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LatentView Analytics is one of the world’s largest and fastest growing data analytics firms.

Conversation with a Resident Analyst. Observation: The data obtained was the analysed to see where these products were predominantly being used.

Conversation with a Resident Analyst

It can be observed that QuickBooks Desktop was being used widely in the Accounting vertical, QuickBooks Online being used in Accounting and Marketing verticals almost equally, Xero and Sage were used the most in Information Technology & Services while FreshBooks and Sage were being preferred in the Marketing & Advertising vertical. Also, while ‘Small Merchants, prefer QuickBooks Online over the rest, the product that gained momentum in the ‘Mid-Market’ and ‘Enterprise’ categories was Sage.

Furthermore, since QuickBooks Online product was aimed at catering to the accounting needs of small and medium businesses, dislikes and negative sentiment associated with the product is a major concern for Intuit. Representation: The ratings were on a scale of 1-5. Data Drive Through: Gregory Piatetsky Shapiro. LatentView had an opportunity to Interview Gregory Piatetsky-Shapiro, Ph.D., Data Mining and Analytics Expert from KDNuggets – a one-stop target for all things related to data analytics.

Data Drive Through: Gregory Piatetsky Shapiro

If you haven’t heard of Gregory or have not visited KDNuggets, you are missing a treasure trove. Follow him at @kdnuggets on Twitter. We wanted to use this opportunity to ask a wide range of questions and get pointers that would help the audience get a feel of the gamut of possibilities within the realm of Data Analytics. Gregory has been generous enough to quote pertinent references and also elucidate various aspects using the work covered by KDNuggets. According to you, what makes a Big Data Solutions/Services provider? First, I want to mention that most businesses do not have Big Data problems. This is also supported by a recent KDnuggets Poll, where a median answer to the question Largest Dataset Analyzed/Data Mined was in 40-60GB range, the data size which comfortably fits on a single laptop.

Time-series forecasting. Observation: The data obtained was the analysed to see where these products were predominantly being used.

Time-series forecasting

It can be observed that QuickBooks Desktop was being used widely in the Accounting vertical, QuickBooks Online being used in Accounting and Marketing verticals almost equally, Xero and Sage were used the most in Information Technology & Services while FreshBooks and Sage were being preferred in the Marketing & Advertising vertical. Also, while ‘Small Merchants, prefer QuickBooks Online over the rest, the product that gained momentum in the ‘Mid-Market’ and ‘Enterprise’ categories was Sage. Furthermore, since QuickBooks Online product was aimed at catering to the accounting needs of small and medium businesses, dislikes and negative sentiment associated with the product is a major concern for Intuit.

Representation: The ratings were on a scale of 1-5. Movie Success Prediction. With the advent of Big Data and analytics, the world has changed in ways previously unimaginable.

Movie Success Prediction

In a rapidly growing and thriving industry such as the motion picture industry, data analytics has opened a number of important new avenues that can be used to analyze past data, make creative marketing decisions, and accurately predict the fortunes of impending movie releases. The timing of the movie release is critical to the success of a movie. To facilitate the release date selection, studios decide and pre-announce the targeted release dates on a weekly basis long before the actual release of their forthcoming movies. Their choices of release dates and then the subsequent changes are strategic in nature taking into consideration various factors like regional holidays, cultural events, political situation, sports events etc.

Consider a scenario where a movie has already been slated for release on a particular date. Business Challenge: Approach. Advantages and Applications of Mobile Data. In this issue of #highondata Anindya Ghose, Professor of Information Technology and Marketing and Director, Center for Business Analytics at NYU Stern talks about how mobile data is helping companies target consumers at a very granular level.

Advantages and Applications of Mobile Data

Mobile advertising has a reputation of being difficult, from an execution perspective, and ineffective. What is your take on this? Mobile as a channel has its own challenges just like any other channel. Yes, you often hear marketers say mobile doesn’t really work. Those who say so are misguided. It’s a myth that consumers hate ads. Consumers spend about 24 percent of their time on their mobile device, but only 8 percent of marketing dollars are being spent on mobile advertising as of 2015. That said, in the U.S. alone, mobile ad spends amounted to $25 billion last year. I have a forthcoming book on this topic that is being published by MIT Press. What are some of the most common ways marketers are using mobile data today?