Table of contents for IBM Big Data Analytics Analyst Insights 2013
- Driving Transformation with Big Data Analytics #BDA13
- Business Value of Big Data and Analytics #BDA13
- Big Data and Analytics: Fueling Competitive Advantage #BDA13
- Using Analytics for Competitive Advantage #BDA13
- Big Data and Analytics Use Cases #BDA13
- IBM Watson Engagement Advisor #BDA13
- An IBM Innovation Panel #BDA13
- Enhancing the Client Experience #BDA13
- Addressing the Analytics Skills Gap #BDA13
- Big Data Inspires Analytics Innovations at IBM #BDA13
- Speed of Analytics: Why Infrastructure and Platforms Matter #BDA13
- IBM Solutions for Insight Driven Marketing #BDA13
- IBM Counter-Fraud Point of View #BDA13
- Closing thoughts on IBM Big Data Analytics #BDA13
After a couple of customer conversations Stephen Gold of IBM came back on time to discuss Watson. I have blogged about Watson a couple of times (including this one on what Watson means for decision management). Recently IBM has started working with clients around customer engagement, not least because it has become increasingly practical to run Watson on reasonable commercial systems.
Watson has a number of critical differentiators:
- It understands natural language
- Generates and evaluates evidence-based hypothesis
- Adapts and learns from user selections and responses
Customers are willing to share more information if better customer service is the consequence and will actually pay significantly higher prices if they can get more personalized service. Yet most are consumers are becoming increasingly frustrated by the terrible interactions they have and by the lack of availability at certain times or in certain locations. Add to this the fact that 10,000 baby boomer retirees every day (which removes knowledge from the system and changes the demographics of users and customers), mobile and social media and companies need something new.
IBM Watson Engagement Advisor is designed therefore to allow a natural dialog, a chat, to get to a better evidence-based result either alone or through an agent. In a typical conversation it not only analyzes the unstructured data available, it also presents evidence based assessments of how good the advice is and presents multiple back up items. It can be made stateful so it can rejoin a conversation later.
IBM Watson Engagement Advisor is targeted on Financial Services, Telecommunications in particular (retail, tech and healthcare too) with business buyers dominating inquiries. It’s a SaaS delivery for mobile-first, cloud-centric solutions. The target is a six month deployment and a six month ROI.
Scott McKinley from Nielsen came up to talk about their plans for Watson. Nielsen, like many organizations, is faced with massive challenges as people buy goods and consume media in new ways. Not just vastly more data (from RFID, mobile devices) but data in new formats. Their clients also have a need for more rapid response to cope with more rapid changes in consumer behaviors.
In the face of this change, Nielsen is focused on how to help people deliver personalized interactions (beyond segmentation) across in-store, online, advertising and marketing. Nielsen has created an innovation facility with Standard GSB where technologies like Watson can be evaluated for and with clients with a focus on social, mobile and cross-media attribution.
Sadly he had nothing to say about how well it worked – clearly this is just beginning at Nielsen. Some interesting potential use cases though, will be interesting to see how this goes.
Clearly IBM sees Cognitive Computing, and Watson, as a big deal going forward.