“Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.”
Too much information?
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, customer records, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. And it is growing in volume, velocity and variety with every passing day.
Suffice it to say that the data holds tremendous potential for insight and competitive advantage. However, to gather any information from this data, we need to tackle some of the challenges it creates:
- Data is not structured in relational tables
- Data has not been cleansed or optimized for analysis
- Data must be integrated from heterogeneous data stores (CRM systems, flat files, emails, social media and so on)
Are we then going to drown in waves upon waves of big data or do we need an ever increasing army of data scientists to tackle the problem?
Enter IBM Watson
IBM Watson gained widespread attention in 2011 by beating two of the all-time champion human contestants on the TV quiz show, Jeopardy! Since that time, IBM has put Watson to work in ways that benefit society and change the way our world thinks, acts and operates.
The understanding is expedited by leveraging Watson’s language vocabularies and capability for natural, familiar interaction. Its semi-automated ingestion of organization-specific documents and other information also helps slash set-up time. In short order, Watson can answer human questions using natural language processing (NLP).
Jeopardy was selected as the ultimate test of the machine’s capabilities because it relied on many human cognitive abilities traditionally seen beyond the capability of computers, such as:
- The ability to discern double meanings of words, puns, rhymes, and inferred hints.
- Extremely rapid responses
- The ability to process vast amounts of information to make complex and subtle logical connections
The next logical extension is to use the smarts of Watson to build an updating knowledgebase with continuous ingestion of big data. Of course, it involves the use of processes and vocabularies that “direct”, so to speak, the intelligence a tool like Watson might gather. IBM Watson can systematically use its natural language processing, hypothesis generation and evidence-based learning capabilities to create a semantic Extract-Transform-Load (ETL) layer for the data it encounters.
In a human, these capabilities come from a lifetime of participation in human interaction and decision-making along with an immersion in context-based information.
IBM Watson represents a bold new step into a new era of computing and has the potential to transform the way people and companies interact over the lifetime of their relationships. In the field of marketing for instance, individual consumers can interact with Watson in plain English, directly or through an agent, to get personalized responses to questions and receive actionable insight with supporting evidence and confidence to help create the experiences customers expect.
For example, a telecom firm could arm its agents with Watson to help troubleshoot a problem such as synchronizing email with a mobile device. Combining use of Watson for both direct customer self-service as well as agent assistance can help create the kind of engagement experiences that delight customers.
The Watson Engagement Advisor can help meet growing expectations that companies know their customers based on their past history, engage them wherever, whenever, and however they choose, and empower them at the point of action.
(Interested to hear more? For case studies or a cost-benefit analysis, please contact us or check us out at the Smarter Commerce Global Summit)
(Title picture courtesy of Venkatesh Madala)
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