Harnessing the Power of Big Data Analytics and Master Data Management

 As society advances unabated to embrace the digital age, it is worth pointing out that data is the new oil. More so, every organization from different industries is now adopting big data analytics and master data management (MDM) to foster innovation and support their decision making all in a bid to gain competitive advantage. This article discusses the importance of big data analytics features as well as MDM to provide a complete understanding on the utilization of data.


Big Data Analytics


Business intelligence is applied on big data, which is a collection of a large and multiple kind of data that is analysed with the intention of identifying various patterns, relationships, trends, and customer preferences in the market. The main purpose is to achieve the essential business intelligence that may make an impact on the decision making and strategies. Here are some key aspects of big data analytics:

  1. Volume, Variety, Velocity, and Veracity (4Vs): Volume, Variety, Velocity, and Veracity (4Vs):


Volume: Big data on the other hand is data of immense size and variable volume, velocity, variety and value from sources like social media, sensors, transactions among others.


Variety: The data can be categorized based on different structures it follows; these include structured data, semi structured and unstructured data.


Velocity: The rate of production of data and a measure of how fast data is transformed into other forms.


Veracity: The credibility of the data collected, meaning whether it is relevant or not, authentic, and accurate.

Advanced Analytics:


  1. Predictive Analytics: Exploitation of historical information to extrapolate future results.

Descriptive Analytics: Gives some perceptions of the past data in order to learn the patterns.


Prescriptive Analytics: Provides recommendations for action that will help achieve specific goals, given the results obtained from processing data or other materials.

3. Technologies and Tools: Today’s big data analysis is based on a multitude of technologies, from Hadoop and Spark to NoSQL databases and machine learning algorithms. These tools assist in dealing with large data resources and analysing them for meaningful purposes.


4.Applications:


Healthcare: The following are some of the potential uses of the predicted value and the consequences of a disease outbreak.


Retail: Customer segmentation, understanding, and evaluation for the sake of marketing.


Finance: For instance, fraud detection and risk management are the types of activities that lenders can make efficient by leveraging big data analytics.


Manufacturing: benefiting from effective utilization of predictive maintenance as well as smart supply chain.


Master Data Management (MDM)


Master Data Management is a systematic technique that can be used to establish plans and guidelines for the valuable data of the organization to support data integration, that is to serve as a single source of reference. This helps eradicate the problem of data inconsistency and follows the best practices of data control in organizations. Key components of MDM include:


Data Governance: I therefore recommend that we develop procedures that have to do with data management to avoid lapses in the accuracy and consistency of data that’s collected as well as to ensure security.


Data Integration: When I was using computers, people, and other means to get information, I was integrating the information sources in a way that presented a single view of this information. This is such as data transfer, details harmonization and data rectification.


Data Stewardship: Centralizing EMC to oversee the accountability and compliance with governance guidelines and protocols.


Single Source of Truth: Establishing master data management that will allow proper storage and sharing of correct and consistent master data for all the business units.


Data Quality: Enforcing the rules to check data integrity and validity, data transformation in order to improve the quality of data.


Synergy Between Big Data Analytics and MDM


Master Data Management is a systematic technique that can be used to establish plans and guidelines for the valuable data of the organization to support data integration, that is to serve as a single source of reference. This helps eradicate the problem of data inconsistency and follows the best practices of data control in organizations. Key components of MDM include:


Data Governance: I therefore recommend that we develop procedures that have to do with data management to avoid lapses in the accuracy and consistency of data that’s collected as well as to ensure security.


Data Integration: When I was using computers, people, and other means to get information, I was integrating the information sources in a way that presented a single view of this information. This is such as data transfer, details harmonization and data rectification.


Data Stewardship: Centralizing EMC to oversee the accountability and compliance with governance guidelines and protocols.


Single Source of Truth: Establishing master data management that will allow proper storage and sharing of correct and consistent master data for all the business units.


Data Quality: Enforcing the rules to check data integrity and validity, data transformation in order to improve the quality of data.


Conclusion


Specifically, big data integration with master data management is a key priority for companies seeking to use their data as a competitive advantage. The importance of data quality and the opportunity to make available sophisticated tools that can indeed better manage this asset can lead to development, higher efficiency and hence competitiveness. Thus, integration of big data and MDM will steadily rise in importance as data flows, types, and sizes develop into a critical factor of ongoing business success.

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