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IT Briefcase Exclusive Interview: How Big Data Analytics differs from Traditional BI with Joe Nicholson, Datameer

August 20, 2012 No Comments

The interest and importance of big data analytics has grown faster than anyone would have imagined even a few years ago. In this interview, Joe Nicholson, Vice President of Marketing at Datameer shares some real use cases, how businesses can separate the market hype from reality and the differences between traditional business intelligence and big data analytics applications.

    1. What are people today actually doing with Big Data analytics?

The use cases in Big Data analytics go far beyond just analyzing very large datasets.  They center on use cases that require the integration and analysis of different data types and sources. These range from marketing use cases such as advanced web analytics, online product usage (cohort) analysis and social media sentiment analytics, security and risk use cases such as fraud detection, identifying rogue trader activity and asset risk analytics, and scientific or research use cases in medical, pharma and related industries.

With traditional business intelligence tools, users are only able to analyze structured data, which limits the amount and kinds of analysis they can perform on their data. With big data analytics, it is now possible to quickly bring together and work with all types of data from any number of data sources, whether it’s structured transaction data or semi-structured or unstructured data such as weblogs, social media data or emails.  Analysis of all data helps users understand and analyze both customer transactions and interactions and lets them answer questions and find insights that simply are impossible from traditional BI and structured data alone.  For example, companies want to understand the entire customer engagement cycle from a web ad all the way through to a purchase, either online on in a store, so that they can see just which web ads actually result in the highest purchase percentage, rather than just the highest click rates.  This requires the integration and correlation of weblogs, clickstream analytics and transaction data in order to get the complete picture.

    2. In your opinion, what is all the “hype” around Big Data that has caused the big BI players to jump on the bandwagon?

It’s not so much hype as it is a whole new set of critical analytic use cases that can only be answered by the correlation of structured and unstructured data. Generally, business has evolved into an interaction economy as the web and social media now drive most of the touches we all make with businesses and with each other.  Transactions are still very important of course, but interactions are much more prevalent and interaction data grows four times faster than transaction data.

Mainstream BI vendors have recognized the importance of these use cases and have evolved their marketing stories –and to some extent their technologies– to “include” big data.  Most of these vendors have introduced some sort of connector to Hadoop, typically via Hive, which enables them to get data out of Hadoop and import it into their systems for analysis. The critical thing to understand here is that BI vendors only analyze structured data in their systems so Hadoop is treated as just another data source for structured data.  Considerable, manual work must be done on any unstructured data in Hadoop to convert into a structured format and loaded into a schema prior to it being brought into any traditional BI system.  This results in a slow and cumbersome process not well suited to today’s demand for faster decisions.

    3. How does the ability to work with unstructured data differentiate Datameer from other vendors in the space?

It actually goes beyond just being able to work with unstructured data — Datameer is the only analytics vendor that runs natively on Hadoop, which gives us the ability to work with raw data whether it’s structured or unstructured.  But beyond that, Hadoop is also compelling for its unlimited storage and compute capabilities, and we’re using it as our product’s backbone for super easy data integration, and powerful analysis and visualization capabilities.  This storage and compute scalability means that we’re able to avoid the rigid, time consuming and costly pre-ingest data modeling process (ETL) that is required by traditional BI solutions.  Basically, Datameer users are free to analyze across any number of data sets of any type and size and can ask any question of the data without the limitations of pre-built data model or schema.

    4. You mentioned that Datameer runs “natively” on Hadoop. How can this potentially benefit different industry verticals?

Not needing to pre-model data means major time and cost savings, which is an attractive proposition in any vertical market or industry domain, whether a company is a Fortune 100 or a fast moving startup. In financial services, Datameer and Hadoop’s capability to analyze years of data means that fraud patterns are easier to identify, rogue traders are easier to detect and asset risks are easier to quantify.  In retail, Datameer can analyze all data sources so marketers can best understand exactly which web marketing campaigns are most effective in driving new revenue and how competitors pricing changes effect in-store sales.  Social media and online gaming companies can use Datameer to understand just how product changes affect usage and can market their third-party advertising placements for maximum revenue.  And medical and scientific researchers can use Datameer’s unlimited analytic horsepower to better understand critical factors in cancer research, plant genomics and pharmaceutical testing.


Joe Nicholson is Vice President of Marketing at Datameer and a veteran software marketing executive with extensive analytics experience at both startups and publicly traded companies.

Joe NicholsonPrior to Datameer, he served as the vice president of product marketing at open source BI leader Pentaho as well as vice president of marketing at compliance and risk management solutions provider Trintech, having joined Trintech as part of its acquisition of Movaris in 2008. Nicholson also served as vice president of marketing and business development at DecisionPoint Software that was acquired by Teradata and served as vice president of platform marketing at Informatica. Earlier in his career, he spent 10 years focused on geographic information systems software with companies such as Autodesk and Strategic Mapping.

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