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CEO at Kahuna, a cross-channel marketing automation platform that uses artificial intelligence to engage and convert consumers on the right device at the right time. Kahuna is trusted by modern digital products such as Dollar Shave Club, Yelp, GoPro and others. Prior to Kahuna, Sameer was SVP for Enterprise Social and Collaborative Software in SAP/ SuccessFactors cloud business unit. Sameer has been cited in publications such as CNBC Business, The New York Times, CIO.com and Forbes on high performing organizations, leadership, and trends in enterprise software.

2 responses to “Is Big Data all that? Consider the source.”

  1. Is Big Data all that? Consider the source. - Enterprise Irregulars - Big Data News Magazine

    […] Is Big Data all that? Consider the source.Enterprise IrregularsA seriously content rich debate ensued in the past days on the validity of big data and accompanying algorithms that govern them. The central hypothesis we're debating is MIT Professor Andrew McAfee's assertion that algorithms are smarter than human … […]

  2. Michael Howard (@MichaelHowardSF)

    Andrew McAfee’s Blog on HBR suggests, in effect, that we should throw out or at least relegate human intuition over data algorithms in making decisions. Having been in the analytics space for many years, at Oracle, Greenplum, and now C9, I agree, to a certain point.

    Here in Silicon Valley, where I live and work, we tend to take very little data, and apply our intuition to it, and come to the conclusion that the data suggests a trend. Such erroneous extrapolations are all too familiar, especially when it comes to Sales Forecasts. Yes, when it comes to Sales Forecasts, intuition is, simply put, an obstacle and typically emphasizes the context of a deal, rather than facts, which then cascades into senseless judgments.

    In this scenario, we should let the algorithm do its work, and avoid human intervention as much as possible. But again algorithms and statistics are susceptible too. Case in point are those companies that game Google’s search algorithms, or varying results from genetic testing. In fact, many Data Scientists who build the algorithms see biases in the tools they use, especially proprietary ones. That is not to say that algorithms are just another representation of our intuition, but it does suggest that the topic is not so black and white. At C9, for example, we use both machine learning and embedded risk metrics (e.g. boolean expressions) for sales forecasting. Our algorithms take in thousands of variables to produce the most accurate forecasts in the industry, and have surprising attribution relevance, yet it is sometimes easier for some customers to segue to these algorithms via our more “human” boolean method.

    In short, by applications incarnating both human and algorithmic methods, the results are more trust worthy.