Do You Trust Your Analytics?
Numbers can be misinterpreted if the full context is not understood. Leading you to think, do you really trust your analytics? However, sometimes the metric is just not adapted.
Remember, user behaviors vary from depending on the device used, and so should the metrics. Clean data is absolutely essential, but all too often, companies lack trust in that critical resource. Regardless of how good your analytics is, you’re not getting anywhere if you can’t trust your data. The goal for many companies when constructing predictive analytics is to get the data as clean as possible. But even if the data is 100% true, your model may give the wrong predictions. The importance of training data is easily overlooked.
Companies that don’t have a plan for how they’re going to train data at scale don’t have much of a shot at succeeding at. 90% of artificial intelligence projects fail due to a lack of good or appropriate training data. It all comes back to training data and the quality of your training data. If there’s bias in the training data or there’s missing data in the training data. Not all data is equal. Some data needs to be governed closely and transformed into pristine values