Analytics Life Cycle: Orchestrating Data, Discovery and Deployment

Abstract: 

Analytics is booming. Today there are hundreds of firms in the analytics ecosystem - providing multiple sets of technology and services to help organizations store, access, analyze and present data.

All these options can make it difficult for organizations, regardless of their maturity to find a path that ensure a return on their investments. It’s chaotic and challenging to tie all the disparate pieces together. McKinsey (2018) recently found that only 8 percent of companies globally were able to successfully scale analytics and therefore drive a culture of analytics within their organization.

To become one of those organizations that moves beyond this chaos and is successful at driving a culture of analytics, organizations must find a way to bring order to their analytic efforts.

Session Outile
This session presents the way forward through a unified analytics platform – because a true analytics platform helps organizations orchestrate the journey from data to tangible results. The session attempts at delivering the possibilities to address and connect each phase in what is called the Analytics Life Cycle.
The session is a mix of concepts, case study and demonstrations to drive home the benefits of unified approach towards managing and implementing Analytics Life Cycle.

Primary Tools: SAS Viya and related tools
Primary Area: Data Analytics/Machine Learning
Main Demonstrations: Data Access , Data Exploration and ML Algorithm Applications.

Beginning of the session:

The session starts with introducing the challenges presented in having a unified approach towards analytics. It then presents the SAS unified approach towards analytics as a single platform to handle almost all analytics needs. It presents the concept of Analytics Life Cycle (ALC) and describes in detail each of the phases involved : Data Discovery and Deployment. The session introduces challenges and benefits that are representative and universal examples. The main theme of this part of the session revolves around the fact that without recognizing the importance of the analytics life cycle, organizations don’t really have a process. Companies may end up with analytics that are not tied to business issues, that don’t have the right data, and/or are just an academic exercise.

Middle part of the session:

While describing each phase a sample case study is used. The platform used is SAS Viya and its corresponding tools. The case study starts with data access demo. It builds on the step of data exploration including major steps like distribution analysis , visual data exploration and necessary demos for data manipulation. At this stage participants are introduced with the power of SAS Visual Analytics and SAS Data Studio.

Post the data phase , it introduces and demonstrates the powerful modelling capabilities, of SAS VDMMLSAS Model Studio. The session briefly introduces predictive models like regression, neural nets and gradient boosting and demonstrates model building at this stage. Steps for data partitioning are introduced at this stage and importance of model validation based on honest assessment is discussed. The session develops into showcasing various activities under data discovery phases like application of machine learning algorithms and model comparison strategies.

The session then introduces and demonstrates deployment phase of analytics life cycle. Here the power of SA Studio is leveraged.

End of the session:

The session culminates in summarizing the learnings and the advantages of SAS Viya platform to manage and implement analytics life cycle.


Who should Attend: Data Analyst, Data Scientists, Students Pursuing Masters in the Field of Statistics, Engineering, Mathematics, Economics, Business Analysts.

Bio: 

Sunil is a senior analytics consultant, Education at SAS India. He is a SAS certified data scientist and in his current role, Sunil works with various clients of SAS in the Asia Pacific region to develop workforce in effective use of the SAS products in machine learning, artificial intelligence, data management and Business data visualization. He is also engaged in Industry specific solution mentoring in Financial Services, Insurance, Manufacturing, and Telecommunication.

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