Data must have established standards, rules and must be created via a designed workflow and validation process to ensure “consistency for consumption” across your application landscape. Experience shows that silo-based data creation is not serving the requirements of the enterprise. Ideally data will be cleansed, consolidated, standardized, duplicates verified, and enriched with content at the point of creation.
The enforcement of business rules related to data is an essential element in the data transformation process. Data cleansing routines which are based on data quality standards and the organization’s business rules can be reused time and time again to deliver real-time data governance (RTDG) – ensuring data quality at the point of creation.
By following this path, an automated process can be established. The first step in an effective methodology is to profile and analyze the master data against a number of quality criteria (e.g. completeness, conformity to standards, duplicates, etc.) and report against the data’s integrity.
The next step is through a series of workshops and interviews with the business teams, capture, document, and validate the organization’s business rules. Regional and global rules would be documented as part of the process.
Just the like data integrity was profiled, the company’s business rules would be measured based on enforcement. Just because there is a rule, does not mean it is followed. The rules and standards are then mapped into a data-flow for the creation of the data objects in scope.
Example: Business Rule Conformity
A best practices approach would be for data to be cleansed, normalized to a standard taxonomy, and then imported into the central MDM repository as a “single version of the truth.” By ensuring data standards, business rules and policies are enforced (governed) at the point of creation, the ERP system will receive the right data in the right format for business consumption. This allows for creation, consolidation, harmonization, and syndication of master data for use across heterogeneous landscapes.
The initial task of data cleansing has limited value unless it is followed by business processes that ensure the future integrity of the data. The data processes promoted by industry leaders will help companies’ establish an enterprise data management (EDM) environment. EDM is the overarching and orchestrated processes for data: architecture, standards, governance, quality, and change management. EDM’s key assumption is that data is a business asset that intersects and pulls together data throughout the people, processes, and technology of an enterprise. This data must be designed, used, and governed to add maximum value to organizations. Achieving an optimized landscape demands that people, processes, and information are all in sync, and this includes your extended supply chain and business partners.
One very effective way to sustain the EDM process for the long term is to ensure organizational change management is incorporated into the strategy. As organizations are compelled to meet their goals, understanding the affect and impact of poor data against a goal’s achievement is a key to getting the organization on board. One should outline the business rule and then illustrate the business impact if not applied.
Learn more about how organizations are harnessing their data management by downloading our complimentary whitepaper “Real Time Data Governance for MDM Best Practices”.