Intended Purpose
Watson Analytics is a commercial offering of IBM’s Watson computer. The Watson computer is an artificially intelligent machine that IBM (2015) says processes information using a cognitive framework, imitating the human thinking process (IBM Watson, 2014, October 7). Watson uses machine learning rather than having developers enter already known facts into its system. First, Watson "learns" a subject by having a myriad of resources such as word docs, PDFs, and webpages uploaded into its databases. Watson is then "trained" by human experts who input question/answer pairs from which Watson learns facts and patterns. (IBM, 2015, "What is Watson?") Watson then "searches millions of documents to find thousands of possible answers, collects evidence and uses a scoring algorithm to rate the quality of this evidence, [and] ranks all possible answers based on the score of its supporting evidence" to answer questions (IBM, 2015, "What is Watson?").
According to Alistair Rennie, General Manager of Business Analytics at IBM Software Group (as cited in IBMWatson, 2014, September 16), Watson Analytics aims to put “incredibly powerful analytics” within the reach of “every user in every business” since it often remains “locked up in IT or in the domain of data scientists” (1:38). It allows users to safely access enterprise data or upload their own before directing them through sophisticated statistical analysis and visualization. Furthermore, it removes the need to understand writing predictions or detailed statistical analysis, allowing employees and leaders to make decisions and come to answers with a user-friendly tool. (IBM Watson, 2014, September 16)
Watson Analytics is a commercial offering of IBM’s Watson computer. The Watson computer is an artificially intelligent machine that IBM (2015) says processes information using a cognitive framework, imitating the human thinking process (IBM Watson, 2014, October 7). Watson uses machine learning rather than having developers enter already known facts into its system. First, Watson "learns" a subject by having a myriad of resources such as word docs, PDFs, and webpages uploaded into its databases. Watson is then "trained" by human experts who input question/answer pairs from which Watson learns facts and patterns. (IBM, 2015, "What is Watson?") Watson then "searches millions of documents to find thousands of possible answers, collects evidence and uses a scoring algorithm to rate the quality of this evidence, [and] ranks all possible answers based on the score of its supporting evidence" to answer questions (IBM, 2015, "What is Watson?").
According to Alistair Rennie, General Manager of Business Analytics at IBM Software Group (as cited in IBMWatson, 2014, September 16), Watson Analytics aims to put “incredibly powerful analytics” within the reach of “every user in every business” since it often remains “locked up in IT or in the domain of data scientists” (1:38). It allows users to safely access enterprise data or upload their own before directing them through sophisticated statistical analysis and visualization. Furthermore, it removes the need to understand writing predictions or detailed statistical analysis, allowing employees and leaders to make decisions and come to answers with a user-friendly tool. (IBM Watson, 2014, September 16)
Features, functionalities,
and why they are useful
Watson Analytics has various distinctive and exciting features. The cognition model of the Watson computer allows for a question-answer model that is seen in Watson Analytics. Users can pose a question in their natural language, and Watson responds (Watson Analytics, 2015). IBM employee and Watson Analytics user Debra Pesek (2015, September 17) explains, “this empowers users across… the lines of business to get quick responses from their data and gain from the benefit of analytics.” Additionally, after a user’s data has been entered, Watson Analytics will suggest a variety of questions the user may not have considered. Pesek (2015, September 17) says this helps to “uncover the hidden patterns and relationships most likely to yield actionable insights” and saves users from wasting time perusing “uninformative blind alleys” buried within large data sets. Watson Analytics also helps users generate visualizations. Users who work with data will find its directed discovery particularly advantageous when Watson will recommend a variety visualizations that best illustrate the data (Pesek, 2015, September 17).
Watson Analytics has various distinctive and exciting features. The cognition model of the Watson computer allows for a question-answer model that is seen in Watson Analytics. Users can pose a question in their natural language, and Watson responds (Watson Analytics, 2015). IBM employee and Watson Analytics user Debra Pesek (2015, September 17) explains, “this empowers users across… the lines of business to get quick responses from their data and gain from the benefit of analytics.” Additionally, after a user’s data has been entered, Watson Analytics will suggest a variety of questions the user may not have considered. Pesek (2015, September 17) says this helps to “uncover the hidden patterns and relationships most likely to yield actionable insights” and saves users from wasting time perusing “uninformative blind alleys” buried within large data sets. Watson Analytics also helps users generate visualizations. Users who work with data will find its directed discovery particularly advantageous when Watson will recommend a variety visualizations that best illustrate the data (Pesek, 2015, September 17).
