Monthly Archives: December 2015

Top 5 Reasons Why Analytics Projects Fail

Whether you are a seasoned analyst or a business executive with a significant investment in analytics, chances are you’ve seen the powerful impact of analytics as well as the failures. So what drives an analytics project’s failure or success?

Analytics projects fail when they produce no actionable insights. Even a seasoned analyst’s efforts to coax insights from the data can be futile unless a number of key factors come together to increase the odds of success.

Let’s talk about the top 5 reasons analytics fail.

  1. Starting with data instead of the question. The most common misunderstanding about analytics is that if you look at data hard enough, you will find insights. Staring at daily dashboards in the hope that insights will miraculously reveal themselves is often overwhelming, confusing and unsuccessful.Successful analytics start by identifying the question you’re trying to answer from the data. For example, if site conversion is an issue, instead of studying your website data hoping to find reasons for low conversion, narrow down your efforts to a specific question. In this case, it might be “How can we increase conversion from 23% to 26%?” This approach allows you to focus on finding actionable drivers of conversion that can have impact.
  1. An exploratory approach to analytics. Once you have identified the question you are trying to answer, do you explore all the data at hand in the hopes of finding insights or do you identify which data to study by using hypotheses as guard rails?The exploratory approach often fails to find any insights and if it does, is a lengthy process. On the other hand, using hypotheses to narrow down both the scope of the project and the data set needed, leads to the answers quickly. This process also generates secondary questions to ask data to further refine the insights.In our example, the hypotheses might involve certain pages or experiences thought to be driving lower conversion. These hypotheses are then used to identify the data needed to find segments of low conversion, and, once proven, address them.
Source: http://www.forbes.com/sites/piyankajain/2015/12/12/5-reasons-why-analytics-projects-fail/
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