Data Quality

Any dashboard, scorecard or a report will be reduced to a NIL value, if the audience don't trust the data contained therein. In today's world, data is going exponentially complex and the maze of system & interfaces is reaching "beyond visual range". With supply chain management, CRM and other practices, the enterprise boundaries of data are blurring. The regulatory & disclosure pressures are mounting. Its time that organizations re-in-force their focus on Data Quality. Data quality has different connotations and follow the conventional principles of prevention, monitoring and remedy. Data Quality does not seek perfection, but business-case driven sponsorship.

Chapters In Data Quality : -

Data Quality Program

In the previous chapters, you have gone through various aspects of Data Quality. Lets see on how to implement them. Data Quality program in a company is a combination of one time initiatives and a continuous, fairly business as usual set of people, process and technology changes which drive an acceptable level of data sanity. This can be at functional level (generally) or at enterprise level (rarely), and it can sweep across business and technology worlds (rarely) or is limited to one of them, with some cascading impact on the other (generally).

Data Quality Overview

Before one gets onto working on data quality, one has to appreciate \n on what is data quality?, why is it so important? and what are the \n reasons for data quality failures?

Data Quality Assurance and monitoring

Prevention is better than cure. Quality can be much assured by \n pro-active assurance controls, while designing your systems and \n processes. Avoid bad data through interface controls, data standards, \n data models, database & Data processing and business controls.

Customer Data Quality for Customer Relationship Management

CRM is one of the most important customers of Data Quality. Organizations \n have improved much more on transaction processing data quality in \n comparison to Customer related data Quality. Let's look at Customer \n Data quality issues, Customer data matching, de-duping, data augmentation \n and enrichment.\n

Data Mapping & Assessment

Data Mapping & Assessment (DMA) is a comprehensive assessment of the state of Data in an environment. This includes assessment of data quality, Data Profiling and Data Flow Analysis. The application of DMA exercise is found for Data Quality Program, Data Conversion, Data Warehousing and wherever one needs to analyze the existing state of Data.