the data science process different units of measurement when integrating two data setsyou have to pay attention to their respective units of measurement an example of this would be when you study the prices of gasoline in the world to do this you gather data from different data providers data sets can contain prices per gallon and others can contain prices per liter simple conversion will do the trick in this case different levels of aggregation having different levels of aggregation is similar to having different types of measurement an example of this would be data set containing data per week versus one containing data per work week this type of error is generally easy to detectand summarizing (or the inverseexpandingthe data sets will fix it after cleaning the data errorsyou combine information from different data sources but before we tackle this topic we'll take little detour and stress the importance of cleaning data as early as possible correct errors as early as possible good practice is to mediate data errors as early as possible in the data collection chain and to fix as little as possible inside your program while fixing the origin of the problem retrieving data is difficult taskand organizations spend millions of dollars on it in the hope of making better decisions the data collection process is errorproneand in big organization it involves many steps and teams data should be cleansed when acquired for many reasonsnot everyone spots the data anomalies decision-makers may make costly mistakes on information based on incorrect data from applications that fail to correct for the faulty data if errors are not corrected early on in the processthe cleansing will have to be done for every project that uses that data data errors may point to business process that isn' working as designed for instanceboth authors worked at retailer in the pastand they designed couponing system to attract more people and make higher profit during data science projectwe discovered clients who abused the couponing system and earned money while purchasing groceries the goal of the couponing system was to stimulate cross-sellingnot to give products away for free this flaw cost the company money and nobody in the company was aware of it in this case the data wasn' technically wrong but came with unexpected results data errors may point to defective equipmentsuch as broken transmission lines and defective sensors data errors can point to bugs in software or in the integration of software that may be critical to the company while doing small project at bank we discovered that two software applications used different local settings this caused problems with numbers greater than , for one app the number meant oneand for the other it meant one thousand