The Consistency of the Data
In today's complex environments, data information on business names or prospects must be shared among multiple applications. Here's where the problem begins.
For a variety of reasons, access to master data is not always available, people start storing other master data by copying and duplicating it in other locations (such as spreadsheets and private application archives).
These are the reasons why data quality degradation and decay occurs, that is, when integration between databases is not present and the same data are not reused across the organization.
If an enterprise data entity (called Master data) is reused in multiple systems, it is certain that that database should be managed with a careful Master Data Management (MDM) policy in order to make that archive a "Consistent Archive."
Meaning of Master Data Management (MDM)
Master Data Management (MDM) could be translated to "Management and curation of core archive data" of a business organization. It consists of managing an organization's effort to create a single master data source for all critical business data, leading to fewer errors and avoiding redundancy in business processes.
One of the key disciplines in the Master Data Management MDM process is that it helps improve the quality of the data itself, ensuring that identifiers and other key data elements related to those entities are accurate and consistent across the enterprise.
What is archival or master data?
Master data archives are often referred to as the basic archives of a data domain; they are the fundamental entity of the enterprise's information assets and archives. Data domains vary from company to company and from industry to industry but the principle of "consistency" and curatorship of the data does not vary.
For example, a trading company's archives might include customers, products, suppliers, materials, etc.
In contrast, a bank's archives might focus on customers, current accounts, financial stocks.
In hospital health care organizations, on the other hand, patients, visits, referrals, analyses, etc. will be managed.
For insurers, Master data include policies, contracts, claims and claims management.
Employees, locations, resources, customers, documents in general are examples of data domains that can be applied across the board as part of master data management initiatives. Another example is "master data," which consists of standard encodings such as codes for countries and states, currencies, and other generic values. Master data does not include the transactions processed in the various data domains. Instead, it essentially functions as a master file of dates, names, addresses, customer IDs, item numbers, product specifications, and other attributes used in transaction processing systems and analysis applications. As a result, well-managed master data is often described as a "single source of truth SSOT" or, alternatively, a single version of the truth on an organization's data and data from external sources integrated into business systems.
The benefits of MDM
An Innovation Manager's main tasks include making enterprise data Consistent. Thanks precisely to MDM solution designs and System Integration actions will come to operate a wide range of data cleansing, transformation and integration practices.
In this new way of operating, when new data is added to the system, MDM solutions will initiate automatic processes to identify, collect, transform and repair the data involved in order to make it integral to the desired system.
Only when the data meet quality thresholds will schemas and taxonomies be created to help maintain an overall high-quality reference.
Organizations that rely on Innovation Manager to use MDM logic enjoy the peace of mind that the data within the company is accurate, up-to-date and Consistent.
Good Master Data Management extends across the entire data lifecycle.
By providing a reference point for critical information, MDM through System Integration eliminates the costly realignments that occur when organizations rely on multiple conflicting sources of information.
Having multiple sources of information is a common problem, especially in large organizations, and the associated costs of maintaining alignment can be high.
One of the main business benefits of MDM is increased data consistency, both for operational and analytical uses. A uniform set of master data on customers and other entities can help reduce operational errors and streamline business processes, such as ensuring that customer service representatives view all the same data on individual customers and that the shipping department has the correct addresses for deliveries. It can also improve the implementation of business intelligence (BI) and analytics applications, with the hope of achieving better strategic planning in business decision making.
Who needs MDM?
MDM initially emerged as a necessary tool of particular interest to large organizations, highly distributed data organizations, and those with frequent or large-scale merger and acquisition activities. Acquiring another company creates far-reaching data integration challenges that an MDM approach is designed to mitigate. Therefore, MDM can accelerate the time-to-value of a corporate acquisition.
Today, however, with advanced computing systems and more accessible databases, MDM helps even the smallest companies. So even modest business organizations but with heterogeneous data systems can enjoy MDM projects to harmonize and enjoy their business data efficiently.
MDM architecture
Successful MDM projects begin with the project scope between the business process owners and the Innovation Manager responsible for the MDM implementation project. Integration planning can show that the business process includes either data producers, data consumers, or both. The primary MDM architectural styles identified by management consultants and Innovation Managers can be classified from simplest to most radical according to one of the following patterns:
A Registry of Architecture
A unified index of master data is created for analysis purposes without changing any of the data in individual source systems. This facility is the lightest MDM architecture and uses data cleaning and matching tools to identify duplicate data entries in different systems and cross-reference them in the registry.
Coexistence of databases.
