Big Data Analytics and Enterprise Data Modeling are some of the critical concepts current industries require to thrive in current business environments. With the increased production and collection of data from multiple sources by organizations, it is now essential to deal with such data in a more structured way in order to derive the most from it while using it for decision making. These conceptions and their connection can give a better comprehension of the company’s data resources that businesses may find helpful.
Big Data Analytics means the analysis of Big Data sets as well as identification of unfamiliar relationships, tendencies and patterns that can be used for making correct decisions. The term ‘big data’ itself defines the huge amount of data delivered from various entities like social networks, sensors, transactions, etc. It is usually described by the four V’s: Variety, Volume, Velocity, Variability/Veracity, which present a problem for conventional data handling technologies. Hence big data analytics uses sophisticated technologies like Hadoop, Apache Spark and NoSQL databases to sort and analyze this type of data. These tools are used to work with structured, semi-structured, and unstructured data so that the organizations can transform this data into significant information and make decisions based on it.
There are several methods that can be used in the analytics process; some of these methods include; machine learning, predictive analytics, natural language processing, and artificial intelligence. Cohort, client, and open-ended aggregation applications allow businesses to accomplish something like client segmentation, fraud identification, prediction repairing, and sentiment evaluation. For example, in the retail sector, this concept can be used to provide insights into the patterns of consumer buying behavior and thus used in order to market products that will suit the consumers. In the same manner, health care will be improved by using it in forecasting diseases, attending to patients, and in managing the hospitals.
Enterprise Data on the other hand is cantered on creating a map of an organization's data as well as how the data relates with one another. It is an important process in the development of databases and data management systems that will address strategic plans of the organization. It mainly focuses on defining the elements, the relations between the elements, and structures in order to get consistent, high-quality data and enable easy access to it within the enterprise level. Enterprise Data typically involves three levels: This category includes conceptual models, logical models and physical models. The conceptual model gives an overview of the data and is business oriented, the logical model gives more detail as to the entity, attributes and relationships. The physical model is then used to transform the logical design into a concrete schema of the database; this is based on the technical specifications of the DBMS.
This component is an essential part of data integration, data warehousing and business intelligence since it provides the framework against which an organization’s data will be modelled. It makes it possible to bring together data generated in a company’s various departments and systems for use in reporting, analysis and decision-making. Another application of data modeling is for enhancing the ability to manage and govern incoming and processed data, its accuracy, consistency, and adherence to industry and legal norms. This is especially true for industries like finance, and especially health care organizations where data integrity and security remain critical success factors.
In a nutshell, the role of Big Data and Enterprise Data is quite complementary to each other in context. Whereas Big Data Analytics deals with extracting intelligence from big volumes of structured and unstructured data, Enterprise Data guarantees the former has quality and relevancy, with regards to enterprise specifications. If both of these practices are combined, organizations can establish a sound data management system to enhance decision-making. For example, a good data model may improve the standards and goodness of the data that go through the analysis process hence providing even better decision-making results.
Conclusion
It can be stated that both Big Data Analytics and Enterprise Data Modeling are critical for contemporary businesses that try to manage their data. Big Data Analytics provides the capability of a firm to digest large amounts of data for the purpose of discovering patterns, whereas enterprise data modeling provides a means by which organizations can systematically deal with such data. Arising from this, the stated practices enable businesses to make proper decisions, make existing processes better, and gain competitive advantage in the market. These concepts may be key as data will continue to become larger and even more intricate for organizations who want to succeed in the data-orientated future.