Objectives:

In any organisation, certain data are needed in all business areas, processes and systems. If this data is shared and all business units can access the same costumer lists, geographic location codes, business units lists, delivery options, parts lists, accounting cost centres codes, governament tax codes and other data used to run the business, the overall organisation and its costumers will benefit from it. People using data generally assume that there is a level of consistency throughout the organisation until they see heterogeneous data.

In most organisations, systems and data evolve more consistently than data management professionals would like. Especially in large organisations, various projects and initiatives, merges and acquisitions and other business activities result in multiple systems performing essentially the same functions isolated from each other. This variability increases costs and risks. Both can be reduced through the management of Master Data and Reference Data.

Activities carried out by our Team:

Activity of Master Data Management – MDM

• Defining MDM drivers and requirements: Every organisation has different drivers and critical issues related to MDM, influenced by the number and type of systems, their age, the business processes they support and the way data is used for both transactions and analytics. Drivers often include opportunities to improve customer service and/or operational efficiency, as well as to reduce privacy and compliance risks. Critical issues include differences in the meaning and structure of data between systems.
• Estimate and evaluate data sources: Data in existing applications form the basis of Master Data Management activities. It is important to understand the structure and content of these data and the processes through which these data are collected or created. One outcome of MDM activities can be the improvement of Metadata generated through the actvity of evaluating the quality of existing data. One objective of the evaluation is to understand how complete the data are in reltion to the attributes that comprise Master Data. This process includes clarifying the definitions and granularity of these attributes.
• Defining the architectural approach: The architectural approach to MDM depends on the business strategy, the existing data source platforms and the data itself, in particular its lineage and volatility, and the implications of high or low latency. The architecture must take into account data utilisation and sharing patterns. Maintenance tools depend on both business requirements and architecture options. These tools help define and at the same time depend on the approach to stewardship and maintenance.
• Modelling Master Data: MDM is a data integration process. To achieve consistent results and manage the integation of new sources as an organisation expands, it is necessary to define a data model within subject areas. A logical or canonical model can be defined on the subject areas within the data sharing hub. This would allow organisation-wide definitions of entities and attributes of the thematic area.
• Defining stewardship and maintenance processes: Technical solutions can do a remarkable job of matching, merging and managing master record identifiers. However, the process also requires stewardship, not only to address records that are discarded from the process, but also to remediate and improve the processes that cause them to be discarded in the first place. MDM projects must take into account the resources needed to support the consistent quality of Master Data. Records must be analysed, feedback provided to source systems and input that can be used to optimise and improve the algorithms that drive the MDM solution.
• Establish governance policies to enforce the use of Master Data: The initial launch of a Master Data activity is challenging and requires a lot of attention; the real benefits come when people and systems start using Master Data. The overall effort must include a roadmap for systems to adopt Master Data values and identifiers as input for processes.

Activities of Reference Data

• Defining of drivers and requirements: The main drivers of Reference Data Management are the operating efficiency and superior data quality. A centralised management of Reference Data is more favorable than having several business units maintain their own data sets. It also reduces the risks of inconsistency between systems. The most important Reference Data sets must fulfil the requirements of a Reference Data Management System. One such a system is in place, new Reference Sets can be set up.
• Evaluating data sources: Most of industry-standard Reference Data sets can be obtained from the organisations that create and manage them. Some organisations provide such data for free; others charge a fee. Intermediaries also package and sell Reference Data, often with value-added features. Depending on the number and type of Reference Data sets needed by an organisation, it may be preferable to purchase from a supplier, especially if the supplier guarantees delivery of updates according to a set schedule and performs basic quality control on the data. Many organisation also rely on Reference Data created and managed in-house. Determining the source of local or internal Reference Data is often more difficul than doing so for industry-standard Reference Data.
• Defining the architectural approach: Before purchaising or creating a Reference Data management tool, it is essential to take into account the requirements and challenges set by the Reference Data to be managed. It must be ensured that the interface for updates is simple and can be set up to enforce basic data entry rules, e.g. ensuring that parent/child relationships are maintained in Reference Data comprising hierarchies.
• Modeling Reference Data sets: Many people see Reference Data as simple codes and descriptions. However, many Reference Data are more complicated. In order to ensure a long-term use and to establish accurate Metadata, as well as the maintenance process itself, it is useful to create models of Reference Data sets. Template help data users to understand the relationships within Reference Data set and they can be used to establish data quality rules.
• Defining stewardship and maintenance processes: Reference Data require stewadship to ensure that values are complete and current and that definitions are clear and understandable. In some cases, stewards will be directly responsible for the practical maintenance of Reference Data; in other cases, they can facilitate the process.
• Establishing Reference Data governance policies: an organisation obtains value from a centally managed repository of Reference Data only if people actually use data from that repository. To this end, it is important to implement policies that govern the quality and enforce the use of Reference Data from that repository, both directly through pubblication from it and indirectly from a reference system populated with data from the central repository.