This blog post is about comparison of and in terms of similarities across dimensions of analytic maturity & use of data shared by their customers. As Thomas Davenport mentions in his book “Competing with analytics”, is one of the few companies which was built on the foundation of data, the so called “Analytically mature” company. LinkedIn has joined the list, with lot of new features available to their users.

As customers interact with the site, they generate data about their liking towards certain products or feature. Companies like and LinkedIn clearly understand how to leverage this information to make the interaction between the customer and the site even more valuable & relevant. Users who are ready to share more data with site about their likes/dislikes, the better would be the site’s recommendation for the user.  The companies need to instil this confidence in the customers mind, and hence have the users share data by will.

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Turning raw data into insights often involves integrating data from multiple disparate sources (not just limited structured one), analyzing the data, visualizing it and socializing the results/insights to a broader audience to whom the results are of interest. In this cycle of turning data into insights, Visualization plays a vital role and hence would be the topic of my discussion in this blog post . Visualization could aid in analyzing huge data by identifying patterns which are easily interpretable visually as compared to tabular layout of numbers.Second, Visualization could help  represent the numbers using visuals which are easy for everyone to read and understand. One could easily convey the insights of the analysis by visuals, grasped in a minute or two, which might have possibly took 3-4 mins using textual aid/table of numbers.This is a important factor to consider especially when are you delivering the findings to the CEO/CFO/CXO/CIO of a company, as often they have limited time.

London Cholera Outbreak visualized

London Cholera Outbreak visualized

Going back to history of visualization. The most famous, early example mapping epidemiological data was Dr. John Snow’s map of deaths from a cholera outbreak in London, 1854, in relation to the locations of public water pumps. The original (high-res PDF copies from UCLA), spawned many imitators including this simplified version by Gilbert in 1958. Tufte (1983, p. 24) says,”Snow observed that cholera occurred almost entirely among those who lived near (and drank from) the Broad Street water pump. He had the handle of the contaminated pump removed, ending the neighborhood epidemic which had taken more than 500 lives.” Read More

In my previous post, i had discussed about Association rule mining in some detail.  Here i have shown the implementation of the concept using open source tool R using the package arules. Market Basket Analysis is a specific application of Association rule mining, where retail transaction baskets are analysed to find the products which are likely to be purchased together. The analysis output forms the input for  recomendation engines/marketing strategies. Read More

For the last 6 months, i have been closely following trends in information management. Below are few of my observations.

  • Data source explosion: Business Problems are gaining complexity day by day, hence there is a huge demand for analyzing data from multitude of sources to help companies frame strategies for growth.  GPS data accumulated by Telecom companies offer insights into customers current location and provide context aware recomendations. Infact, some of the telecom companies have introduced location based pricing. Sensor data helps identify security threats to secure networks. Social network data has opened up as a channel for marketing services/product. Analysis of such closely knit data leads to behavioral & Contextual targeting. Traditional data analysis tools/algorithms fail to perform efficiently because such data are of huge sizes and needs newer datastructures for efficient analysis.
  • Databases going beyond relational is gaining popularity. NoSQL dbs and Graph/Tree/XML based databases.
  • Open Source tools continue to emerge.(R, RapidMiner, Weka)
  • Growing need for massive dataset analysis.
  • Artificial Intelligence(AI) and NLP gaining popularity among data analysts( in additional to ML techniques)
  • Multimedia Analytics: Need for gathering critical metrics like customer footfalls, quantifying customers satisfaction by using facial expressions. All these applications demand high end signal processing( both Image & Video). There is a lot of scope for innovation in this area.
  • Privacy preserving techniques for data analysis. This in turn encourages companies to outsource some of the critical data analysis to third parties.
  • Agile Methodologies for Analytics Project to cope up with rapidly changing customer/business needs.
  • Bio-Inspiration/Bio-Imitation: To learn from nature/natural processes and develop analogous techniques which could potentially solve a real-world problem. Some classic examples are development of Neural network inspired by working of a human brain, solving path optimization problem from Ant colonies, 280 degree view of honey bee(vision) etc.
  • More and more data are made publicly available.
  • Real Time data integration, insight generation and business decision.
  • Complex visualization techniques through new technology like Adobe Flex , MS Silverlight,etc which are known for generating RIA.(Rich Internet Applications)

And I am sure these are just few items in the list and really not exhaustive. Feel free to share your comments.

Visualization is considered to be one of the valuable tools in data mining. Visual analysis helps to understand data with minimum effort. Using graphs/ 3D plots to visualize data is more effective way of understanding massive sets of data. Ofcourse, we have other ways of exploring data like for example using statistical functions namely mean which depicts the average value, standard deviation / variance which depicts the spread of data and correlation which depicts the relationship between attributes. Some of the classical visualization techniques include

(i) Histograms (ii) Scatter plots (iii) Pie charts

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