HfS published the Top 10 Cognitive Assistant Service Provider report that explores the emerging conversational service provider ecosystem across key areas including execution ability, innovation capability, and the voice of the customer. The conversational services called as Cognitive Assistants, go beyond the traditional chatbots and augment human-customer interaction across both front- and back-office business operations. IBM was recognized for its market leadership in Cognitive Assistant that harness the power of IBM Watson capabilities – including NLP, conversation and analytics. HfS also merits IBM for demonstrating the greatest volume and depth of cognitive assistant use cases across industry verticals and enterprise processes.
Hear a panel of HR leaders engage in a conversation about how they are using predictive and prescriptive analytics applied to talent to drive better business results. Join us to explore how you can be more effective in designing pilots, demonstrating the ROI for talent analytics, and translating talent data into talent insight.
What if we stopped arguing over which analytics software is best, and decided instead to use them all? With today’s analytics technologies, the conversation about open analytics and commercial analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics within your organization. Download this e-book to learn how businesses in multiple industries are integrating disparate code and information to deploy models and deliver critical results with analytics.
How can you open your analytics program to all
types of programming languages and all levels of
users? And how can you ensure consistency across
your models and your resulting actions no matter
where they initiate in the company?
With today’s analytics technologies, the conversation
about open analytics and commerical analytics is no
longer an either/or discussion. You can now combine
the benefits of SAS and open source analytics
technology systems within your organization.
As we think about the entire analytics life cycle, it’s
important to consider data preparation, deployment,
performance, scalability and governance, in addition
to algorithms. Within that cycle, there’s a role for
open source and commercial analytics.
For example, machine learning algorithms can
be developed in SAS or Python, then deployed in
real-time data streams within SAS Event Stream
Processing, while also integrating with open systems
through Java and C APIs, RESTful web services,
Apache Kafka, HDFS and more.
With more data in the hands of more people – and easier access to easy-to-use analytics – conversations about data and results from data analysis are happening more often. And becoming more important. And expected. So it’s not surprising that improved collaboration is one of the most common organizational goals.
Let’s take a look at how you can use results produced by SAS Visual Analytics with Microsoft Office applications. You’ll see how easy it is to combine sophisticated analytic visualizations and reports with Microsoft’s widely used productivity tools – to share insights, improve collaboration and drive increased adoption of analytics and BI across your organization.
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