Master Data Management, Data Governance, Data Quality

Master Data Management (MDM) is the technology, tools, and processes an organization needs to create and maintain consistent and accurate inventory of its data.

Master data usually refers to non-transactional data within the organization and can include customers, suppliers, employees, products, partners, accounts etc.

Master data management provides a trusted view of those entities and a common point of reference. It streamlines data sharing among all systems and business users across lines of business.

Master data management

http://help.sap.com/saphelp_mdm550/helpdata/en/47/1c5928cd0412b8e10000000a1553f7/content.htm

Efficient MDM delivers access to “single version of the truth” by eliminating critical data being fragmented across multiple systems, and therefore duplicated, mirepresented and misinterpreted, which can easily compromise overall performance of an organisation.

From an analytics perspective, organizations can employ quality data for reporting and compliance purposes, and to optimize and enhance partner and channel engagement.

Some of benefits organisations can gain by using MDM include:

  • Identifying new opportunities to interact with customers and channel partners
  • Enhancing business intelligence, reporting and analytic capabilities
  • Realizing improved efficiency across business processes
  • Optimizing the manual effort required to manage and use data across the enterprise

MDM

Data Governance

Being one of elements of data management, Data Governance is more about process than technology. It refers to policies and processes for managing the availability, usability, integrity, and security of data.

It ensures that the data entry by an operations team member or by an automated process meets precise standards, such as a business rule, a data definition and data integrity constraints in the data model.

A data governance initiative must build competencies, assign roles and responsibilities and invest in technologies. Business owners across organisation must own the data and the business processes. Well defined business rules, data stewardship, and data control and compliance mechanisms need to be in place for a success of MDM implementation.

DG Process-stages

There are four main business processes that enable data governance and stewardship – Discover, Define, Apply and Measure and Monitor.

Insights from discover processes (capturing the state of an organization’s data lifecycle, state of the data itself, dependent business processes etc),  are used to define the data governance strategy, priorities, business case, policies, standards, architecture and the ultimate future state vision.

Define processes document data definitions and business context needed to operationalise data governance efforts.

Apply processes operationalise and ensure compliance with all the data governance policies, business rules, processes, workflows, and roles and responsibilities captured through the previous stages.

Measure and Monitor processes capture and measure the effectiveness and value generated from data governance and stewardship efforts, monitor compliance, and enable transparency and auditability into data assets and their life cycle.

Data Quality

Data quality is the degree to which data is complete, timely, consistent, valid, integrated and accurate.

Data is considered high quality if “they are fit for their intended uses in operations, decision making and planning.” (Redman, T.C. (2008).

data-quality-dimensions

Quality data provides a strong and secure foundation to drive business execution and differentiated services and leads to better decision making.

Data that are inaccurate, untimely or inconsistent with other sources of information lead to incorrect decisions, product recalls, and financial losses. Data quality problems can be caused by redundant or inconsistent data produced by multiple systems feeding a data warehouse. Properly designed database and established enterprise-wide data standards minimises duplicate or inconsistent data elements (Laudon and Laudon, chapter 6).

 

All the three above initiatives are strongly linked together. Master Data Managements starts with Quality Data and Data Governance. Essentially, master data management is a data governance, quality oriented and business driven initiative and its success depends on the collaboration and continous involvement of the steakholders across the organisation’s units.

 

Sources:

https://www.informatica.com/campaigns/infosys_best_practices_mdm_wp.pdf

http://searchdatamanagement.techtarget.com/definition/master-data-management

http://blogs.informatica.com/2014/01/02/the-process-stages-of-data-governance/#fbid=mNM5ZF2FmCc

 

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