Future of Predictive Analytics

Recently i had attended a conference on “Future of Predictive Analytics” here at Bangalore. Here is the summarized version of the topics covered; Data mining is an automated process of discovering hidden patterns/trends in data using statistical and mathematical techniques. Data Mining is an Academicians term while “Predictive analytics” is the equivalent term used by an business analyst/business professional. The motivation for predictive analytics are reduced storage costs, ease of availibility of data capture techniques and growing complexity of the business.Here are some of the interesting applications of data mining:

• Sentiment Analysis: It is sometimes referred as “Opinion Mining“. There are lot of unstructured data/information available in the form of blogs, social networking sites, emails & Documents. For example, feedback/comments about a newly released product may be available in the form of tweets in case of twitter micro blogging or as comments in a blog.By extracting this unstructured information and running NLP algorithms on this data can give the company valuable insight about  “how well is the product accepted among users?” and “what are the positives/negatives seen by the users about my product?“.This is a very challenging problem, as NLP stands to be the highly researched topics with not much signifcant achievement due to highly dynamic language semantics.
• Audio/Video mining: The voice logs of call center/service desk generate huge amount of data.Manually listening to each of them and infering conclusions is humanly impossible. Hence automated means are necessary to programatically infer the underlying feedback provided by its customers. The challenges here are the variation in the human voice, pronunciation and accent.For example: some individuals pronounce sci-fi as ski-fi. 
  

  High resolution camera in a restaurant to record customer's facial expressions

        Recently i read a newspaper article about a restaurant in US using high resolution cameras to record customer facial expressions. On analyzing this video data, some valuable feedback can be obtained about the food being served, which can be used to make strategic decisions.

• Visualization: Visualization is another technique of analyzing spatial data.
  

  Visulization of US elections campaigning cost

  US elections campaign cost data could be visualized broken down by cities and zip codes. Google Maps provides APIs to integrate the geo-spatial data. As the complexity of data increases, newer visualization techniques evolve.

• Real time BI: Walmart, the biggest retail chain in US,uses real time decision making to promote sales of products across various parts of the world. It is known to have the state of the art of Trickle feed systems and data warehouse.

My next post would cover many more industry applications of data mining and its concepts. Feel free to comment about various other applications of analytics which you would have come across.

                      

Advertisement