Data management is the term used for referring to how companies and organisations around the world handle data that is being recorded on a daily basis with ever more data being generated each year than the previous. The importance of having this information at hand is proving more and more important so trying to maintaining uniformity across all machines in different countries, time zones and organisations can be a difficult. This is why more and more companies are looking to Master Data Management in order to gain the maximum benefits from the available data and to use it in the most effective way possible.
According to a Gartner article from 2013
“Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.
Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”
What this implies is that for a company to really use MDM that insuring the right manor and methods are used when dealing with the data and data entry. The correct handling of this MDM is importance for companies as:
- Master data is used to make decisions on all company levels.
- Business processes throughout the entire company rely on master data.
- Higher quality master data helps to improve the operational efficiency of a company.
- With high-quality master data, costs can be reduced.
The goal of an MDM initiative is to provide processes for collecting, aggregating, matching, consolidating, assuring quality and distributing critical data throughout an organisation to ensure consistency and control in the ongoing maintenance and application use of this information
In order to utilise Master Data Management there are different processes that can be followed, they are:
- ETL: Extract, Transform, Load. A process in database responsible for data extraction extracting data from different sources and compiling it into a consolidated location
- EAI: Enterprise application integration is the term for the plans, methods and tools used to modernizing, consolidating, and coordinating data for use in companies systems
- EII: Enterprise information integration, is the ability to support a unified view of data and information for an entire organisation
Data Governance is used by organisations to share common goals of company/corporate polices for data definition, enforcement and for communicating ideas and principals.
It is thought that as most companies data is held in databases and on computers that the responsibility for the data should fall within the IT department but this is not always the case and some people don’t see the need to look after the data governance in a company on a continuing basis.
Data governance initiatives can improve data quality by have an assigned teams responsible for data’s accuracy, accessibility, consistency, and completeness, among other metrics. The team commonly consisting of project management, business managers, and data stewards.
These would be the people that would drive strategy and vision for that data, what data is stored, assign and manage the data stewards and set “Best Pratices” for the company to follow
Some of the most popular tools for Data Governance are
- Onesoft Connect
- Fusion Platform
Gartner (2013) http://www.gartner.com/it-glossary/master-data-management-mdm [Accessed 16th September 2016]
Baum, D., (n.d) ‘Masters of the Data’ Oracle. [Online]. Available from: http://www.oracle.com/us/c-central/cio-solutions/information-matters/importance-of-data/index.html [Accessed 17th September 2016]
Couture, N., (n.d.) ‘Implementing an enterprise Data Quality Strategy’. Business Intelligence Journal, vol. 18, no. 4.
David L., (n.d) ‘Data Governance for Master Data Management and Beyond’ SAS The power to know. [Online]. Available from: http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/data-governance-for-MDM-and-beyond-105979.pdf [Accessed 15th September 2016]