A consolidated set of master data is created in the MDM hub. In this case, however, changes to master data in individual source systems are updated in the MDM hub and can then be propagated to other systems so that they all use the same data. This provides a good balancing act between system-level management and centralized governance of master data.
An architectural transition
Known as "Data Centralization," this approach moves all management and updating of master data to the central MDM system, which publishes data changes to each source system. It is the most organizationally intrusive style of MDM because of the move to full centralization, but it offers the highest level of enterprise control in terms of data consistency.
Database consolidation.
In which master data sets are extracted from various source systems and consolidated into one repository or MDM hub. This creates a centralized repository of consistent master data that can also be leveraged for use by business intelligence systems-BI, statistical analysis and enterprise reporting.
However, operational systems continue to use their own master data for transaction processing.
The 14 steps above define the functionality that any MDM solution must fully execute. Partial, non-integrated solutions abound, especially on behalf of software solution vendors such as ERP or management. Only total coverage for all master business entities combined with a full set of integrated MDM business processes brings the full value of MDM to the business. This is exactly what the MDM solution does.
This is why you need to rely on professionals who have experience in data curation and consistency and can architect a good MDM master data management project
Data fruition in complex environments
Many companies today, particularly global companies, have hundreds of separate applications and systems (e.g., ERP, CRM, BI, Quality, etc.) in which data that cross departments or organizational divisions can easily become fragmented, duplicated, and most commonly out of date. In this case, answering even the most basic questions, about any kind of performance metrics or KPI Key Performance Indicators(Performance Indices) for a company becomes complicated and laborious.
Basically, the need for accurate and timely information has become strategic, and as data sources increase, getting all parts of a company to use the same information has become a very important challenge.
To meet these challenges, companies are turning to data engineering specialists and Innovation Managers to deal with MDM data architectures.
What is master data?
Most software systems have data lists that are shared and used by different applications that make up the system.
A typical ERP management system will have among the master data lists of Customers, Suppliers, Products, Price Lists, Agents, Price History, Cost History, etc. This master data is often one of a company's key assets and must necessarily be preserved and made available to the entire business organization in a unique and certain way.
Types of data
Master data:
The main data within the company that describe the objects around which the business is conducted. It usually changes infrequently and may include reference data necessary for the operation of the business. Master data are not transactional in nature, but are involved in transactions.
Reference data:
A special type of master data used to classify other data or used to relate data to information across company boundaries. Reference data can be shared between master or transactional data objects (e.g., countries, currencies, time zones, payment terms, etc.).
Transactional data:
Data about business events (often related to system transactions, such as sales, deliveries, invoices, service tickets, complaints, and other general interactions) that have historical significance or are needed for analysis by other systems. Transactional data are unit-level movements using Master data entities. Unlike Master data precisely, transactions are inherently temporal and instantaneous in nature.
Metadata:
Data about other data. They are often stored in other DataStore for targeted processing. ETL for example is the case for a specific structuring of data for Business Intelligence systems, they are developed by drawing from the underlying repositories and reprocessed in order to be performant in terms of usability and computation. Other types of Metadata can be Json or XML for storing dynamic structures and archives or report definitions, column descriptions in a database, log files, connections and configuration files.
Unstructured data:
Data that by its nature cannot be typed except for nondominant elements. For example in e-mail, the structure consists of a handful of information (sender, recipient, subject and body of the e-mail) But the real archive of the e-mail is the intrinsic meaning of the content of the e-mail itself. Unless external profiling systems are adopted, the mail remains an unstructured archive. I will search an email only through personal inspirations. If it does not go into a profiling and categorization system, the mail document is not consistent. The same problem also occurs with all file documents such as Images, videos, PDF Word Excel etc.
In the Data Management world, these types of data find their ally in the new concepts of profiling with document systems, or even indexing and profiling in NoSQL databases, in which to make these archives quickly findable from the abstract form to the database form by indexing them with the classic criteria of Data Management
Hierarchical data:
Data that store the relationships between other data. They contain the relationships among different data sources, such as organizational storage structures Hierarchical data are sometimes considered a super MDM domain because they are critical to understanding and sometimes discovering the relationships among Master data.
What are master data entities?
Although the identification of master data entities is fairly straightforward, not all data that fit the definition of master data need necessarily be managed as such.
In general, Master data are normally a small part of all data from a volume perspective, but they are the most complex and most valuable to maintain and manage.
So what data should you manage as master data?
