Ten Commandments for MDM Implementation
Prabuddha Roy
Without wanting to labour the point about what MDM is, I nevertheless feel compelled to state my understanding of this relatively new technology area before I get into discussing integration of it with business intelligence. This is done mainly for the sake of clarity. Master data management is a set of processes, policies, services and technologies used to create, maintain and manage data associated with a company’s core business entities as a system of record (SOR) for the enterprise. Core entities include customer, supplier, employee, asset, etc. Note that master data management is not associated with transaction data such as orders, for example. Whether you build your own MDM system or buy a MDM solution from a MDM vendor in the marketplace, it should meet a number of key requirements. These include the ability to:
* Define and maintain metadata for master data entities in a repository.
* Acquire, clean, de-duplicate and integrate master data into a central master data store.
* Offer a common set of shared master data services for applications, processes and portals to invoke to access and maintain master data entities (i.e., system of entry [SOE] MDM services).
* Manage master data hierarchies including a history of hierarchy changes and hierarchy versions.
* Manage the synchronisation of changes to master data to all operational and analytical systems that use complete sets or subsets of this data.
Here is a brief elucidation of the Ten Golden Principles that needs to be appreciated during any MDM Investment
1. Getting Started: Irrespective of the industry, market segment, or existing IT environmentthere is a hunt for the right way to launch an MDM initiative. Many due diligence initiatives propose that MDM is the only right answer to the most critical business problems. Many Corporates have started securing budgets . for launching a sustainable MDM program. The Idea is clear "Start small -- with an initial project -- but think large-scale, long-term, and across subject areas." MDM requires new technologies, specialized skills, and a business focus. With all of these ingredients in place, the payoff is well worth the effort.
2. ROI : Perhaps the biggest issue is how to justify and get funding for an MDM project. As with data warehousing projects, some organizations are blessed with enlightened executives who understand the correlation between high-quality, consistent data and their strategic objectives; these executives may approve MDM projects without delay. Other project leaders must closely align MDM projects with business need and pain and perform a detailed cost-justification analysis. MDM project managers can easily identify and monetize cost savings, but the best approach is to align the MDM initiative with strategic objectives or shoehorn it into approved projects as a necessary underpinning for success.
3. Serendipity: MDM is a business solution that offers a myriad of unexpected benefits once implemented. Many MDM early adopters discovered that once they cleansed and reconciled data through an MDM initiative, they not only improved the quality and consistency of the data available among systems supporting key business processes, but they also enabled other business initiatives such as mergers and acquisitions support, customer relationship management, target marketing, and supply chain optimization. “Data is a corporate asset, and when carefully managed, it provides a strong, stable foundation to support any information-centric business initiative an organization wishes to pursue now or in the future," said Wayne Eckerson, director of research at TDWI. Without MDM, many organizations will spend millions of additional dollars executing information-centric strategic initiatives or won't even attempt them at all.
4. Change Management: Change management is key. From a technical perspective, understanding and socializing the impact of MDM development activities has everything to do with the perception of success. From a cultural perspective, managing the expectations of business and IT stakeholders is nothing less than a make-or-break proposition. Change management is hard and can derail an MDM project if you aren't careful, when you change the data that end users have become accustomed to receiving through reports or other means, it can cause significant angst. You have to anticipate this, implement a transition plan, and prepare the users.
5. Roadblocks : IT and business can pose significant roadblocks. IT stakeholders, many of whom are beholden to established technologies, often need to hear the MDM pitch as much as business users do. Distinguishing MDM from incumbent technologies is an often-underestimated step. Conversely, the business may not want to initiate or fund an MDM project when they already have many of the existing tools and technologies required to do MDM. A very commonly addressed concern/query from majority of the CTOs "Our business has already funded our data warehouse and a customer relationship management solution.,wasn't the data warehouse supposed to solve these issues? wasn't the CRM system supposed to reconcile customers? How can I now convince the stakeholders to take on an MDM initiative?" The answer is that MDM can optimize those existing solutions, reducing their overall expense and minimizing the risk that they will deliver bad data (which could be their kiss of death). Also it is advisable that when purchasing an MDM solution, you shouldn't pay vendors for comparable technologies you already have in house, but which come bundled in their packages.
