The projects on analytics, as much as it can be successful, can also fail at times, that is they might fail to provide you the insights you look. And to make an analytic project strong, various factors has to be taken into consideration, the key being avoiding or eliminating the risk of failure. To do that, one has to know why they fail. So here are some top reasons for an analytic project to fail.
- Starting point being data: A very common mistake that many business owners do is expecting the data to speak for itself. Staring at the various dashboards will not provide you with the insight that you want. Instead, focus on the question or the issue for which you are looking for a solution. To give an example, if you are seeking to improve site conversion, rather than gazing upon volumes of data, you can just search for methods to improve your conversion from say 20% to 25%. This will narrow down the search and helps you to focus better.
- Do not over explore: Another common mistake is after identifying the question, people work on volumes of data, both wanted and irrelevant, trying to find the insight for solving it. While this might provide you with a solution, the process itself is too lengthy and time-consuming. Instead, it would help to use hypothesis and narrow the search area down so that your results are quicker. Once you have the results, you can then address them to improve your business.
- The absence of a stronger hypothesis: You will still be facing failure if your theory, despite the question and the approach structured, is not high enough. Without the right and the strong hypothesis, you will be barking at the wrong tree without any fruitful result.
- Absent or negligent stakeholders: When an analytic project is successful, it uses for delivering results regarding customer experience and producing the desired results in business. However, to make this happen, you would need the active involvement of all the concerned stakeholders in the firm. Without the right speakers sharing their views, you will again end up with a wrong or weak hypothesis, exploring massive volumes of data without any delightful insight.
- Bad data: This is the worst case scenario, where despite you following a structured approach, you end up with a failed analytics project, merely because the data you were using was not reliable. While analytics projects do not command for a perfect data set, you do need a certain amount of maturity in the data to have a successful venture in the project.