One of the main indicators of the need for Master data management is reuse. When basically the same master data is used within multiple applications, for example, a business prospect or customer that needs to be in both the management and CRM, Business Intelligence, or document system.MDM best practices
The real project of the Innovation Manager implementing an MDM system represents the organizational, cultural, and ultimately technical process of the overall organization. Consequently, it is important to involve managers and users in MDM programs, especially if master data will be centrally managed and updated in operational systems by an MDM hub. An organization's various data stakeholders should participate in decisions about how to structure master data and policies to implement changes to all systems involved.
MDM should be approached as an ongoing initiative rather than a one-time project: frequent updates to master data records are often necessary. Some organizations have established MDM centers of excellence to establish and then manage their programs in an effort to avoid blockages and reduce efforts to incorporate common master data sets into business systems.
MDM Challenges.
The potential benefits of Master data management increase as the number and diversity of systems and applications in an organization increases. For this reason, MDM is more likely to be of greater value to large enterprises than to small businesses (SMEs).
One of the biggest hurdles is getting different business units and departments to agree on standard behaviors about master data management; MDM efforts can lose momentum and get bogged down if users do not agree on how data should be formatted in their separate systems. Another obstacle often mentioned to MDM implementations is the loss of "project scoping." Efforts can become cumbersome if the scope of planned work is out of control or if the implementation plan does not properly stage the required steps.
When companies merge, an MDM project can help simplify data integration, reduce incompatibilities, and optimize operational efficiency in the new combined organization, but the challenge of reaching consensus on master data across business units may be even greater after a merger or acquisition. The increasing use of big data systems in organizations can also complicate the MDM process by adding new forms of unstructured and semistructured data stored in a variety of platforms.
MDM vs. Big Data
A small digression: Big Data policy has set in motion new thinking and that is to collect as much data as possible and in the fastest way since so much will leave in huge dataStore hubs to be magically or miraculously harmonized by some "alien entity."
Well sorry to disappoint science fiction fans, the best rule still in place today for efficient and functional use of one's data is still to harmonize it and make the data as robust and consistent as possible.
If this data is then accompanied by floods of data collected in a thousand ways, well, so much the better. They can be used in addition, to draw up average statistical analyses or trends to enrich the information and strategies you already have. The cloud of Big Data and AI will definitely help increase the control and efficiency of our archives but to date, let's not confuse the 2 levels yet. Proper design of database systems and Master Data Management is still the basis for any structured Big Data project. In the absence of structured data we can only get hints to reason about, perceptions of happenings but nothing consistent and certain.
In today's complex environments, data information on business names or prospects must be shared among multiple applications. Here's where the problem begins.
For a variety of reasons, access to master data is not always available, people start storing other master data by copying and duplicating it in other locations (such as spreadsheets and private application archives).
These are the reasons why data quality degradation and decay occurs, that is, when integration between databases is not present and the same data are not reused across the organization.
If an enterprise data entity (called Master data) is reused in multiple systems, it is certain that that database should be managed with a careful Master Data Management (MDM) policy in order to make that archive a "Consistent Archive."
Meaning of Master Data Management (MDM)
Master Data Management (MDM) could be translated to "Management and curation of core archive data" of a business organization. It consists of managing an organization's effort to create a single master data source for all critical business data, leading to fewer errors and avoiding redundancy in business processes.
One of the key disciplines in the Master Data Management MDM process is that it helps improve the quality of the data itself, ensuring that identifiers and other key data elements related to those entities are accurate and consistent across the enterprise.
What is archival or master data?
Master data archives are often referred to as the basic archives of a data domain; they are the fundamental entity of the enterprise's information assets and archives. Data domains vary from company to company and from industry to industry but the principle of "consistency" and curatorship of the data does not vary.
For example, a trading company's archives might include customers, products, suppliers, materials, etc.
In contrast, a bank's archives might focus on customers, current accounts, financial stocks.
In hospital health care organizations, on the other hand, patients, visits, referrals, analyses, etc. will be managed.
For insurers, Master data include policies, contracts, claims and claims management.
Employees, locations, resources, customers, documents in general are examples of data domains that can be applied across the board as part of master data management initiatives. Another example is "master data," which consists of standard encodings such as codes for countries and states, currencies, and other generic values. Master data does not include the transactions processed in the various data domains. Instead, it essentially functions as a master file of dates, names, addresses, customer IDs, item numbers, product specifications, and other attributes used in transaction processing systems and analysis applications. As a result, well-managed master data is often described as a "single source of truth SSOT" or, alternatively, a single version of the truth on an organization's data and data from external sources integrated into business systems.
The benefits of MDM
An Innovation Manager's main tasks include making enterprise data Consistent. Thanks precisely to MDM solution designs and System Integration actions will come to operate a wide range of data cleansing, transformation and integration practices.
