Predictive Analytics on Cloud: Major Drivers

Predictive analytics can easily mine and turn a large volume of data into valuable business insights. This requires organizations to build statistical predictive modeling systems that demands significant time and resources with niche skill sets. It’s just a matter of time that organizations start realizing and moving their predictive and statistical analytic systems onto the cloud.

Drivers for cloud are not just dealing with big data, niche skills, and time it takes to build the system, but also the volume of consumer’s behavioral information that is available online, which can help in building a full proof predictive modeling system.

When you plan to build and deploy a predictive model, one of the major bottle necks would be to convert your data into a format that facilitates building and deploying predictive models, The transformation of data however is often a series of database operations (group by, join, where clauses), Numerical transformations (binning, ratios, log transforms, etc.), and text processing (stemming, grouping/binning). Except for some database operations, these operations are inherently parallelizable, lending themselves nicely to cloud solutions.

Lastly, a major driver for moving predictive analytics stack to cloud would be mobility and accessibility of the solution if the solution is on cloud; Decision makers are often traveling and having access to their business data on cloud can be a major driver for organizations to move their predictive analytics to cloud.

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What is nosql ? why nosql ? When Nosql?

no sqlWhat is nosql?

Unlike what it sounds Nosql means “not only sql” since the goal is not to reject SQL but, rather, to compensate for the technical limitations shared by the majority of relational database implementations.

NoSQL is a whole new way of thinking about a database. NoSQL is not a relational database.

nosql is becoming prominent, for the simple reason that relational database model may not be the best solution for all situations

Best way to think of nosql db is distributed non-relational db with very loose structure or no structure

NoSQL databases are finding significant and growing industry use in big data analytics and real-time web applications

Why nosql?

In 2000,Eric Brewer outlined the now famous CAP(Consistency, Availability and Partitioning) theorem,

which states that both Consistency and high Availability cannot be maintained when a database is Partitioned across a fallible wide area network.

so to get all consistency availability over partitions nosql comeback to deal with data explosion

other imp advantages along with providing Consistency, Availability and Partitioning are as below

  • Horizontal Scalability
  • More flexible data model and
  • Performance advantages


When nosql?

Typically nosql db would be preferred but not limited in the following scenarios

  • Real time web applications
  • Unstructured/”schema less” data – usually, you don’t need to explicitly define your schema up front and can just include new fields
  • Huge data (TBs)
  • When scalability is critical


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Is Social Mining already deciding your forecasting and Pricing?



big data analytic services

A recent study conducted by Oracle Corporation in the retail sector revealed that customers are more social media savvy and the reason behind selecting a particular brand as the best brand is customer service (post sale). If you have visited Japan, Australia or India you may have seen an “Oxygen Bar.” These are establishments that sell oxygen for recreational and consumer usage…seriously. Visit www.o2bar.com.au or Google it. When I first saw the statistics below, I felt that maybe what I really need is a “free air” bar, as in free of Social Media. But this seems impossible in today’s digital world. Social Media has not only played a major role in connecting people, it has also brought a paradigm shift in the way enterprises conduct business.

Here are some quick facts about the ever-present role social media now plays in our relationships and buying decisions:

– How demand is influenced (Forecast)

– 20% of time on PCs is spent on social media. On mobile devices, people are on social media 30% of the time (Nielson)

– Consumers are 71% more likely to make a purchase based on social media referrals (Hubspot)

– Social networks influence nearly 50% of all IT decision makers (LinkedIn – learn more at TechConnect ’12)

– 74% of consumers rely on social networks to guide purchase decisions (SproutSocial)

– Facebook is the most effective platform to get consumers talking about products (SproutSocial)

– 44% of automotive consumers conduct research on forums (Mashable)

– 81% of US respondents indicated that friends’ social media posts directly influenced their purchase decision (Forbes)

– 78% of respondents said that companies’ social media posts impact their purchases (Forbes)

It is not enough for a company to say, “I am mining social data and using Big Data technologies.” Instead companies need to clearly state and understand “What are you mining?”;”Do you understand the ROI?”; ”Do you know how it integrates with demand and pricing management?” If the answers to these questions are not clear, you may not be there yet; but should any sense of complacency arise, just ask, “Is my competitor ahead of me with social mining?”

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