Data Quality & Governance

With the increasing complexity of data, globalization and the need for real-time accurate information Data Quality plays a very important role in any organization IT initiative. As the saying goes “A clean engine produces better power”, a repository of clean data will give more power to the decision makers of any organization.

Toxic data enters the organizations data arena in many ways. Poor data entry, a poor data verification process, No common metadata and Defective program logic are only a few of the ways. To detoxify the data assets, a complex collaboration of various departments and preventive measures must be taken to restrict further infiltration of toxic data.

Data quality does not happen by accident. Organizations must establish standards and guidelines for all personnel to follow to ensure that data quality is addressed during the entire lifecycle of a system. For example, standards should be established for defining the data, naming the data, establishing domains and business rules, and modeling the data. Guidelines should be in place for data entry, edit checking, validating and auditing of data, correcting data errors, and removing the root causes of data contamination. Training and familiarization with the standards and guidelines should be required of all data entry staff, developers, data stewards and information consumers.

Standards and Guidelines should also include policies and procedures, such as operating procedures, change-control procedures, issue management procedures, and data dispute resolution procedures. Additional policies and procedures should be considered for the communication processes, estimating guidelines, roles and responsibilities, and standard documentation formats.

Scalable Systems Data Quality services can help your organization by delivering accurate, trusted data using latest data quality tools and various data quality methods such as data profiling, data validation and data cleansing etc.

Scalable offers services in Data Quality including but not limited to…

Data Quality Analysis and Data Profiling
Enterprise Data Governance and Data Quality Setup
Data Standardization, Data Anomaly Check, Data Sampling, Data Quality Checks, Data
   Validation, Data Scrubbing/Cleansing, Deduping and Merge/Purge
Data Quality Reporting and Monitoring