An effective foundation for MDM
- Master data management
- Meta data management
- Data integration
- Data Preparation
Seven steps are needed to achieve effective data governance: Stage 1: Establish the order of priority of areas for Improvement
Step 2: Maximize the availability of information assets
Step 3: Create roles, responsibilities and rules. Stage 4: Improve and ensure the integrity of information assets
Step 5: Establish an accountability infrastructure
Step 6: Conversion to a basic Master Data Management culture
Step 7: Develop a feedback mechanism for process improvement
Master Data Management is not just a question of technology. It involves people who are accountable for the business activity by examining the processes they use to interact with the information and how and why it is used. Creating a framework to ensure data confidentiality, quality and integrity – the core elements of a data governance strategy – is essential for meeting internal and external requirements, such as financial reporting, regulatory compliance and policies for the protection of private life. In the best cases, Master Data Management eliminates risks – both at corporate and compliance levels – by increasing monitoring. It enables organizations to integrate and consolidate enterprise information into a single reliable source, providing economies of scale and effectively linking information policy to business strategy.
Stage 1: Establish the order of priority of areas for Improvement
While it may seem ideal to address all an organization’s data issues at the same time, it is much more efficient to start by targeting specific items. The targeted implementation of Master Data Management establishes a solid basis for extending the strategy to the entire company. To start with, it avoids one of the biggest historical problems of Master Data Management: the lack of follow-up. By targeting a specific area of the organization, such as marketing, it is possible to work with the underlying organizational structure to take action and manage monitoring. The information objectives are clearly aligned with the company's strategy. The information architecture is clarified by the inventory of data assets and the evaluation and removal of barriers. Various stakeholders reach consensus and coordinate with each other and identify key data entities and critical data elements and then develop information policies. The first step is objective evaluation of where business improvement can immediately bring the most benefit to the organization. It is essential for this evaluation to be objective, using an outside point of view. Work with an organization that has helped other organizations successfully to implement data governance. One of them examines organizational silos, has a corporate-wide data governance evaluation methodology and knows the key questions for consideration when assessing the starting points of a data governance strategy.
Stage 2: Maximize the availability of information assets
To govern data assets, they must first be available and accessible. Data needs to be holistically examined throughout the organization. If the data is not available, it will hinder the organization's ability to make the most of all data. Information assets come in all shapes and sizes: in transactions, data warehouses, CRM and ERP applications, legacy file structures, partner systems and other external systems. Sometimes these data are available in bulk, in real time or almost real time.
Stage 3: Create roles, responsibilities and rules
Once the information is accessible, the organization must determine who is doing what, it must create roles, responsibilities and rules for the processes that people will use to work on that data. The first step is to understand the data itself. The best area to start with is sales. Indeed, sales managers understand the business and generally know the data available. On the other hand, they are able to identify the elements of data that is incorrect or inconsistent or even of no use to the organization. With their help, an organization can identify the rules for collecting and sorting vital business data. These rules are then forwarded to the IT department to create a cleaning technology. They then improve the data by applying data standardization rules, deduplicating the data if necessary and enriching it with any additional information before it is transmitted to the target system. This type of cooperation is essential. Without this cooperation, no matter what technology or amount of money is spent, any data governance initiative will fail miserably. Each member of the organization must be made accountable for ensuring that the data is qualified and up-to-date. A data governance framework must meet the needs of all participants and all participants must work together to ensure data integrity.
Stage 4: Improve and ensure data integrity
Once roles, responsibilities, and rules are established, ensure that the information enables continuous improvement and assurance of the integrity of the information assets in a four-step process:
- Data analysis and standardization,
- Data enrichment,
- Data monitoring.
The ability to implement this four-step process in real time, with data from any system, is critical to ensuring data integrity.
Stage 5: Establish an accountability infrastructure
Even with all the processes in place to ensure the integrity of the information, some questions remain unresolved:
- What happens if the information is still inaccurate?
- What happens to these data elements?
- What data still passes through the filters of automated processes?
The processes alone do not ensure the integrity of the information. The teams must be the guarantors of this. Establishing an accountability infrastructure is therefore the key to good Master Data Management.
Stage 6: Conversion to a basic Master Data Management culture
With the teams, processes and technology in place to ensure data integrity, the next step towards quality Master Data Management is to change the culture of the organization so that it is based on master data rather than on basic data.
Stage 7: Develop a feedback mechanism for process improvement
By following the steps above, you will quickly begin to identify the important information more clearly or to master and lead your business in that direction. However, the process is a cycle and there is always room for improvement. A continuous feedback mechanism must be built into the process. Indeed, monitoring information assets over time provides a clear picture of how initiatives are being implemented. It provides a way to represent the successes and failures of the process graphically. It therefore becomes possible to correct failures very quickly. The success of Master Data Management ultimately depends on the teams: when people know their role and the rules and are supported by effective technology, it is easy for them to do their job and the data governance strategy works. Master Data Management in today’s strategic and commercial environment. Internal or external risk management requirements make it essential to make a single version of the truth available. Unfortunately, the proliferation of data and new technologies make this task difficult to achieve. Master Data Management is a safe response to this problem. It gives you the power to unite your organization's goals, technology initiatives and internal information management policy. A good master data governance strategy means that all stakeholders can see the same version of the truth and ensure the veracity of that truth. Although the task may seem daunting and expensive, it does not have to be. The practical and ROI-focused approach proposed in this article allows for immediate, measurable improvements in every organization. Do not hesitate to contact our teams of Master Data Management consultants so that you can set up a data governance strategy.