How to reuse enterprise data - Part 5 of 6

Case Studies
Scyllogis Consulting have been helping customers within the Insurance sector continue to achieve significantly higher levels of business performance from their data management programmes and information systems since 2001. Read how we have worked with some of these customers to achieve significant business results across the world, in our case studies. Read more...

Consulting Expertise
Insurance organisations today are no more effective at delivering on large-scale data management initiatives than they were 10 years ago. In a recent survey, 70% of the companies said their data management initiatives did not deliver the expected results. That success rate was unchanged from similar surveys conducted in the 1990's. And the environment for data management is only getting more complex..... Read more

Consulting Expertise

Insurance organisations today are no more effective at delivering on large-scale data management initiatives than they were 10 years ago. In a recent survey, 70% of the companies said their data management initiatives did not deliver the expected results. That success rate was unchanged from similar surveys conducted in the 1990's. And the environment for data management is only getting more complex.....

Read more

Our People
At Scyllogis Consulting all of our consultants have significant experience gained from within the Insurance market. Our people and our culture are our greatest assets. We only select people with relevant experience, intelligence, integrity, passion and the ambition to make a mark and deliver to our Customers the Scyllogis brand values of practical, results based consultancy. Our Consultants are pragmatic and open minded. That is why we deliver solutions that others dont..... Read More

Our People

At Scyllogis Consulting all of our consultants have significant experience gained from within the Insurance market. Our people and our culture are our greatest assets. We only select people with relevant experience, intelligence, integrity, passion and the ambition to make a mark and deliver to our Customers the Scyllogis brand values of practical, results based consultancy. Our Consultants are pragmatic and open minded. That is why we deliver solutions that others dont..... Read More

How to reuse enterprise data - Part 5 of 6

Written by Colin Whickman
Wednesday, 25 January 2012
Master Data Management for Transparency and Visibility Data integration establishes availability. This availability is enhanced with transparency and visibility, which are enabled by the techniques that usually comprise an effective master data management practice. Master data management is intended to enable the development of an accurate and reliable view of business entities, common reference data concepts, and the dimensional data that are vital to the operations of the enterprise, among a variety of other potential master data concepts. Essentially, those data sets that Exemplify common data concepts, Maintain replicated data elements, Are subject to multiple business purposes, and Can be used by multiple applications can be considered master data. Master data provides either a persistent repository or a (potentially virtual) registry or index of uniquely identified entities with their critical data attributes synchronized from the contributing original data sources. With the proper governance and oversight, the data in the master data system (or repository, or registry) can be qualified as a unified and coherent data asset that all applications can rely on for consistent high quality information. Master data management is a collection of data management best practices associated with both the technical oversight and the governance requirements for facilitating the sharing of commonly used master data concepts. MDM incorporates the people, policies, procedures, and technology to orchestrate key stakeholders, participants, and business clients in managing business applications, information management methods, and data management tools. Master data management tools support an organization’s business needs by allowing business modelers to develop a standardized view of the uniquely identifiable master data entities across the enterprise application infrastructure along with their corresponding master data services. Master data management governs the methods, tools, information, and services to: Identify core “business-relevant” data concepts used in different application data; Assess the use of commonly used data concepts and valid commonly-used data domains; Create a standardized model for integrating and managing those master data concepts; Manage collected and discovered metadata as an accessible, browsable resource, and using it to facilitate consolidation; Collect data from originating data sources, and evaluate how different data instances refer to the same real-world entities, and facilitate a unified view of each real-world entity; Enforce roles based access controls to secure and provision the master data; and Establish common master data services to maintain consistent transactions across the collection of data consumers. Data Quality Management for Reliability and Consistency In the context of enterprise data utilization, operational processes for data quality management incorporate best practices with tools and technologies to can ensure reliability and consistency, such as: Defining Data Validity Rules – Rules to measure compliance with identified data quality expectations are used as data controls whose implementation is incorporated directly into the application development process so that data errors can be identified and addressed as they occur. Defining Acceptability Thresholds – Acceptability threshold scores can be defined, and measured scores below the acceptability threshold indicates that the data does not meet business expectations. Defining Data Quality Metrics and Thresholds – Data quality analysts can work with business data consumers to define data quality metrics to baseline and then continuously inspect and monitor levels of data quality. Inspection and Monitoring – Data quality rules are used for data quality inspection, monitoring, and notifying the appropriate people when data quality issues requiring remediation are identified. Data Quality Incident and Performance Reporting – This is a set of management processes for the reporting and tracking the status of data quality issues and corresponding activities. Managing Data Remediation – This provides the mechanism for remedying data issues, including triage, classification, prioritization, and preparation for root cause analysis. Root Cause Analysis – This set of processes is used to isolate the location in which errors are introduced to enable drill down into the data and identify the root cause. Data Correction – This remediation approach is a governed process for correcting data to meet acceptability thresholds when the source of the errors cannot be fixed. Data quality technology such as data profiling, parsing and standardization, cleansing, identity resolution, and data enhancement can be used to support the analysis, documentation, and inspection and monitoring of adherence to enterprise data quality expectations as well as taking the proper corrective measures when data flaws need immediate attention. See my conclusion next week…
Last Updated ( Wednesday, 08 February 2012 )