Data is the lifeblood of any organization. In fact, there isn't a single company in the Global 2000 that would be able to function if you removed the data from their applications. However, the vast majority of these companies and large government organizations are grappling with how to properly manage their data.
The state of data management and data assurance can best be classified as abysmal. Most organizations are overrun with needless data redundancy, very poor data quality and minimal data understanding. Often, these same companies need to establish accountability for the information about their business to meet various constraints placed upon their industry by government regulations, international standards or industry-specific codes of practice. These regulations make data and data assurance everyone's business. Examples include: the ACORD Life standard in insurance for consistency in life, annuity and health products; HIPAA in healthcare for accurate identification of private patient information; and Basel II in banking for accurate enterprise risk analysis. In many cases, bad data equals noncompliance.
Data Assurance Stumbling Blocks
Many organizations do attempt some semblance of a data assurance effort. However, there are several reoccurring themes that stymie these efforts, including:
- Lack of a cross-functional, dedicated team for data assurance.
- Not involving the business community.
- Failure to establish a formal data stewardship organ ization and data quality program.
- Lack of an enterprise meta data management program.
- Believing that maintaining the status quo is acceptable.
- Believing that you do not need data standards to achieve accountability.
- Poor planning, lack of upfront understanding and execution.
- Ignoring the enterprise ramifications by creating redundant data silos.
While all data assurance projects have their own unique characteristics, Figure 1 represents the common road map of data assurance events for most data integration initiatives (e.g., data warehousing, master data management, application consolidation, etc.). The six key phases of the data assurance road map used to implement successful data integration projects are:
- Set/Refine Objectives: What are the business objectives?
- Data Assessment: Where are we currently?
- Data Alignment: Where are we going (end state)?
- Data Harmonization: How do we get there?
- Solution Implementation: Implement the processes.
- Continuous Improvement: Feedback to refine and extend.
Figure 1: Data Assurance Road Map
Set/Refine Objectives: The first step is to initially set the business objectives of the proposed solution. This involves the participation of the data assurance team in helping to define the strategic objectives and plan for the entire project. This plan includes the requirements for information, priorities, plans, issues (open and resolved), metrics, sponsorship, terminology, capabilities and limitations. From the IT side, it contains the technical aspects of the information architecture, including the infrastructure standards, the enterprise applications, sources of data and organizational understanding.
Data Assessment: The goal of the data assessment phase is to assess the current state of the data. During this process, business and technical intelligence will be gathered to produce a source business model report. This report maps out source elements, where they are located, how much of the data element exists (number of occurrences) and what they mean. In addition, the report details the data inconsistencies and quality issues in the data sources. This report will help to bring into focus the metrics and standards defined in the set/refine objectives phase and determine suitable benchmarks and processes for the subsequent data alignment, data harmonization and solution implementation phases.
Data Alignment: The data alignment phase identifies the desired "end" state of the organization. It is during this time that the key business users define their specific data assurance rules for each of the relevant data elements that have been targeted by the project. Data alignment is responsible for establishing a defined level of accuracy. For example, when an organization constructs the extract, transform and load (ETL) process of a data warehouse, business users must first define the percentage of errors that they will allow (tolerance levels) for the data warehouse load to be considered of sufficient quality.
Next month, I will conclude this two-part series by walking through the data harmonization, solution implementation and continuous improvement phases of the data assurance road map.1
1. This column is based on the "How to Design and Deliver an Effective Data Assurance Solution" white paper. Please visit www.EWSolutions.com to download a free copy of the paper.
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