis and Assessment (Analytics focused)

4.5. Implementation (Operation-focused)

1. Introduction 

In today’s interconnected world, an enormous amount of data is being continuously generated at a rapid pace thus compelling organizations to deploy analytics methodologies to gain faster business insights to steadily innovate, flourish and grow their businesses. According to Gartner, the global BI and analytics market would grow to $22.8 billion by 2020. Today, most enterprises are striving to find new ways, tools and platforms to derive value from their business data to drive productivity, enhance existing revenue and to solve some business problems like customer satisfaction, churn, etc. However, many organizations are still struggling to derive significant ROIs. The winning formula to achieve success with analytics lies in an organization’s ability to identify specific business use   cases/ problems and formulate a strong implementation strategy to achieve those goals.

2.  The Four Pillars of Success in Analytics

In today’s digital age, a successful organization has to achieve data and analytics competencies to remain competitive and relevant to the marketplace; i.e. it must be data and analytics driven. By leveraging detailed relevant data at its disposal and applying analytics over it, an organization can position itself ahead of disruptions in its industry. These competencies enable organizations to augment its competitive positioning in the marketplace.

This pyramid illustrates succession of steps to go from Raw Data to Intelligent Decisions and how to achieve such analytics maturity. Organizations can optimize their decisions based on specific analysis performed on the data and dependent on the specific business use case. In classic organizations, these competencies will take time to evolve and mature. An organization should first commit to the importance of making analytics-driven decisions. This will require a clear technical roadmap and gradual alignment in culture and organizational structure. 


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