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  • admin 9:51 am on November 7, 2017 Permalink
    Tags: , , , , , , , Wars   

    Building Deep Learning Machines: The Hardware Wars Defining the Future of AI 

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  • admin 9:52 am on December 18, 2015 Permalink
    Tags: , Polls, seen, Star, Swift, Taylor, , Wars, ,   

    We’ve All Seen Polls For Star Wars And Taylor Swift, But What Can They Add In A Big Data World? 

    Earlier this year, when pollsters predicted a hung parliament (no clear party taking the majority) in the British national elections, pretty much everyone, politicians and public alike, thought the result was all but certain.

    Well, the big news story on the day the results were revealed was not about who won, but about how the pollsters could have gotten it so wrong, leading to an uncomfortable few weeks of (very public) head-scratching and navel- gazing for market research firms.

    That’s not all. These days, the rise in survey software providers have fueled polls on everything you could think of, from the best Star Wars film, to whether you would support Taylor Swift or one of her adversaries. It’s no wonder that businesses are questioning whether market research still has a role to play in a big data world.

    tswift
    Some time back, I was asked to develop analytic models to predict upsell opportunities for corporate telecommunication customers. As the only meaningful transactional data was in the form of quarterly revenue for each customer (spanning over 50 products for three years), I thought the customer services poll results would provide an additional, rich source of information. The survey was extensive, longitudinal and conducted at multiple levels of each customer organization. Pure gold, or so I thought!

    Once we started the analytics, we found that there was very little correlation between customer satisfaction and spending. Customers who expressed strong dissatisfaction continued to increase their net spend with the company, and there were examples of customers who expressed satisfaction and then churned to other service providers.

    Unfortunately, I had to conclude that primary market research data could not be used in a meaningful manner. In the end, it was the use of revenue data that proved far more predictive of a customer’s likelihood to churn.

    So despite these known issues, why are surveys still the predominant means of data collection? Surveys for customer and employee satisfaction, net promoter score (NPS), brand equity modeling and even new product design are so ubiquitous, you’ve probably participated in a few yourself.

    Well, it could be that many executives are measured against metrics such as NPS, and surveys are an easy means of tracking these numbers for senior management reporting. But it is also down to the way that market researchers operate. They assume that every new issue needs a new set of data, and the only reliable way of obtaining this is through a targeted and timely polling of the appropriate population.

    That assumption is no longer valid in the world of big data. Every behavior is observed, digitized and stored over a long period of time. Point-of-sale data and call records capture granular transactional data around purchase patterns and utility usage. Social media outlets record a variety of human generated data including customer sentiments and feedback, audio, photos and videos. Ad consumption is captured through set-top boxes and cookies. Then, there is machine-generated data, such as weblogs and geolocation that adds even more richness.

    Increasingly sophisticated analytic tools and techniques are now available to make sense of these new data sources, and provide new perspectives on customer behavior.

    So what can market research offer in the big data world? The role that market research plays has to evolve. How?

    • Identify all the data that is already available to address the business issue that you’re trying to solve. This means working in collaboration with the database administrators to understand the data resources already available within the company.
    • Enrich these assets by linking them to other data. Market researchers are in the unique position of knowing what historical research data has been collected as well as other external data sources that may be of relevance. These data can provide a context or frame of reference to the internal data and make the inferences from them more valuable.
    • Use the right data processing tools to handle the large volumes of data. The availability of massively parallel data processing systems gives market research teams the opportunity to work with data scientists to bring in big data to answer questions that would have been previously achieved with a survey of a small target population.

    Use the right analytics methodology. Much of the machine generated data sources such as, weblogs and social media data have low information density and will require new analysis methodology to handle the uncertainty in them.

    This post first appeared on Forbes TeradataVoice on 03/12/2015.

    The post We’ve All Seen Polls For Star Wars And Taylor Swift, But What Can They Add In A Big Data World? appeared first on International Blog.

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  • admin 9:49 am on August 4, 2015 Permalink
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    Winning the Connected Car Data Wars 


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  • admin 9:55 am on November 22, 2014 Permalink
    Tags: , integrate, Ownership, , , Turf, Wars   

    Integrate Data, Processes & People: End Data Ownership Turf Wars 

    The biggest thing I’ve realized over the past couple of months, other than Tony Romo is one of the best NFL players I’ve ever seen — is the fact that data ownership is still a huge problem for companies of all types. Tony’s numbers keep getting more impressive game by game – and likewise the pace of data streaming into organizational information channels rises by the hour.

    Romo aside, the dramatic growth of data volume only seems to rekindle data ownership issues among so many internal departments, who don’t or won’t see the advantages of sharing information. Maybe call it data ‘possessiveness.’ I really thought, and I have said in numerous presentations over the summer, that we’d solved the data ownership problem. To begin with, the industry seems to have understood the value of data integration and optimization, and a ‘single view of the customer,’ once just a hazy vision in the distance, was now becoming a technological reality, so isn’t ‘data sharing’ a no-brainer?

