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Author: Admin | 2025-04-28
Data science and data mining are hot topics in the industry. Companies can’t seem to hire enough people to crunch their numbers and do their analytics.Harvard Business School even calls data science the sexiest job of the 21st century!While there is a ton of good in data mining, some major issues are still present in 2022.In this 6-minute read, we go over this complete, and comprehensive list of major issues industry leaders in data mining keep screaming about.Most Data Is Messy DataMissing Data And Its Effects On SolutionsDealing With Distributed DataDifferent levels of data securityExpensive And Timely Data UpkeepData Science is A Newer FieldFast Paced industryUnderstanding The Business ContextDealing With “People” ProblemsAlways a new algorithmNavigating Initial AssumptionsAmount of Knowledge NeededScalability of Good SolutionsProduction vs. TrainingModel Drift And Its Effects On BusinessResult evaluationKnowing How To Correctly Deliver Your ResultsThere Is Always Changing requirementsBudget Seems Smaller in Data MiningProving a positive Return on investment (ROI)1. Most Data Is Messy DataMost of your data mining projects are going to start with messy data.While most analytical professionals love the modeling part of their job, they spend about 80% of their time cleaning messy data.This trend doesn’t seem to be subduing either.The data mining process continues to get more in-depth, with new and innovative approaches coming out every day.While we wish this increase in depths came with a decrease in messy data – it doesn’t.Data continues to be messy – as a data scientist, you’ll need to get used to jumbled columns, weird date formats, non-standard units, and a thousand other data issues that make will make your datasets messy. 2. Missing Data And Its Effects On SolutionsAnother major problem in data mining is missing data.While we know above that data is usually messy, what happens when it’s missing?Missing data impacts your analysis, biases models,
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