Data analysis is one of the most popular fields of information technology today, which can bring significant benefits to enterprises. It is objective to record and analyze the development process of things and predict the future trend through data, but it is more or less affected by some subjective factors, which makes the analysis result meaningless.
Influenced by these factors, data analysts may make some mistakes that lead to deviation between the analysis results and the facts. As a member of the data analysis community, we should be alert to the following common mistakes.
1. The common misunderstanding of data analysis — the purpose of analysis is not clear
Faced with huge amounts of data, we often feel as if we are in the middle of the sea, blind and at a loss. We often struggle with what analysis method to use, what chart to make, what data to need and what form of report to write.
For a project, the first thing to do is to make clear why to do data analysis and what problems to solve according to the needs of the business side, that is, the purpose of analysis. Then, according to the analysis purpose, the analysis framework is built, the analysis methods and specific analysis indicators are selected, and the analysis ideas such as which data are extracted and which charts are used are clarified. Only with a clear understanding of the analysis purpose, can the misunderstanding of analysis for analysis be avoided, and the analysis results and processes become more valuable.
2. A common misconception of data analytics — analytics can eliminate bias
There should be no bias in the way automated systems are executed, but technology is built by humans and is subjective, so eliminating bias is essentially impossible. Algorithms and analyses are tweaked using “training data” and will reproduce any characteristics that the “training data” has, which in some cases can introduce a benign bias in the analysis, but can also lead to more serious bias — because “the algorithm says so” doesn’t mean the answer is fair or useful.
3. A common misconception of data analysis — analysis takes a lot of time
Getting things done quickly — whether it’s getting a product or service to market or responding to customer inquiries in near real time — is an important factor affecting core competence for any business today. Analytics may sound like they take a long time to implement, which runs counter to the goals of speed and agility, but this is still a misconception. We often use BI tools to achieve data analysis, such as Smart BI software. Using BI software can greatly improve efficiency.
4. The common misunderstanding of data analysis — the pursuit of perfect algorithm
Some people hold a stubborn idea when carrying out data analysis, the pursuit of the so-called cutting-edge, advanced, show their technical level of analysis technology, that the more advanced the analysis technology, the better, the more sophisticated the more powerful. Obviously there are ready-made, simple, and very applicable solutions are not adopted, and the time is spent in the pursuit of data algorithms.
There is nothing wrong with pursuing technological progress and development, but we should not always emphasize advanced methods. To save time and resources, and to come up with cost-effective solutions are the working attitudes required by enterprises. Therefore, no matter advanced or low-level methods, as long as they can solve problems, they are good methods.
5. A common mistake in data analysis — over-reliance on data
Overreliance on data, on the one hand, will lead us to do a lot of worthless data analysis; On the other hand, it will limit the inspiration and creativity that the product manager should have. Many good, even great, product decisions are not discovered through data, but rather reflect the collective wisdom of a product manager.