The growing pool of data scientists, advances in collective intelligence, new data science tools, and emerging consulting teams can’t possibly eliminate few critical data science mistakes. The only way to avoid these mistakes is to constantly identify them and come up with creative solutions. Find below the common data science mistakes the organisations make.

Poor data quality
A huge volume of data can be messy and organising it is a tedious task. The ideal way to avoid poor quality data is to avoid manual data entry whenever possible and use tools that reduce the proliferation of typographical errors, alternate spellings, and individual idiosyncrasies. The data scientists should keep the process from the beginning by ensuring thorough implementation of data, the absence of which can jeopardize the quality of the results.

Too much data
Too much data causes a host of problems that prevent meaningful progress. The unnecessary data is the result of collecting too much data that is unrelated to reaching the goal during predictive analysis. Reduce features and employing data selection techniques such as PCA and penalization methods to eliminate the noise and get what matters most.

Overpromising what data science can deliver
Data science is a highly advanced analysis of large data sets in search of unique and actionable insights. Before deriving the true value, the data needs to be refined, cleansed, restructured and even combined with other data sources. Many organisations fail to understand this due to which expectations often go unmet.