He has over 15 years in quality of the data checking for time to market on critical timeline. Data quality assessment for several ways, using different types of UTA - User Technologies Analyses: frequency counts, descriptive statistics (mean, standard deviation, median), normality (kurtosis, frequency histograms, normal probability plots), associations (correlations, scatter plots). He designs and develops business and systems documentation including graphical representations to clarify materials as appropriate such workflows and updating organizational charts as necessary for Claims Connect project. Researches procedures and interviews Subject Matter Experts to solicit documentation content. Creates End-to-End business and systems procedures from both existing data as well as non-existent data. Edits and analyzes the written content from other departments with accountability to Claims Administration and provides constructive feedback.Other initial DQC - data quality checks are:• Checks on data cleaning: have decisions influenced the distribution of the variables. The distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data• Analysis of missing observations: divide and concur missing values, and are the values missing at random/variances The missing observations in the data are analyzed to see whether more than 25% of the values are missing, whether they are missing at random (MAR), and whether some form of imputation is needed• Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution• Coordinating and analyze deliverables to meet team direction for allocated projects• Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set