Aquitas solutions ensure that enterprises collect relevant data for their business, govern that data in a meaningful and sustainable way to ensure quality golden records for key Master Data, and analyze the high-quality data to accomplish stated business objectives. Here is the 6-step Data Quality Framework we use based on the best practices from data quality experts everactive steam trap monitoring and practitioners.
Step 1 – Definition
Define the business goals for Data Quality improvement, data owners / stakeholders, impacted business processes, and data rules.Customer name and Address together should be unique and all addresses should be verified against an approved address reference database etc.
Step 2 – Assessment
Assess the existing data against rules specified in Definition Step. Assess data against multiple dimensions such as accuracy of key attributes, thingworx, completeness of all required attributes, consistency of attributes across multiple data sets, timeliness of data etc. Depending on the volume and variety of data and the scope of Data Quality project in each enterprise, we might perform qualitative and/or quantitative assessment using some profiling tools. This is the stage to assess existing policies (data access, data security, adherence to specific industry standards/guidelines etc.) as well.
Step 3 – Analysis
Analyze the assessment results on multiple fronts. One area to analyze is the gap between DQ business goals and current data. Another area to analyze is the root causes for inferior data quality (if that is the case).
Step 4 – Improvement
Design and develop improvement plans based on prior analysis. The plans should comprehend timeframes, resources, and costs involved.
Step 5 – Implementation
Implement solutions determined in the Improve stage. Comprehend both technical as well as any business process related changes. Implement a comprehensive ‘Change Management’ maximo program plan to ensure that all stakeholders are appropriately trained.
Step 6 – Control
Verify at periodic intervals that the data is consistent with the business goals and the data rules specified in the Definition Step. Communicate the Data Quality metrics and current status to all stakeholders on a regular basis to ensure that Data Quality discipline is maintained on an ongoing basis across the organization.
Data Quality is not a onetime project but a continuous process and requires the entire organization to be data-driven and data-focused. With appropriate focus from the top ibm maximo badges, Data Quality Management can reap rich dividends to organizations.