Through big data modeling, data-driven organizations can better understand and manage the complexities of big data, improve business intelligence (BI), and enable organizations to benefit from actionable insight.
Big data modeling is an extension of data modeling, a practice adopted by many areas of Information Technology (IT), used to better understand enterprise data resources. Big data modeling helps organizations represent and uncover the complex relationships hidden in big data stores.
Below, are some of the key benefits of data modeling:
– Reduced software and database development errors;
– More consistent documentation and system design;
– Improved database and application performance;
– Enhanced data mapping and communication for developers and BI consumers;
– More efficient database design and implementation.
– Learn more: The benefits of data modeling for data-driven enterprises
Big Data Defined
In the era of data-driven business, the amount of information organizations are faced with managing and processing can quickly become overwhelming. The term Big Data was coined in 2001 to describe the growth of data streams and resources. It was originally associated with what are called the “Three Vs” (volume, velocity, and variety), which have been supplemented by a few more characteristics that all begin with the same consonant.
The Three Vs
The “Three Vs” include:
- Volume – The sheer volume of data makes it challenging to locate the data that is important to an organization.
- Velocity – The velocity with which data is generated is steadily increasing, making it difficult to process promptly.
- Variety – Data streams provide information that is both structured and unstructured. Enterprise data resources can include documents, videos, emails, and many other types of digital information.
The Seven Vs
The “Seven Vs” include the “Three Vs” plus:
- Veracity – The quality of big data sources affects their usefulness for generating BI. Trusting data quality is mandatory as the world moves toward greater automation.
- Variability – Data’s meaning can change based on how it was generated. Natural language processing is difficult due to the nuances involved in communicating with words but is necessary to address big data resources.
- Visualization – Understanding the insights uncovered in big data stores requires innovative methods. Visualization enables complex data to be presented in a form that can be easily understood by all stakeholders.
- Value – The financial benefits of understanding an organization’s big data resource is the main reason a company would want to spend the required time and effort.
Extracting Business Intelligence with Data Modeling for Big Data
Business Intelligence (BI) can be defined as using software and services to turn data into actionable insights that influence an enterprise’s strategic and tactical business decisions. Dedicated BI tools are used to access and analyze data so it can be used efficiently throughout a business. Effectively using enterprise data resources can provide an organization with a substantial competitive edge over market rivals.
Big data lives in big databases. Big data modeling is necessary to make the information available for use in BI systems and by consumers. Some changes in the way data modeling is performed may be required to get the most out of big data modeling and assets. Following are some of the characteristics of dimensional data modeling for big data:
- Using snowflake schemas instead of star schemas to improve query execution with more granular tables
- Reducing the use of surrogate keys in favor of natural keys to facilitate database maintenance
- Refraining from using Type-2 SCDs
- Introducing the concept of snapshot dimensions that make it easier to detect corrupted data in big data stores
- Strategic use of denormalization if the value of the attribute in question is immutable
- Embracing the complex data types that are inherent in big data streams
Data modeling techniques can help organizations address the “Vs” that characterize big data. They make it easier to handle the volume, velocity, and variety that characterize big data. The veracity of data resources can be ensured with robust models and variability can be identified and addressed more easily.
Data models help design databases that can be used for visualizing the insights found in big data. The value of corporate big data assets is made accessible through data modeling and allows the information to be used to improve enterprise business intelligence.