SILVERRUN Business Architecture Tools

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Grandite

Coming up on May 29, 2017

The next SILVERRUN version will feature the integration of business modeling and measures for Data Governance and Regulatory Compliance.

Grandite

Multi-Domain Master Data Management
(MDMDM / MDM)


What Is Master Data?

Master Data is non-transactional data that is key to the business of your organization.

There are three main domains to be considered for Master Data Management: Party, Location and Object.

Party is any natural or legal person whose data is processed by your organisation. A party can take multiple roles e.g. be a customer and a supplier.

Location is a physical or virtual place whose data is processed by your organisation. Traditional examples are Postal Address, Phone Number. When including social media and more recent ways of communication, you may want to consider e.g. Twitter Handle, LinkedIn Account, Skype Id as (virtual) locations.

Object is any tangible or non-tangible entity whose data is processed by your organisation. As opposed to Party and Location, the range of sub-domains for Object varies from industry to industry. Typical examples are Product, Service, Material, Item, Part, Store, Machine, Tool.

Party and Object describe your organization's operational assets, considering that Customer is the largest populated subdomain of Party and that Product / Service is the largest populated subdomain of Object. Moreover, Party, Location and Object (combined with Time) constitute the 4 primary dimensions needed for your organization's Business Intelligence (BI), (as a typical BI question is e.g.: How many Products abc were sold to Customer target group xyz in Location qaz during the last quarter?)

Since any organization depends on its assets and derives its decisions from its Business Intelligence, Master Data is the most important, i.e. the most valuable data.


What Is Master Data Management?

Master Data Management is an organization's practice, based on documented policies and standards, to accomplish and maintain Master Data quality throughout the organization while applying the principles of Economy.


What Is the Challenge with Master Data Management?

Almost any application for commercial transactions includes the administration of Master Data, an important subset being Enterprise Resource Planning (ERP) systems. The majority of medium and large businesses employ multiple such application systems for at least one of the following reasons:

  • Departments use individual solutions for their specific needs.
  • Legacy systems have not been (completely) replaced, as other applications still depend on them.
  • Multi-national businesses need to respond to different regional requirements.
  • Mergers and acquisitions have introduced additional redundancy.

Accordingly, Master Data are created and maintained in multiple parallel storage silos.

The consequence:

  • Unsynchronized and therefore inaccurate, outdated Master Data in parts of the operation
  • No single customer view
  • Unexploited cross-selling potential
  • Contradictory business reports from different parts of the operation
  • No reliable basis for business decisions


How SILVERRUN Helps Organizations to Develop a Solid Master Data Management Concept

SILVERRUN leverages the modeling functionality to

  • Document the models of the existing Master Data sources incl.
    • Capture structure of source and characteristics (type, length, domain values, nullability etc.) of each column (e.g. through reverse engineering)
    • Assign semantics to each entity and attribute
  • Define the model of the target database
    • Integrate the source models into a draft of the Master Data's target structure
    • Derive a sustainable logical model of the Master Data's target structure based on an organization-wide synchronized understanding of the semantics
    • Derive the physical model of the Master Data's target structure based on the chosen architecture style (Consolidated, Registry, Coexistence or Transaction)
  • Keep track of the lineage between source columns and target columns
  • Define the mapping from source columns to target columns (ETL process)

SILVERRUN supports all multi-level modeling features (integration, propagation, lineage, common items) necessary to convert a siloed application structure to an integrated Enterprise Data Architecture, thus avoiding future disruptions in the organization's application landscape. In particular, SILVERRUN delivers the indispensable blueprint for any organization's Master Data Management.


How Grandite Helps Organizations to Successfully Implement the Right Master Data Management Solution

Grandite's Professional Services offers Senior Project Management experience embedding the above SILVERRUN modeling steps into a complete project plan including, but not limited to major tasks such as

  • Identifying organizational goals and prioritized objectives
  • Identifying domain(s) related to the prioritized objectives
  • Identifying business processes involved in creation and update of this/these domains
  • Identifying the organizational units performing those business processes
  • Selecting a business case to prove the economical advantage of a Master Data Management project
  • Finding a business sponsor on the executive level
  • Composing a project team and assigning roles and responsibilities that appropriately reflect the structure and culture of the organization
  • Introducing permanent roles responsible for the ongoing process of Master Data Management (e.g. Data Stewards)
  • Profiling the Master Data (i.e. compare expected and actual content for each source column and derive measures for the treatment of data quality issues, such as cleansing, validation, correction and enrichment)
  • Selecting the Master Data Management architecture style (Consolidated, Registry, Coexistence or Transaction)
  • Harmonizing / integrating the organizational and technical measures with other existing or planned organization-wide activities such as a Data Governance process as well as with priorities of other projects
  • Evaluating the Master Data Management solution
  • Defining metrics to measure Data Quality and monitor in an ongoing process
  • Defining complementary organizational measures (user responsibilities, business process improvements) to maintain the quality accomplished with the deployment of the Master Data Management solution and to establish Master Data Management as an ongoing program.


More Information...

The right Master Data Management solution is as unique as each organization's history. If you are looking for additional answers, please be invited to contact us here at your convenience.


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