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The strength of any data architecture would depend on how strong the data governance process is institutionalized in the organization. Following are some of the aspects of Data Governance process.

Data Quality

The data in the data warehouse is ingested from the data sources in an as-is state. This necessitates the need to ascertain the quality of data used from the data source for downstream usage. The measurement of quality includes;

  • Profiling the data to identify null values, incorrect dimensional values,
  • Outlier values and general statistics to understand mean, minimum, maximum values for measurable attributes in the data
  • In addition, business data value validations are executed on the data. These include checks like value ranges in data, cross field value dependencies are amongst the other validations done on the data
  • Application of Value code mapping rules to standardize the data

Data Transformation​

The data stored in the data warehouse is not necessarily in a usable form since it is stored in the rawest possible form as ingested from the various data sources. To make sense of the data and make it usable in the context of the downstream insights mining and business decision making, it is necessary to transform and aggregate the data. This is done as a part of the aggregation framework that reads data from various data sources and inserts data into a usable format in the data marts designed as per business requirements of downstream usage.

  • Profiling the data to identify null values, incorrect dimensional values,
  • Outlier values and general statistics to understand mean, minimum, maximum values for measurable attributes in the data
  • In addition, business data value validations are executed on the data. These include checks like value ranges in data, cross field value dependencies are amongst the other validations done on the data
  • Application of Value code mapping rules to standardize the data