6. Enterprise Scope : It is true Enterprise MDM may be fraught with problems. With the vision of "start small, think big”, the vast majority of the corporates asserts their wish to quickly broaden their initial implementation to encompass additional domains, systems, and data. Organizations have started supporting their CRM program with better data which facilitiaites to perform business functions that could never have done with native CRM." Thus starting Small companies now plans to extend its MDM capabilities to additional operational systems. Once an organization has implemented an MDM project and it takes root, the organization can then decide whether to widen the road by adding more domains to the existing environment or extend the road by using MDM to address other business problems. 7. Data Governance. - "Data governance is a critical path to MDM,In order to be effective data governance must be designed. A company's cultural norms, established development processes, and incumbent steering committees must all factor into its data governance framework.It is recommended to grow data governance organically and in lockstep with an MDM architecture, which evolves over time. First, one should define what policies and rules are needed by the business to formulate to support an MDM project that solves a business problem. Then, one can formalize the requirements needed to sustain the initiative. That way, the business is working in their own perceived interest, not IT's.
8. Cross-System Data Analysis :One major issue is the time and costs involved in understanding and modeling source data that spans multiple, heterogeneous systems. Microsoft had 10 people working for 100 days to analyze source systems targeted for MDM integration, while the European Patent Office has 60 people analyzing and managing patent data originating around the world. Estimates show that the services-to-software ratio in MDM deployments is 10 to 1, with cross-source data analysis consuming the lion's share of the services. Just as in the data warehousing world, when early adopters in the 1990s underestimated the quality and condition of source data required to deliver an enterprise data warehouse, many implementers have not thought much about the challenges of understanding and reconciling source data.
9. Matching :Calibrating matches between records generated by disparate systems is both art and science. Many speakers acknowledged that these matching engines -- which are typically delivered within a data quality tool -- are the brains of the MDM system.
Many a times consultants need a few go-arounds configuring their matching rules. Many Consultants shared the consequences of "over-matching" records and thus consolidating two products or customers into one faulty record. The point was that matching needs to be refined over time. One must remember that MDM is as much about business and data rules as it is about the data itself.
10. Logical Data Models. An existing logical data model can propel you forward. Data administration skills is imperative in MDM. While some vendors maintain that a logical data model isn't required to make their tools work, most agree that the exercise itself can help a company understand definitions and usage scenarios for enterprise data, which makes it easier to gain consensus around data policies. Carl Gerber, senior manager of data warehousing at Tween Brands, shared how his company's data modeling and stewardship skills were a large part of his team's successful MDM delivery. Tween has created a series of data integration hubs to manage various subject areas, such as product, inventory, suppliers, and merchandising hierarchies. All data exchanged between systems passes through these hubs to ensure data consistency and reconciliation. The architecture has eliminated numerous point-to-point interfaces and improved operational efficiency, decision making, and revenue-generation objectives.
Sunday, March 8, 2009
Ten Commandments for MDM Implementation Prabuddha Roy
Ten Commandments for MDM Implementation
Prabuddha Roy
Without wanting to labour the point about what MDM is, I nevertheless feel compelled to state my understanding of this relatively new technology area before I get into discussing integration of it with business intelligence. This is done mainly for the sake of clarity. Master data management is a set of processes, policies, services and technologies used to create, maintain and manage data associated with a company’s core business entities as a system of record (SOR) for the enterprise. Core entities include customer, supplier, employee, asset, etc. Note that master data management is not associated with transaction data such as orders, for example. Whether you build your own MDM system or buy a MDM solution from a MDM vendor in the marketplace, it should meet a number of key requirements. These include the ability to:
* Define and maintain metadata for master data entities in a repository.
* Acquire, clean, de-duplicate and integrate master data into a central master data store.
* Offer a common set of shared master data services for applications, processes and portals to invoke to access and maintain master data entities (i.e., system of entry [SOE] MDM services).
* Manage master data hierarchies including a history of hierarchy changes and hierarchy versions.
* Manage the synchronisation of changes to master data to all operational and analytical systems that use complete sets or subsets of this data.