Watson at IVK
 |
| (IBM, 2014 December) |
CIO Jim Burton could implement Watson
Analytics across a multitude of functions at IVK including sales, human
resources, and information technology among others. The sales department can
take a cue from Amazon’s success at relentless A/B testing (Gallaugher,
2015, “Amazon”) and use Watson Analytics
to construct win/loss predictions in order to identify strong and risky leads,
allowing sales to spend the most attention and resources on deals that will
likely go through and have a higher yield (IMB Watson Analytics, 2015 “Business
Role: Sales”). Sales can also use Watson to identify which customers are at
risk of leaving for another firm and proactively win their loyalty (IMB Watson
Analytics, 2015 “Business Role: Sales”). HR could also run analytics on which
employees are more likely to leave and stay.
This would allow each department to key in on their top performers (IMB
Watson Analytics, 2015 “Business Role: HR”), which would reduce the cost of
turnover and anticipate attrition’s impact on business performance (Pesek,
2015, September 17). Within the IT function, Watson Analytics improve help desk activity and
efficiency by allowing the team to see how long it takes to resolve problems
and why (IMB Watson Analytics, 2015 “Business Role: IT”), improve processes,
and develop strong relationships with internal and external customers.
Watson Analytics would be one avenue of introducing innovation to IVK, which according to a Rackspace white paper (2015) is becoming increasingly important to stakeholders (p. 3). Since Watson Analytics is a cloud-based service, it would provide “attractive opportunities… to offload activities to focused vendors with economies of scale, experience and expertise” (Rackspace, 2015, p.3). This would allow IT to focus less on maintaining data in order to focus resources on strategy and competitive advantage. Investing in a cloud-based data analysis that can be utilized across functions can also help IVK establish culture of data stewardship, which is incredibly important as stakeholders have deep interests in big data. As a culture of data stewardship grows stronger, “collaborating with peers increases over time as business people realize the data is getting better and has greater value” (Dyché, J. & Polsky, A., n.d., p.21)
Watson Analytics would be one avenue of introducing innovation to IVK, which according to a Rackspace white paper (2015) is becoming increasingly important to stakeholders (p. 3). Since Watson Analytics is a cloud-based service, it would provide “attractive opportunities… to offload activities to focused vendors with economies of scale, experience and expertise” (Rackspace, 2015, p.3). This would allow IT to focus less on maintaining data in order to focus resources on strategy and competitive advantage. Investing in a cloud-based data analysis that can be utilized across functions can also help IVK establish culture of data stewardship, which is incredibly important as stakeholders have deep interests in big data. As a culture of data stewardship grows stronger, “collaborating with peers increases over time as business people realize the data is getting better and has greater value” (Dyché, J. & Polsky, A., n.d., p.21)
References
Dyché,
Jill, & Polsky, Analise. (n.d.) 5 Models
for data stewardship. (SAS Inc.) Cary, NC. [White paper]. Retrieved
fromhttp://resources.idgenterprise.com/original/AST0147233_5_Models_for_Data_Stewardship.pdf
Gallaugher, John. (2015). Information systems: a manager's guide
to harnessing technology, v. 2.0. Available from http://catalog.flatworldknowledge.com/bookhub/reader/12375?e=fwk-38086-ch12_s02#fwk-38086-chab
IBM. (2014, December). Do it yourself with Watson Analytics.
[Infographic]. Retrieved from http://www-01.ibm.com/software/analytics/infographics/watson-analytics/diy.html
IBM. (2015). IBM
Watson: what is Watson? Retrieved September 17, 2015, from http://www.ibm.com/smarterplanet/us/en/ibmwatson/what-is-watson.html
IBM. (2015). IBM Watson Analytics. Retrieved September 17, 2015 www.ibmbigdatahub.com/blog/watson-analytics-revolutionizing-office-finance
Pesek, Debra. (2015, September 17). Watson Analytics is revolutionizing the
office of finance. The Big Data Hub. Retrieved September 19, 2015, from
http://www.ibmbigdatahub.com/blog/watson-analytics-revolutionizing-office-finance
Rackspace.
(2015). Starting the journey to managed infrastructure. Windcrest, Texas. [White paper]. Retrieved on 2015,
September, 20 from http://resources.idgenterprise.com/original/AST-0141122_Journey_to_Managed_Infrastructure_
white_paper-3.pdf
No comments:
Post a Comment