In this new way of operating, when new data is added to the system, MDM solutions will initiate automatic processes to identify, collect, transform and repair the data involved in order to make it integral to the desired system.
Only when the data meet quality thresholds will schemas and taxonomies be created to help maintain an overall high-quality reference.
Organizations that rely on Innovation Manager to use MDM logic enjoy the peace of mind that the data within the company is accurate, up-to-date and Consistent.
Good Master Data Management extends across the entire data lifecycle.
By providing a reference point for critical information, MDM through System Integration eliminates the costly realignments that occur when organizations rely on multiple conflicting sources of information.
Having multiple sources of information is a common problem, especially in large organizations, and the associated costs of maintaining alignment can be high.
One of the main business benefits of MDM is increased data consistency, both for operational and analytical uses. A uniform set of master data on customers and other entities can help reduce operational errors and streamline business processes, such as ensuring that customer service representatives view all the same data on individual customers and that the shipping department has the correct addresses for deliveries. It can also improve the implementation of business intelligence (BI) and analytics applications, with the hope of achieving better strategic planning in business decision making.
Who needs MDM?
MDM initially emerged as a necessary tool of particular interest to large organizations, highly distributed data organizations, and those with frequent or large-scale merger and acquisition activities. Acquiring another company creates far-reaching data integration challenges that an MDM approach is designed to mitigate. Therefore, MDM can accelerate the time-to-value of a corporate acquisition.
Today, however, with advanced computing systems and more accessible databases, MDM helps even the smallest companies. So even modest business organizations but with heterogeneous data systems can enjoy MDM projects to harmonize and enjoy their business data efficiently.
MDM architecture
Successful MDM projects begin with the project scope between the business process owners and the Innovation Manager responsible for the MDM implementation project. Integration planning can show that the business process includes either data producers, data consumers, or both. The primary MDM architectural styles identified by management consultants and Innovation Managers can be classified from simplest to most radical according to one of the following patterns:
A Registry of Architecture
A unified index of master data is created for analysis purposes without changing any of the data in individual source systems. This facility is the lightest MDM architecture and uses data cleaning and matching tools to identify duplicate data entries in different systems and cross-reference them in the registry.
Coexistence of databases.
A consolidated set of master data is created in the MDM hub. In this case, however, changes to master data in individual source systems are updated in the MDM hub and can then be propagated to other systems so that they all use the same data. This provides a good balancing act between system-level management and centralized governance of master data.
An architectural transition
Known as "Data Centralization," this approach moves all management and updating of master data to the central MDM system, which publishes data changes to each source system. It is the most organizationally intrusive style of MDM because of the move to full centralization, but it offers the highest level of enterprise control in terms of data consistency.
Database consolidation.
In which master data sets are extracted from various source systems and consolidated into one repository or MDM hub. This creates a centralized repository of consistent master data that can also be leveraged for use by business intelligence systems-BI, statistical analysis and enterprise reporting.
However, operational systems continue to use their own master data for transaction processing.
The 14 steps above define the functionality that any MDM solution must fully execute. Partial, non-integrated solutions abound, especially on behalf of software solution vendors such as ERP or management. Only total coverage for all master business entities combined with a full set of integrated MDM business processes brings the full value of MDM to the business. This is exactly what the MDM solution does.
This is why you need to rely on professionals who have experience in data curation and consistency and can architect a good MDM master data management project
Data fruition in complex environments
Many companies today, particularly global companies, have hundreds of separate applications and systems (e.g., ERP, CRM, BI, Quality, etc.) in which data that cross departments or organizational divisions can easily become fragmented, duplicated, and most commonly out of date. In this case, answering even the most basic questions, about any kind of performance metrics or KPI Key Performance Indicators(Performance Indices) for a company becomes complicated and laborious.
Basically, the need for accurate and timely information has become strategic, and as data sources increase, getting all parts of a company to use the same information has become a very important challenge.
To meet these challenges, companies are turning to data engineering specialists and Innovation Managers to deal with MDM data architectures.
What is master data?
Most software systems have data lists that are shared and used by different applications that make up the system.
A typical ERP management system will have among the master data lists of Customers, Suppliers, Products, Price Lists, Agents, Price History, Cost History, etc. This master data is often one of a company's key assets and must necessarily be preserved and made available to the entire business organization in a unique and certain way.
Types of data
Master data:
The main data within the company that describe the objects around which the business is conducted. It usually changes infrequently and may include reference data necessary for the operation of the business. Master data are not transactional in nature, but are involved in transactions.
