Friday, April 1, 2016

5 Key Factors of Master Data Management

Transaction Cost Analysis (TCA)
Master data management (MDM) is where an organization manages data via a single point of reference is a growing trend, and more and more companies are getting on board. In fact, GlobeNewsWire reports “the global master data management market is expected to grow from $9,440.4 million in 2015 to $26,799.6 million by 2020, at a compound annual growth rate of 23.2% during the forecast period from 2015 to 2020.” But for MDM initiation to be successful and for organizations to truly reap the rewards, it relies upon five key factors.

1) Management and Staff Support

Perhaps most important of all is having the complete support of everyone involved with the process. Because of the often exhaustive nature of MDM initiation, long-term commitment and changes it can bring about within an organization, it’s crucial that everyone buys in and is willing to do whatever it takes to make the process a success. Otherwise, a lack of support can throw a serious wrench in things.

2) Quality Data

If master data is inaccurate, extraneous, contains duplicates or is generally of low quality, it basically defeats the purpose of implementing MDM. Not only can it be costly to an organization and reduce profitability, but it can put a damper on productivity as well. Consequently, it’s imperative that master data meets strict quality standards, which is why many companies utilize data profiling to assess the quality of data prior to data migration.

3) Efficient Data Integration

Besides upholding rigorous quality standards, the actual process of consolidating data and moving it to a master repository needs to be efficient. Whether it’s a single company creating a master repository between different departments or two companies combining data during a merger, the data integration process needs to be as streamlined as possible to minimize setbacks and prevent it from being overly time-consuming.

4) A Secure and Scalable Master Data Repository

AdobeStock_83032898 (1)In an age where cyber crime is an omnipresent threat, security should be a major priority for everyone involved with MDM. An organization should uphold scrupulous security standards throughout initial integration and continue to uphold them moving forward. And because there’s a good chance that a data model will require modifications at some point in the future, it’s wise to have a repository that’s flexible enough to accommodate those modifications.

5) Continual Quality Control

For MDM to be successful in the long run, it’s important for an organization to take quality control seriously. Developing a data quality assurance program is usually the most effective way to go about it – and different departments throughout an organization need to be on the same page to maintain consistency. Doing so should ensure that data remains of the highest possible quality for years to come.

The bottom line is that MDM can do wonders for a company and is likely to be a growing trend over the next five years plus. But in order for MDM to be a success, it’s contingent  upon these five main factors and a certain amount of persistence.

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Master Data Management

TCA Software Companies

Master data management (MDM) projects must be business-owned, joined up, and proven step-by-step
Business must take the lead of MDM
Think big, start small
MDM has to deal with data from multi domain
similar with dimensional data instead of transactional data




Identify domain (manifacture/health care/financial )
Identify master data (consistent across system and process)

Master data: Data representing key data entities critical to a company operations and analytics because of how it interacts and provides context to transactional data.
Transactional data: Data associated with or resulting from specific business transactions.
Reference data: Data typically represented by code set values used to classify or categorize other types of data, such as master data and transactional data.
Metadata: Descriptive information about data entities and elements such as the definition, type, structure, lineage, usage, changes and so on.
Following SAP's lead, other giant vendors began muscling in on the act. IBM bought DWL, Oracle introduced a series of products, and Microsoft purchased Stratature, a small MDM vendor.

Research Data Management: what do you need to know?

        Transaction Cost Analysis Software
Aston University now has a Research Data Management (RDM) policy, setting out the expectations that the University has of researchers with regards to the management of data arising from their research and the roles of the various individuals or services that will support this during the lifecycle of research data.

Research data are defined as factual records, which may take the form of numbers, symbols, text, images or sounds, used as primary sources for research, and that are commonly accepted in the research community as necessary to validate research findings.

In line with the RCUK Common Principles on Data Policy, Aston's policy recognises that research data arising from publicly-funded research should be treated as a public good, and made available to others wherever possible. We have begun to publish datasets supporting published research.

If you would like to know more about RDM, there is a suite of web pages to help you, covering the benefits of RDM, Data Management Plans, and Funders' Data Policies, or contact or your Information Specialist for advice.

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