    But after presentations at a few conferences this fall, including Teradata Partners, folks have come to say things like, “Everyone in all of our marketing areas wants data from the Customer Insights group — but they won’t play nice and share it.” Others have made similar complaints at reception chats or over lunch. These little ‘data dramas’ and ‘turf tussles’ surprise me.

    What also continues to surprise me is the lack of marketing participation at many IT gatherings. When I lead a session on how to use big data in marketing, the audience is usually 90% data scientists or IT specialists and 10% marketing. Sunday, in my well-attended session at the Partners event, it was zero marketers. What’s up with that? Where are those savvy ‘data-driven’ marketers?

    On to Monday, where we held a lunch for 30 retailers and CPG firms. Other than the one analyst from IDC, the rest were from companies like Safeway, Target, HEB, Williams & Sonoma, Hallmark, etc. While some work in marketing, none viewed themselves as ‘marketers’ – they were data scientists or IT specialists. Great people, truly interested in Dynamic Customer Strategy, and like Sunday’s session, it went very well. But again, no CMOs, no marketing directors, no merchandisers. Baffling!

    Trend-watchers report that marketing is supposed to be the biggest spender on IT by 2017, outpacing the IT department. Somehow, the CMO is supposed to become the most proficient IT buyer on the planet. So when does the foundational due diligence take place?

    Reading a few white papers or looking at where a particular solution is — in some magic matrix — is not sufficient. Someone thinks, “Oh, we can make money with marketing automation tools here. Let’s get one and get some data and go to work.” And then they demand the data from the data group, or from some other group so they can get their work done without thinking about the greater good. Sharing data always results in the greater good, right?

    Data possessiveness has become the modern tragedy of the commons, a phrase coined to describe the overgrazing that would occur when everyone shared a common pasture (like the Boston commons).

    In this modern-day tragedy, there are two outcomes. First comes technology bloat, and with technology bloat comes lots of little not-playing-well-with-others data sets and an insufficient data strategy. Maybe they can import the data in — but not out.

    In case you are unfamiliar with the term, technology bloat was coined by my former student and now consultant Ben Becker (beckerstrategies.com) to describe the common situation of multiple overlapping software solutions. In environments where data silos and turf battles over applications exist, technology bloat is a huge challenge for IT: Multiple systems to support when one would do, budget-crushing agreements when rationalization would be less expensive, and so on and so on.

    That’s why I found it interesting that Michael Koehler, Teradata’s CEO, emphasized integration as the key watchword for 2015. Integration clearly is a play that works well for Teradata, especially with its full suite of solutions. But when marketing is spending more than IT on IT and doesn’t know how, there’s a tall challenge.

    Another causal factor of technology bloat is ‘how’ marketing budgets and spends funds for IT. The budget to acquire may not even be an IT budget but come out of monies allocated to a particular program or profit center. The campaigns budget, for example, might be used to buy a campaign management tool. As long as revenue targets are hit, all is well from a budgetary perspective, at least as far as marketing management is concerned. Of course, no matter that it’s the third campaign management tool that the organization purchased.

    Similarly, there’s the revenue ownership problem. In spite of attribution modeling that can weight the effectiveness of each element in the marketing mix and apportion revenue accordingly, each profit center is unwilling to share revenue or customers. The result is customers who delete and ignore every marketing message from their former partners who now over-market because they won’t/can’t share data. In my department alone, I know of at least three different CRM systems.

    Moreover, marketers just want to do marketing, and especially the cool marketing. I get that. It’s fun to see marketing strategy actually lead to revenue, whether you’re in B2C and actual sales are immediately triggered or B2B and the work is mostly above the funnel.

    I suspect, though, that the problem is greater in B2B. When we did the study in retailing earlier this year, we were far less likely to identify data ownership as a bottleneck. Retailers are more mature than B2B in the whole data thing anyway, but there are also fewer marketing areas. B2B companies tend to be organized by product or vertical market and each operates as a separate business unit. Marketing departments or teams proliferate, and that leads to technology bloat etc.

    While the simple answer might be that IT should be making more decisions, I don’t see that as realistic. And if Koehler is correct that we’re in for a period of integration, then I suspect that will mean consolidation of tools and applications into suites. What’s interesting to me is that Teradata seems to be the lone voice, even among full-suite providers, crying out to end technology bloat.

    However, I agree that a period of integration is coming. When the CIO can demonstrate to the CMO how integration can improve revenue through better data and marketing strategies while reducing costs (and in that order), most CMOs will make that move.

    Data possessiveness will fade as the benefits of sharing integrated data become ubiquitous and irresistible. Data dramas will cease, and marketers will more enthusiastically participate in IT conferences.

    It’s called teamwork – something Tony Romo totally understands.

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    Dr. Jeff R. Tanner is Professor of Marketing and the Executive Director of Baylor University’s Innovative Business Collaboratory. He regularly speaks at conferences such as CRM Evolution, Teradata Partners, Retail Technology, INFORMS, and others. Author or co-author of 15 books, including his newest, Analytics and Dynamic Customer Strategy, he is an active consultant to organizations such as Lawrence Livermore National Laboratory, Pearson-Prentice Hall, and Cabela’s.

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