This document provides an overview of how workflows can help make big data insights more accessible. It discusses how workflows allow customers to benefit from cost reductions and faster deployment times. Examples are given of customers in healthcare and banking that have reduced surgical infection rates and cut model development time in half using workflows. The document also covers how to pull insights together and deploy predictive models to external systems using tools like Tibco Statistica. It provides a technical overview of building predictive analytics workflows for big data, including examples of workflow templates for Spark, H2O, and deep learning with CNTK.
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Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workflow Tools
1. How Big Data Insights become Easily
Accessible with Workflow Tools
2. Session Overview
➢ Introduction To Workflows and Big Data For Data Scientists/Data
Citizen
➢ Examples Of Customers Benefiting From Using Workflows –
Reduction In Cost, Speed To Deploy
➢ Pulling It All Together - Introduction To Deploying Models To
External Systems
➢ Technical Overview Into Building Predictive Analytics Workflows For
Big Data (Tibco Statistica)
12. Bank speeds time to market
with advanced analytics
Business need
To deliver timely and accurate credit decisions and other customer
services in today’s 24/7 world, Danske Bank needed to be able to
quickly build and deploy advanced analytical models.
Benefits
● Slashed time to develop and deploy analytical models by 50
percent
● Improved decision-making with more advanced analytical models
● Delivered an easy-to-use, standardized toolbox that can quickly
be customized to meet users’ needs
● Ensured fast ROI by deploying easily and integrating smoothly with
existing systems
Solution
Enable customers to apply for products such as loans through Danske
Banks portal. Generate scoring models to determine whether customers
applications are accepted.
“We have reduced the time we spend
on models up to 50 percent with
Statistica. Our development process is
much leaner and smoother compared
to what it was before.”
Jens Christian Ipsen,
First Vice President, Danske Bank
13. 13
➢ Pulling It All Together
Introduction To Deploying Models
To External Systems
15. Machine Learning in TIBCO Statistica
TIBCO StreamBase for real-time scoring and action
TIBCO Statistica Deploy To External Application
• Model built in TIBCO Statistica
• Score model in TIBCO StreamBase on live data
• Action: equipment intervention
17. One stop shop for actionable insights
DATA SCIENCE STREAMING ANALYTICSBI & ANALYTICS
AI-driven visualization
to gain insight to
find actionable
insights
Create analytics that
can predict the future
based on history
Provide analytics and
take action on real
time streaming data
18. One stop shop for actionable insights
DATA SCIENCE STREAMING ANALYTICSBI & ANALYTICS
AI-driven visualization
to gain insight to
find actionable
insights
Create analytics that
can predict the future
based on history
Provide analytics and
take action on real
time streaming data