Reference data:
A special type of master data used to classify other data or used to relate data to information across company boundaries. Reference data can be shared between master or transactional data objects (e.g., countries, currencies, time zones, payment terms, etc.).
Transactional data:
Data about business events (often related to system transactions, such as sales, deliveries, invoices, service tickets, complaints, and other general interactions) that have historical significance or are needed for analysis by other systems. Transactional data are unit-level movements using Master data entities. Unlike Master data precisely, transactions are inherently temporal and instantaneous in nature.
Metadata:
Data about other data. They are often stored in other DataStore for targeted processing. ETL for example is the case for a specific structuring of data for Business Intelligence systems, they are developed by drawing from the underlying repositories and reprocessed in order to be performant in terms of usability and computation. Other types of Metadata can be Json or XML for storing dynamic structures and archives or report definitions, column descriptions in a database, log files, connections and configuration files.
Unstructured data:
Data that by its nature cannot be typed except for nondominant elements. For example in e-mail, the structure consists of a handful of information (sender, recipient, subject and body of the e-mail) But the real archive of the e-mail is the intrinsic meaning of the content of the e-mail itself. Unless external profiling systems are adopted, the mail remains an unstructured archive. I will search an email only through personal inspirations. If it does not go into a profiling and categorization system, the mail document is not consistent. The same problem also occurs with all file documents such as Images, videos, PDF Word Excel etc.
In the Data Management world, these types of data find their ally in the new concepts of profiling with document systems, or even indexing and profiling in NoSQL databases, in which to make these archives quickly findable from the abstract form to the database form by indexing them with the classic criteria of Data Management
Hierarchical data:
Data that store the relationships between other data. They contain the relationships among different data sources, such as organizational storage structures Hierarchical data are sometimes considered a super MDM domain because they are critical to understanding and sometimes discovering the relationships among Master data.
What are master data entities?
Although the identification of master data entities is fairly straightforward, not all data that fit the definition of master data need necessarily be managed as such.
In general, Master data are normally a small part of all data from a volume perspective, but they are the most complex and most valuable to maintain and manage.
So what data should you manage as master data?
One of the main indicators of the need for Master data management is reuse. When basically the same master data is used within multiple applications, for example, a business prospect or customer that needs to be in both the management and CRM, Business Intelligence, or document system.MDM best practices
The real project of the Innovation Manager implementing an MDM system represents the organizational, cultural, and ultimately technical process of the overall organization. Consequently, it is important to involve managers and users in MDM programs, especially if master data will be centrally managed and updated in operational systems by an MDM hub. An organization's various data stakeholders should participate in decisions about how to structure master data and policies to implement changes to all systems involved.
MDM should be approached as an ongoing initiative rather than a one-time project: frequent updates to master data records are often necessary. Some organizations have established MDM centers of excellence to establish and then manage their programs in an effort to avoid blockages and reduce efforts to incorporate common master data sets into business systems.
MDM Challenges.
The potential benefits of Master data management increase as the number and diversity of systems and applications in an organization increases. For this reason, MDM is more likely to be of greater value to large enterprises than to small businesses (SMEs).
One of the biggest hurdles is getting different business units and departments to agree on standard behaviors about master data management; MDM efforts can lose momentum and get bogged down if users do not agree on how data should be formatted in their separate systems. Another obstacle often mentioned to MDM implementations is the loss of "project scoping." Efforts can become cumbersome if the scope of planned work is out of control or if the implementation plan does not properly stage the required steps.
When companies merge, an MDM project can help simplify data integration, reduce incompatibilities, and optimize operational efficiency in the new combined organization, but the challenge of reaching consensus on master data across business units may be even greater after a merger or acquisition. The increasing use of big data systems in organizations can also complicate the MDM process by adding new forms of unstructured and semistructured data stored in a variety of platforms.
MDM vs. Big Data
A small digression: Big Data policy has set in motion new thinking and that is to collect as much data as possible and in the fastest way since so much will leave in huge dataStore hubs to be magically or miraculously harmonized by some "alien entity."
Well sorry to disappoint science fiction fans, the best rule still in place today for efficient and functional use of one's data is still to harmonize it and make the data as robust and consistent as possible.
If this data is then accompanied by floods of data collected in a thousand ways, well, so much the better. They can be used in addition, to draw up average statistical analyses or trends to enrich the information and strategies you already have. The cloud of Big Data and AI will definitely help increase the control and efficiency of our archives but to date, let's not confuse the 2 levels yet. Proper design of database systems and Master Data Management is still the basis for any structured Big Data project. In the absence of structured data we can only get hints to reason about, perceptions of happenings but nothing consistent and certain.