Even with the same set of data, two people can draw completely different conclusions. This is because the data themselves are not “reference”. Researchers can derive data from incorrect information, rely on subjective judgment or use sources of dubious origin
Here are 6 common mistakes that managers make when they work with Big data.
1. Lack of a clear goal
If you do not clearly set a goal, you will not know what you need to collect. Most likely, you will collect incorrect or incomplete data. In large data, there is a common tendency when companies collect tons of information, not understanding why they need it and how to use it.
2. Definition error
Let’s imagine that you want to know how many customers have spent on your services for the past quarter. It would seem an easy task, right? Unfortunately, even such simple goals require an accurate definition of concepts.
First, how do you define the “client”? You do not want to cut one size fits all. You, most likely, plan to segment clients according to their buying behavior, in order to build a marketing model accordingly. In this case, you will need to make sure that you take into account important customer information
3. Data capture error
Once you have determined the type of data that you would like to collect, you need to develop a mechanism for capturing them. In case of an error, you can collect incorrect data.
4. Measurement error
These errors occur when something goes wrong in the software or hardware that you use to capture data.
5. Processing error
As you already understood, many errors occur even before you look at the data. Most enterprises that own the data have long been out of date, and the original team that could explain their decisions has disintegrated. Many of their assumptions are most often not documented. So, you have to make your own conclusions, and this is not an easy task.
6. Failure to collect
This error occurs when you analyze the data of only a small group of people, and this is not enough to draw a clear picture. The conclusions you make will most likely turn out to be wrong – they will not apply to the entire target audience.
Along with the exact mistakes, there are those that we cannot explain.
Every leader should know about these nine errors, but remember that besides them there are a huge number of problems that can interfere with the effectiveness of AI technologies.
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