Here is a brief elucidation of the Ten Golden Principles that needs to be appreciated during any MDM Investment
1. Getting Started: Irrespective of the industry, market segment, or existing IT environmentthere is a hunt for the right way to launch an MDM initiative. Many due diligence initiatives propose that MDM is the only right answer to the most critical business problems. Many Corporates have started securing budgets . for launching a sustainable MDM program. The Idea is clear "Start small -- with an initial project -- but think large-scale, long-term, and across subject areas." MDM requires new technologies, specialized skills, and a business focus. With all of these ingredients in place, the payoff is well worth the effort.
2. ROI : Perhaps the biggest issue is how to justify and get funding for an MDM project. As with data warehousing projects, some organizations are blessed with enlightened executives who understand the correlation between high-quality, consistent data and their strategic objectives; these executives may approve MDM projects without delay. Other project leaders must closely align MDM projects with business need and pain and perform a detailed cost-justification analysis. MDM project managers can easily identify and monetize cost savings, but the best approach is to align the MDM initiative with strategic objectives or shoehorn it into approved projects as a necessary underpinning for success.
3. Serendipity: MDM is a business solution that offers a myriad of unexpected benefits once implemented. Many MDM early adopters discovered that once they cleansed and reconciled data through an MDM initiative, they not only improved the quality and consistency of the data available among systems supporting key business processes, but they also enabled other business initiatives such as mergers and acquisitions support, customer relationship management, target marketing, and supply chain optimization. “Data is a corporate asset, and when carefully managed, it provides a strong, stable foundation to support any information-centric business initiative an organization wishes to pursue now or in the future," said Wayne Eckerson, director of research at TDWI. Without MDM, many organizations will spend millions of additional dollars executing information-centric strategic initiatives or won't even attempt them at all.
4. Change Management: Change management is key. From a technical perspective, understanding and socializing the impact of MDM development activities has everything to do with the perception of success. From a cultural perspective, managing the expectations of business and IT stakeholders is nothing less than a make-or-break proposition. Change management is hard and can derail an MDM project if you aren't careful, when you change the data that end users have become accustomed to receiving through reports or other means, it can cause significant angst. You have to anticipate this, implement a transition plan, and prepare the users.
5. Roadblocks : IT and business can pose significant roadblocks. IT stakeholders, many of whom are beholden to established technologies, often need to hear the MDM pitch as much as business users do. Distinguishing MDM from incumbent technologies is an often-underestimated step. Conversely, the business may not want to initiate or fund an MDM project when they already have many of the existing tools and technologies required to do MDM. A very commonly addressed concern/query from majority of the CTOs "Our business has already funded our data warehouse and a customer relationship management solution.,wasn't the data warehouse supposed to solve these issues? wasn't the CRM system supposed to reconcile customers? How can I now convince the stakeholders to take on an MDM initiative?" The answer is that MDM can optimize those existing solutions, reducing their overall expense and minimizing the risk that they will deliver bad data (which could be their kiss of death). Also it is advisable that when purchasing an MDM solution, you shouldn't pay vendors for comparable technologies you already have in house, but which come bundled in their packages.
6. Enterprise Scope : It is true Enterprise MDM may be fraught with problems. With the vision of "start small, think big”, the vast majority of the corporates asserts their wish to quickly broaden their initial implementation to encompass additional domains, systems, and data. Organizations have started supporting their CRM program with better data which facilitiaites to perform business functions that could never have done with native CRM." Thus starting Small companies now plans to extend its MDM capabilities to additional operational systems. Once an organization has implemented an MDM project and it takes root, the organization can then decide whether to widen the road by adding more domains to the existing environment or extend the road by using MDM to address other business problems. 7. Data Governance. - "Data governance is a critical path to MDM,In order to be effective data governance must be designed. A company's cultural norms, established development processes, and incumbent steering committees must all factor into its data governance framework.It is recommended to grow data governance organically and in lockstep with an MDM architecture, which evolves over time. First, one should define what policies and rules are needed by the business to formulate to support an MDM project that solves a business problem. Then, one can formalize the requirements needed to sustain the initiative. That way, the business is working in their own perceived interest, not IT's.
8. Cross-System Data Analysis :One major issue is the time and costs involved in understanding and modeling source data that spans multiple, heterogeneous systems. Microsoft had 10 people working for 100 days to analyze source systems targeted for MDM integration, while the European Patent Office has 60 people analyzing and managing patent data originating around the world. Estimates show that the services-to-software ratio in MDM deployments is 10 to 1, with cross-source data analysis consuming the lion's share of the services. Just as in the data warehousing world, when early adopters in the 1990s underestimated the quality and condition of source data required to deliver an enterprise data warehouse, many implementers have not thought much about the challenges of understanding and reconciling source data.
