One service that’s critical but most companies never think about is Data itself. Before we can use data for Reporting, Visualization or Data Analysis it has to be accurate or what we call clean. That’s where Data Cleansing comes to play. Your data could have duplicate values or it could have inaccurate data types. For example, values that are supposed to represent currency may have extraneous text fields. There may be what’s known as outliers which means data that’s so far off it’s both unrealistic and would skew the analysis. For example, maybe the price of a loaf of bread from you Point of Sale Data said it cost $1,000. You’ll probably want to drop that record. It is also critical to define Meta Data or Data about Data, like a Data Dictionary. It defines what data types you have and more important what they really mean. We are here to help with all of your data needs.

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