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Effective Master Data Management instills governing measures to maintain data en masse through aggregation, consolidation and standardization. A lack of MDM strategy can lead to faulty decision- making and loss of speed in business growth. By administering the right MDM procedures create a unified source of reliable information which can be efficiently modified via multiple entities within a business organization which naturally optimizes all operations.
Therefore it is crucial to understand and analyze Master Data Management in order to apply it to nay organizational structure. Henceforth, in this article we have compiled 8 most essential MDM test cases to help gain a more comprehensive understanding applied MDM.
Part 1: Must-Read 8 Best Practices for MDM
PPart 2: What Could We Learn from These Practices for MDM
MDM is an evolved form of IT enterprise strategy which views master data as an essential asset to a company that is comprised of multiple data points. These may include entities such as suppliers, customers, accounts, transactions, plans, goals, accounts and blueprints, all of which represent the core of a business's functioning. Once these groups of data have been standardized into master data, it enables users to analyze them so as to highlight the key metric areas which facilitates the making of crucial business decisions.
Appropriate MDM practices or solutions enhance accuracy and governance in an organization. Hence, let us have a look at some of the best use cases to better our understanding of MDM.
It is known to be the primary subject of the most common Master Data Management techniques known today. Party Data, which is also known as ‘Customer Data' comprises of the complete information which is in possession of a company with regards to an individual. Often in many case scenarios, business organizations and enterprises may also be treated as an individual by other enterprises.
Customer Data is accumulated primarily to compile Customer Master Data which is managed through the use of workflows, batches and transactional processes. In most cases, B2C and B2B data management processes differ from one another. In case of B2C, master data entry points are not technologically controllable while B2B entry points are.
Another more commonly noted style of MDM applies to Product Data. There are two different approaches which apply to this type of data; one from the buyer's side and the other from the seller's side. The ‘buy-side' master data is based off the supply chain of an organization. It involves collecting data from suppliers through the use of MDM software. It is also a form of inventory data management.
The focus of the ‘sell-side' is on providing customers with essential product information which is interfaced with life-cycle management or even resource management. These sources of data is usually managed with the use of a workflow. The sell-side MDM is more customer-oriented and involves relaying organized information to end-consumers.
The advent of Multidomain MDM has fired up a new raging trend in data management. Up until now, enterprises were heavily dependent on a best-of-breed approach involving multiple vendors with separate functions to assist in the management and maintenance of a single domain. But now with Multidomain MDM, buyers can rely on building data management governance infrastructure using a single tool for the job. This makes the previously complex process of training and orientation more simplified as all users are now under the same framework utilizing a singular set of languages as referential data across all domains.
The constantly growing sophistication of MDM requirements is further enhanced through the introduction of Data Wrangling. It is the process of cleaning and unifying vast sets of diverse and un-organised data into a heterogeneous language. This advancement has become ever so useful given the exponential growth of organisational data. Data wrangling facilitates effective Master Data Management as it involves reformatting and mapping massive data into a more simplified version for smoother consumptions, organisation and analysis. Modern day business enterprises are integrating Data Wrangling into their MDM strategy to further separate themselves from the competition through enhanced speed and accuracy.
The constantly growing faction of mobile phone operators, cellular communication operators and wireless service providers are challenged with operating multiple incongruent systems of MDM and storage. Their goal is to integrate all that data into a singular view otherwise juggling with several versions of the truth.
The only way to do so would by installing a system that effective identifies inaccurate data, runs clean up at the source and provide viable results. The MDM platform designed by TIBCO offers to prescribe such a system as a middle layer of filtration which functions as a central point of synchronised collection and point of reference for enhanced integrated MDM.
It is common knowledge that poorly designed MDM systems create gaps in your organisational effectiveness. Most legacy systems rely on sub-optimal databases that hinder responsiveness. In such scenarios, graph database technology has proven to be most effective given that it provides organisations with a competitive edge through storing, querying and modeling metadata, hierarchies and endpoints in your mater data. A prime example of this instance is when Airbnb developed a self-servicing and well-integrated data portal which gave a holistic view of all data which is easy to navigate by users. In this context, large sets of information were organised into graphs for accessing millions of data connections in a matter of seconds.
Similar to the adopted process of Data Wrangling, Operational MDM follows the objective of identifying faulty data and cleaning up master data at the source in order to use the result in all matters related to the business. To do so, Operational MDM extracted real work business information from users to resolve bad master data. This would effectively change the overall MDM strategy of a business organisation. Such an instance of MDM use case can be seen in Business Intelligence (BI) Land. Here, operational MDM is used to identify original data sources to gather, clean up and store a multitude of information in order to perform analysis within the business organisation.
Once business organisations begin to use applications, their prime objective reverts to streamlining master data in terms of quality and governance. Enterprises are usually faced with the issue of redundant data collected from multiple systems, lack of standardization and un-synchronized arbitrary creation of data. By using SAP NetWeaver these companies were able to usher a streamlined process that involves customers requesting the creation of master data themselves.
This improvement leveraged specific MDM services and a pre-installed user interface to reduce the effort of implementation while increasing flexibility and consistency. BPM companies are now able to leverage new entries of master data which triggers a process of recording and automation.
Key Takeaways from This Episode