9. Matching :Calibrating matches between records generated by disparate systems is both art and science. Many speakers acknowledged that these matching engines -- which are typically delivered within a data quality tool -- are the brains of the MDM system.
Many a times consultants need a few go-arounds configuring their matching rules. Many Consultants shared the consequences of "over-matching" records and thus consolidating two products or customers into one faulty record. The point was that matching needs to be refined over time. One must remember that MDM is as much about business and data rules as it is about the data itself.
10. Logical Data Models. An existing logical data model can propel you forward. Data administration skills is imperative in MDM. While some vendors maintain that a logical data model isn't required to make their tools work, most agree that the exercise itself can help a company understand definitions and usage scenarios for enterprise data, which makes it easier to gain consensus around data policies. Carl Gerber, senior manager of data warehousing at Tween Brands, shared how his company's data modeling and stewardship skills were a large part of his team's successful MDM delivery. Tween has created a series of data integration hubs to manage various subject areas, such as product, inventory, suppliers, and merchandising hierarchies. All data exchanged between systems passes through these hubs to ensure data consistency and reconciliation. The architecture has eliminated numerous point-to-point interfaces and improved operational efficiency, decision making, and revenue-generation objectives.
Prabuddha Roy
Without wanting to labour the point about what MDM is, I nevertheless feel compelled to state my understanding of this relatively new technology area before I get into discussing integration of it with business intelligence. This is done mainly for the sake of clarity. Master data management is a set of processes, policies, services and technologies used to create, maintain and manage data associated with a company’s core business entities as a system of record (SOR) for the enterprise. Core entities include customer, supplier, employee, asset, etc. Note that master data management is not associated with transaction data such as orders, for example. Whether you build your own MDM system or buy a MDM solution from a MDM vendor in the marketplace, it should meet a number of key requirements. These include the ability to:
* Define and maintain metadata for master data entities in a repository.
* Acquire, clean, de-duplicate and integrate master data into a central master data store.
* Offer a common set of shared master data services for applications, processes and portals to invoke to access and maintain master data entities (i.e., system of entry [SOE] MDM services).
* Manage master data hierarchies including a history of hierarchy changes and hierarchy versions.
* Manage the synchronisation of changes to master data to all operational and analytical systems that use complete sets or subsets of this data.
Here is a brief elucidation of the Ten Golden Principles that needs to be appreciated during any MDM Investment
1. Getting Started: Irrespective of the industry, market segment, or existing IT environmentthere is a hunt for the right way to launch an MDM initiative. Many due diligence initiatives propose that MDM is the only right answer to the most critical business problems. Many Corporates have started securing budgets . for launching a sustainable MDM program. The Idea is clear "Start small -- with an initial project -- but think large-scale, long-term, and across subject areas." MDM requires new technologies, specialized skills, and a business focus. With all of these ingredients in place, the payoff is well worth the effort.
2. ROI : Perhaps the biggest issue is how to justify and get funding for an MDM project. As with data warehousing projects, some organizations are blessed with enlightened executives who understand the correlation between high-quality, consistent data and their strategic objectives; these executives may approve MDM projects without delay. Other project leaders must closely align MDM projects with business need and pain and perform a detailed cost-justification analysis. MDM project managers can easily identify and monetize cost savings, but the best approach is to align the MDM initiative with strategic objectives or shoehorn it into approved projects as a necessary underpinning for success.
3. Serendipity: MDM is a business solution that offers a myriad of unexpected benefits once implemented. Many MDM early adopters discovered that once they cleansed and reconciled data through an MDM initiative, they not only improved the quality and consistency of the data available among systems supporting key business processes, but they also enabled other business initiatives such as mergers and acquisitions support, customer relationship management, target marketing, and supply chain optimization. “Data is a corporate asset, and when carefully managed, it provides a strong, stable foundation to support any information-centric business initiative an organization wishes to pursue now or in the future," said Wayne Eckerson, director of research at TDWI. Without MDM, many organizations will spend millions of additional dollars executing information-centric strategic initiatives or won't even attempt them at all.
