A variety of reasons may cause your data to fail. Here we listed several common situations for you to refer to. After learning these reasons, you can know what can you do when there is a data failure easier.
Creating data models and definitions that accurately and adequately reflect that reality is the first challenge in the data journey, and therefore the first place where problems can arise. If the entities and their relationships are not well defined in the data model, the data cannot be fit for purpose. The word "right" in this case includes observing and understanding modeled reality and using data.
Additionally, if you are providing data at an inappropriate level of granularity for your use case, data failure may occur. While it may be tempting to elaborate on bugs, doing so can complicate implementation and process steps.
Assuming the data specification is a beneficial representation of reality, the approach to implementation can still let us down. Typically, this is the point where data specifications meet system and process specifications.
Once you have assembled your implementation, fire it up and watch the various elements come together like a well-oiled machine. However, data may still be missing. This is a deviation from the specification and may be systematically detected if there is a reliable indicator of its absence. Additionally, the data may simply be wrong. These are very difficult to detect and, in some circumstances, can have zero to fatal consequences depending on how the data is used.
The most obvious way to go wrong in data analysis is simply doing the math wrong. When you use spreadsheets to analyze data, it's far more likely that you're wrong than you get it right.
Data analysis and interpretation fail to understand the reality that the data represents and the context in which that reality exists. This is not a data issue, it's a domain knowledge issue. A subtle twist to the previous error is a failure to understand how data specifications and data implementations represent that reality. Both areas require assumptions and interpretations that, if not fully understood, jeopardize the data-to-information path. This often leads to conclusions drawn from the data that do not support the data.
That’s the general reason that may cause your data to fail. So, is there an effective way to fix it? Read the solution and the prevention methods in the following.
A data failure means that the information needs to be run again. If the data fails, it means there may have been an error. Running the data again may resolve the error. Moreover, you’d better back up crucial data on different platforms and devices to avoid data loss. To fix the error exactly, you can try the sophisticated PC and mobile testing tool, WeTest. It integrates amazing tools such as automated testing, functional testing, remote device testing, performance testing, etc. Using it, you can detect PC errors and problems on other devices, and further improve your work as well as entertainment.
This guide targets what can you do when there is a data failure. In summary, to avoid your important data failure, you might as well back it up in advance. Furthermore, using professional PC, mobile, and software-detecting software, WeTest is also a great option. It can help you detect errors regularly and further prevent data failure issues.