Value of Analytics is in Consumption – Operationalize it


Many large organizations today have embarked on a journey of Digital Transformation. One of the key tenets within it is to adopt and drive Analytics and Insights based Decisions across the Enterprise. Naturally, we’ve seen companies investing a great deal of time and money to make this a reality. In 2019 alone, IDC estimates that organizations invested $189.1 billion in analytics. By 2022, they predict spending will increase to $274.3 billion.

However, despite such significant investments:
• Less than half of the models that are developed get deployed.
• 90% of models take more than three months to deploy.
• Nearly 50% take over seven months to be put into production.

Deployment, the “last mile” of analytics, remains a formidable challenge. Having said that some of the world’s leading technology firms that specialize in Analytics and Decisioning are investing heavily in their R&D to build unified platforms that will manage the complete Analytics Life Cycle. Coupled with synergizing Data Access & Data Governance Technologies and related business processes and workflows, these companies are helping address exactly the challenge stated above. So today, friends, I will primarily talk about the “Last Mile” challenge and also discuss how SAS has been helping address this problem, for the developer and data science community at large, both Open Source and SAS. I will walk you through the full Analytics Life Cycle (build | compare | publish | govern | consume), and time permitting showcase a small demonstration of the same.


Saptarishi is Sr. Analytics Consultant, India, supporting the SAS Platform product lines. In his current role, Saptarishi works with customers to collaborate with multiple stakeholders to ideate and build product prototypes for various business demands which can become a revenue potential. He has worked with customers in Financial Services, Insurance, and Manufacturing.
Saptarishi is passionate about discussion and designing solutions in the area of Machine Learning, Deep Learning, and Data warehousing.

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