4. Change Management: Change management is key. From a technical perspective, understanding and socializing the impact of MDM development activities has everything to do with the perception of success. From a cultural perspective, managing the expectations of business and IT stakeholders is nothing less than a make-or-break proposition. Change management is hard and can derail an MDM project if you aren't careful, when you change the data that end users have become accustomed to receiving through reports or other means, it can cause significant angst. You have to anticipate this, implement a transition plan, and prepare the users.
5. Roadblocks : IT and business can pose significant roadblocks. IT stakeholders, many of whom are beholden to established technologies, often need to hear the MDM pitch as much as business users do. Distinguishing MDM from incumbent technologies is an often-underestimated step. Conversely, the business may not want to initiate or fund an MDM project when they already have many of the existing tools and technologies required to do MDM. A very commonly addressed concern/query from majority of the CTOs "Our business has already funded our data warehouse and a customer relationship management solution.,wasn't the data warehouse supposed to solve these issues? wasn't the CRM system supposed to reconcile customers? How can I now convince the stakeholders to take on an MDM initiative?" The answer is that MDM can optimize those existing solutions, reducing their overall expense and minimizing the risk that they will deliver bad data (which could be their kiss of death). Also it is advisable that when purchasing an MDM solution, you shouldn't pay vendors for comparable technologies you already have in house, but which come bundled in their packages.
6. Enterprise Scope : It is true Enterprise MDM may be fraught with problems. With the vision of "start small, think big”, the vast majority of the corporates asserts their wish to quickly broaden their initial implementation to encompass additional domains, systems, and data. Organizations have started supporting their CRM program with better data which facilitiaites to perform business functions that could never have done with native CRM." Thus starting Small companies now plans to extend its MDM capabilities to additional operational systems. Once an organization has implemented an MDM project and it takes root, the organization can then decide whether to widen the road by adding more domains to the existing environment or extend the road by using MDM to address other business problems. 7. Data Governance. - "Data governance is a critical path to MDM,In order to be effective data governance must be designed. A company's cultural norms, established development processes, and incumbent steering committees must all factor into its data governance framework.It is recommended to grow data governance organically and in lockstep with an MDM architecture, which evolves over time. First, one should define what policies and rules are needed by the business to formulate to support an MDM project that solves a business problem. Then, one can formalize the requirements needed to sustain the initiative. That way, the business is working in their own perceived interest, not IT's.
8. Cross-System Data Analysis :One major issue is the time and costs involved in understanding and modeling source data that spans multiple, heterogeneous systems. Microsoft had 10 people working for 100 days to analyze source systems targeted for MDM integration, while the European Patent Office has 60 people analyzing and managing patent data originating around the world. Estimates show that the services-to-software ratio in MDM deployments is 10 to 1, with cross-source data analysis consuming the lion's share of the services. Just as in the data warehousing world, when early adopters in the 1990s underestimated the quality and condition of source data required to deliver an enterprise data warehouse, many implementers have not thought much about the challenges of understanding and reconciling source data.
9. Matching :Calibrating matches between records generated by disparate systems is both art and science. Many speakers acknowledged that these matching engines -- which are typically delivered within a data quality tool -- are the brains of the MDM system.
Many a times consultants need a few go-arounds configuring their matching rules. Many Consultants shared the consequences of "over-matching" records and thus consolidating two products or customers into one faulty record. The point was that matching needs to be refined over time. One must remember that MDM is as much about business and data rules as it is about the data itself.
10. Logical Data Models. An existing logical data model can propel you forward. Data administration skills is imperative in MDM. While some vendors maintain that a logical data model isn't required to make their tools work, most agree that the exercise itself can help a company understand definitions and usage scenarios for enterprise data, which makes it easier to gain consensus around data policies. Carl Gerber, senior manager of data warehousing at Tween Brands, shared how his company's data modeling and stewardship skills were a large part of his team's successful MDM delivery. Tween has created a series of data integration hubs to manage various subject areas, such as product, inventory, suppliers, and merchandising hierarchies. All data exchanged between systems passes through these hubs to ensure data consistency and reconciliation. The architecture has eliminated numerous point-to-point interfaces and improved operational efficiency, decision making, and revenue-generation objectives.
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