track of all datasets which would be used for analysis and additional data sources if any are necessary this document can be combined with the subsequent stages of this phase data description data description involves carrying out initial analysis on the data to understand more about the dataits sourcevolumeattributesand relationships once these details are documentedany shortcomings if noted should be informed to relevant personnel the following factors are crucial to building proper data description document data sources (sqlnosqlbig data)record of origin (roo)record of reference(rordata volume (sizenumber of recordstotal databasestablesdata attributes and their description (variablesdata typesrelationship and mapping schemes (understand attribute representationsbasic descriptive statistics (meanmedianvariancefocus on which attributes are important for the business exploratory data analysis exploratory data analysisalso known as edais one of the first major analysis stages in the lifecycle herethe main objective is to explore and understand the data in detail you can make use of descriptive statisticsplotschartsand visualizations to look at the various data attributesfind associations and correlations and make note of data quality problems if any following are some of the major tasks in this stage exploredescribeand visualize data attributes select data and attributes subsets that seem most important for the problem extensive analysis to find correlations and associations and test hypotheses note missing data points if any data quality analysis data quality analysis is the final stage in the data understanding phase where we analyze the quality of data in our datasets and document potential errorsshortcomingsand issues that need to be resolved before analyzing the data further or starting modeling efforts the main focus on data quality analysis involves the following missing values inconsistent values wrong information due to data errors (manual/automatedwrong metadata information