Collaborate 2012-critical success factors for data quality management - ppt
Critical Success factors for Data Quality ManagementPresented By:Austin Davis and Sundu RathinamChainSys Corporation, Lansing MIwww.chainsys.com
Objective of this Paper Informationis currency. Information Quality contributes to that edge by delivering the right information, at the right time, in the right place, to the right people. The right Information can help Inventory Managers keep the supply chain lean, help CEO’s make long-term plans for growth based on accurate and dependable performance measures. Intended Audiences: (i) Individual contributor (ii) Project team member (iii) Project Manager
Key Concepts Data Life Cycle Plan Obtain Store Maintain Apply Dispose Master data Reference Data Transactional Data Meta Data•Customers •Customer type •Sales orders •Table name•Employees •Item type •Purchase orders •Field name•Vendors •Trips •Constraints•Locations •Deliveries •Data types•Organization •Invoices•Accounts •Payments•Products
Data Quality Improvement Cycle 1.Identifyroot Action Assessment 1.Business 1.Prevent Awareness cause Assessment needs future errors 2.Analyse 2.Improvement 2.Correct 3.Assess data plans current data Data quality errors Quality 4.Assess 3.Implement Controls Cycle Business impact Action Awareness
Data Quality Improvement Cycle Six sigma DMAIC Define Measure Analyze Improve Control Prevent Assess Future Data data quality errorsBusiness Analyze Identify Develop Implement Needs Data root cause improvem Controls ent plans Correct Assess current Business data Impact errors
Define Stage Define Business Need Based on: 1. Lost revenue and missed opportunity Analyze Information 2. Unnecessary or excessive 1. Identify data subject to the business costs issue 3. Increased risk 2. Create a detailed data list for the 4. Shared processes and data data of interest 3. Understand the data model
Measure Stage Define Business Need Based on:1. Lost revenue and missed opportunity Analyze Information2. Unnecessary or excessive 1. Identify data subject to the business costs issue3. Increased risk 2. Create a detailed data list for the4. Shared processes and data data of interest 3. Understand the data model
Analyze Stage Assess Data Quality1. Finalize your data capture2. Extract the data into data mart Assess Business Impact3. Profile the data 1. Collect examples of poor data4. Analyze the results 2. Illustrate the effects of poor data5. Duplication 3. Compare potential benefits of investing in data quality with anticipated costs.
Analyze Stage Duplication1. True Match2. Non Match noMatch threshold Match3. False Negative4. False Positive False Negatives False Positives
Improve Stage Prevent future data errors1. Focus on root causes2. Implement improvement plans Correct current data errors3. Improvement should not affect 1. Identify the data to be updated or existing functionalities deleted4. Project Planning and effective 2. Existing functionality should not get communication affected. 3. Use Manual or automated tools to fix 4. Document the change
Control Stage Implement Controls 1. Use the data quality dimensions to control the data quality. Communication 2. Survey knowledgeable business users for 1. Determine who need to be issues in the data. communicated 3. Measure the data quality improvements that 2. Increase the have been implemented. communication 4. Identify next potential area of improvements. 3. Agreement and consensus.