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Data Quality Management

 

Data Quality Management is a fundamental and on-going process, which benefits every business area, and helps to ensure that business information is reliable, complete, consistent, current and correctly interpreted.

Customer Data Quality: In today's competitive environment, smart companies have realised that effective relationships with their existing customers are more important than ever. These companies know that good relationships lead to loyal customers, cross and up-sell opportunities, and better business. However, good relationships and effective marketing depends on good information. In most organisations, however, information about customers is spread across multiple computer systems, spreadsheets and databases, typically in different formats and with varying degrees of accuracy and duplication. To compound these problems, customer data is usually stored and orientated around account numbers or other product or service identifiers, making it difficult to discover useful links, hierarchies or dependencies between customers. Unreliable customer data leads to a "competitive disadvantage", because it is impossible to deliver good customer service using bad customer data. The message is clear: organisations have to start taking more control over capturing, storing and using customer data.

Product Data Quality: In the Manufacturing & Retail industries in recent years, the desire to collaborate on initiatives such as electronic trading has grown in response to the on-going quest for greater efficiencies across supply chains. Whilst there are many highly capable technology options available to support this, there is an often overlooked but critical element that contributes to the success, or failure, of such initiatives: product data. Many organisations have now realised that successful collaboration is directly dependent on the state of the data describing traded goods: product names, categories, dimensions and packaging configurations must all be aligned and synchronised across supply chains in order to eliminate costly rework and to help minimise situations such as product unavailability. Furthermore, smart companies in this sector now know that the achievement of Internal Data Alignment (IDA) and the success of industry initiatives such as those driven by GS1, are reliant on a foundation of high quality and reliable product data.

How can InfoBluePrint help?

 

InfoBluePrint supplies a range of proven services to assist companies to achieve business value through effective Data Quality Management. These can be implemented as individual projects or provided as a holistic Data Quality Improvement Solution designed to address all aspects of Data Quality:

  1. Data Quality Assessments/Health Checks
    1. To ascertain the current status of your Data/Information Quality
    2. Data Profiling and identification of specific errors impacting on quality of data
    3. Quantification of problems causing inaccurate, duplicated, dispersed or missing information
  2. Design a Customised Data Quality Improvement Approach
    1. Identification of short and long term corrective and preventative initiatives to improve the quality of the existing and future data
    2. Implementation of metrics required to track progress of improvement activities
    3. The definition of formally recorded Business Rules for use in data management solutions and consistent application elsewhere across the organisation
    4. Design of processes/systems to categorise, prioritise, action and track improvement activities
    5. Implementation of metrics required to provide ongoing monitoring of the data quality status
    6. Creation of dashboards and appropriate reporting mechanisms to communicate the data quality status, improvement progress, priorities and actions required
  3. Implement Data Quality Improvement Actions
    1. Data Correction - cleansing applied with supporting audit trails and management information
    2. Data De-duplication matching and potential merging of duplicate records/data
    3. Data Enhancement - augment data using other existing data sources within the organisation
    4. Data Enrichment - augment data using data purchased from external service providers.
  4. Monitoring of Data Quality
    1. Creation of ongoing functions and processes to maintain the desired quality targets