{"id":114497,"date":"2015-11-02T16:55:00","date_gmt":"2015-11-02T21:55:00","guid":{"rendered":"https:\/\/www.dacgroup.com\/data-driven-business-a-roundup-from-the-major-data-analytics-conference\/"},"modified":"2024-12-31T06:05:16","modified_gmt":"2024-12-31T11:05:16","slug":"data-driven-business-a-roundup-from-the-major-data-analytics-conference","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-gb\/insights\/blog\/analytics\/data-driven-business-a-roundup-from-the-major-data-analytics-conference\/","title":{"rendered":"Data Driven Business \u2013 A roundup from the major data analytics conference"},"content":{"rendered":"

Last week I went to the Data Driven Business conferences, which are essentially eMetrics, Conversion Conference, and Predictive Analytics World all at the same venue in London.<\/p>\n

In terms of analytics maturity, I would say that eMetrics was more intended to deliver the business value of data analytics to a wide audience, Conversion Conference was more focused on discovering particular tools and techniques for analytics processes, and Predictive Analytics world was the highest maturity stage (so far) in the art and science of data analytics.<\/p>\n

Or, in other words, a bunch of introverts from all over the globe who are quiet and friendly at the lunch queue but would speak loudly and boldly about their grains of data enlightenment. In terms of personas, corresponding target audiences would look like this:<\/p>\n

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\"eMetrics:

eMetrics<\/strong>: anxious beginners & relaxed experts<\/p><\/div>\n

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Conversion Conference<\/strong>: on-site conversion tacticians<\/p><\/div>\n

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Predictive Analytics World<\/strong>: VP, CEO, CFO, Head of Data \/ Analytics \/ BI<\/p><\/div>\n

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Summary<\/strong><\/h3>\n

Whilst constantly absorbing the knowledge from all three domains of data and analytics (web analytics, UX & conversion rate optimization and predictive analytics), I have come up with this business process of data & analytics (click any to skip straight there):<\/p>\n

    \n
  1. Problem recognition<\/a><\/li>\n
  2. Data collection<\/a><\/li>\n
  3. Data analysis & Experiments<\/a><\/li>\n
  4. Data modelling<\/a><\/li>\n<\/ol>\n

    \"process
    \n<\/span><\/p>\n

      \n
    1. Problem recognition or The right question stage.<\/li>\n<\/ol>\n

      First comes problem recognition. It requires as much effort as any other stage if not more. You need to ask the right question. Full stop.<\/p>\n

      Here the debate as to what is more important, domain knowledge or data science skills, is not relevant. Domain knowledge is the king when you need to ask the right question. We need to sit down with a client or, even better, spend a few days together and drill down as deep as needed to reveal the initial source of the pain.<\/p>\n

      What if you skip through the problem recognition stage and come up with a wrong question? The cost of error will be as much as the time spent coming to realise that the question was wrong. Doing what matters is called efficiency and a seemingly \u2018easy\u2019 job is usually a trap.<\/p>\n

      For more, check out Dean Abbot’s<\/a> retrospective but not less valuable speech about his Five pet peeves of predictive analytics<\/a>.<\/p>\n

      Please refer to the next presentations for a high-level view on the value of data driven business:<\/p>\n