Big data modeling helps organize the complexities of big data, improves Business Intelligence (BI), and allows organizations to benefit from actionable insight.
It is an extension of data modeling – a technique used in many areas of the Information Technology (IT) world to gain a better understanding of enterprise data resources. Models help represent and uncover the complex relationships hidden in big data stores. Some of the benefits of data modeling include:
- 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.
Big Data Defined
The amount of information available in the digital age can be 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.
Data Modeling Tools
IDERA’s ER/Studio family of data modeling tools offers organizations a suite of applications designed to help create conceptual, logical, and physical data models. Each tool is designed for a slightly different purpose and audience.
Business Architect enables the creation of conceptual and business process models that can be used to:
- Create conceptual and process models to align with business goals;
- Model the relationships between people, processes, and data;
- Export conceptual models to ER/Studio Data Architect to build a logical model;
- Identify sensitive data elements for better regulatory compliance.
Data Architect provides the functionality required to perform many key data modeling activities such as:
- Building out data models during database development;
- Discovering and documenting existing data assets;
- Creating and populating data warehouses;
- Cataloging metadata for data quality or governance;
- Analyzing the impact of new policies and changes to models and databases.
Enterprise Team Edition is a collaborative tool that enables data models and metadata to be shared across an enterprise. Some of the activities it supports are:
- Building out enterprise data models;
- Discovering and documenting assets from across the data landscape;
- Determining and managing data sources;
- Building the foundation of a data governance program;
- Ensuring consistency between data models and databases.
ER/Studio can help organizations tame the challenges of modeling big data and enable the productive use of information resources to improve business intelligence. Take advantage of the benefits that data modeling can bring to your organization with these excellent modeling solutions.