Sunday, September 29, 2013

Bigdata & TimeMachine

Powers of #Bigdata analytics, we can find out which movie gonna be blockbuster next year, not only the movie but also the future, the TimeMachine. Yesterday I saw a movie Paycheck, Michael Jennings is a reverse engineer; he analyzes his clients' competitors' technology and recreates it, often adding improvements beyond the original specifications. I think this is a best real use-case of Bigdata Implementation.

Michael creates a Time Machine with one of the his old college roommate, James Rethrick, the CEO of the successful technology company Allcom, after successful creation of TimeMachine James wipes Michael's memory, but before cleaning Michael's memory, Michael seen his future(in TimeMachine) and accordingly he sent himself a parcel(which delivers him after two years) using the things the parcel has, Michael(with lost memory) able to predict the things which he should do after two years to save himself from James.                                                      
Now we can see the things, which really correlate with Bigdata Analystics, Time Machine woks on principle of Astrology and the things we did in past gonna help us in future to survive and get the right direction, technically the data we(and off course the people who has a impact on our life) generated in our past, gets analyzed and using that analytics we are able to predict a future. Many companies now Analyzing the Bigdata generated/generating by each business vertical and designing a recommendation and decision engines to help business to survive in market.

Recommendation and decision engines, an area of predictive analytics and decision management, are going to quite active in next year, The pioneer was which used collaborative filtering to generate “you might also want”  or “next best offers” prompts for each product bought or page visited. 

I really appriciate your valuable comments and suggestions that guide me and you to direct our own future. Stay tunned for more updates on #TimeMachine

Friday, September 27, 2013

Bigdata & Natural Language Processing(NLP)

Natural language processing (NLP) is increasingly discussed in social media and other verticals of businesses, but often in reference to different technologies such as speech recognition, computer-assisted coding (CAC), and analytics. NLP is an enabling technology that allows computers to derive meaning from human, or natural language input.

Media is data intensive from customer satisfaction, product reviews and business perspectives. While the industry’s transition to electronic data collection and storage in recent years has increased significantly, this has not actually forced physicians to code the majority of meaningful content. Eighty percent of meaningful data remains within the unstructured text, as it does in most industries. This means that it remains in a format that cannot be easily searched or accessed electronically.

NLP can be leveraged to drive and directly impacting on improvements in financial, production, and operational aspects of business workflows:

For financial processes, automating data extraction for claims, banking transactions, financial auditing, and revenue cycle analytics can impact the top line. NLP can automatically extract underlying data, making claims more efficient and offering the potential for revenue analytics.
For production processes, automatically extracting key quality measures existing products and customer reviews, reporting and analytics. NLP can infer whether a product meets a quality measure. prelaunch response from customers, so decide a product launching stategy.

For operational processes, descriptive and predictive modeling can support more effective and efficient operations. NLP can extract hundreds of data elements similar available product rather than the 2-4 available products, producing better models and supporting business insight.

So, NLP is a powerful enabling technology, but it is not an end user application. It is not speech recognition or revenue cycle management or analytics. It can, however, enable all of these.

There is a battle underway that is increasingly recognized in the business space. Individual business divisions seek turnkey solutions and frequently purchase NLP-enabled products. But at a broader level.

We can use natural language processing for customer sentimental analysis, customer segmentation and many of the business cases, and find out the customer response and satisfaction from similar available products in market and to maintain quality of already released product, to decide business strategy to be a different in market.