Data Quality

We can help….

The cornerstone of sound business decision making revolves around having access to accurate and timely information. Without it business leaders are left with intuition, prior experience and little else.

Accuracy, completeness, consistency and timeliness are the chief measures of data quality. Our data quality service is centered on improving these metrics so that the data promotes optimal performance of the business system and support user faith in the systems reliability.

We have helped our clients achieve data quality by setting in order their jumbled customer information. We have also identified and assessed data quality problems like matching sales figures between the data warehouse and source systems.

We can ensure the ongoing accuracy and integrity of your data by:

• Parsing, cleansing, standardizing and linking data from different systems such as CRM, ERP and other legacy sources or 3rd party databases
• Maintaining clean customer data i.e. De-duplication of customer data, verifying email addresses and other contact details
• Checking whether the data in your analytical system is accurate and consistent with those in you transaction system. For example, checking product inventory figures between your data warehouse and inventory system
• Ensuring attribute, column and relationship completeness of your data. For eg. We can identify if customer phone numbers are missing in most of your records and recommend corrective measures
• Auditing your ETL log and user complaint of data problems, ensure effectiveness of existing data quality rules and formulate new data quality procedures

• Understanding business need and data infrastructure
• Receive data quality requirement from business
• Agree on approach to validate data quality improvement

1. 1. Define – set data quality standard and expectation
   2. Measure – identify current data quality level and assess gap
   3. Analyse – analyse root cause for data quality problem
   4. Improve – provide solution for data quality problem and monitor effectiveness

We first identify major analytical requirement such as:

• Sales revenue
• Production cost
• Marketing cost
• Inventory level
• Customer satisfaction
• Operator efficiency etc
• Identify factors, such as customers, products, and locations affecting each metric and rank them by criticality

We run the data quality check for each data quality dimension. We then:

• Quantify the deviation and variance
• Categorize the deviation and variance for severity and highlight the impact

We find the root-cause for each deviation and

• Group the root-cause based on origination and isolation level
• Arrive at problem statement
• Rank problems based on estimated data quality improvement for each one

For each isolated problem, we list the possible solutions and choose the best one based on feasibility. We look for a solution that:

• Is least intrusive to the user and source system
• Help system performance
• Can address multiple problems
• Is predictive in nature
• Is adaptable and extensible for newer requirement
• We continuously track data quality improvement and assess solution effectiveness

• Clean, accurate and usable data for your business
• Improve company wide decision making with better data quality
• Compliance with regulatory policies
• Build valuable data asset for reporting, analysis, & mining
• Optimize operational efficiency with an ongoing data quality process
• Reduce the cost of doing business through more efficient applications