Big data involves large volumes of data that inundates the traditional relational databases of an organization.
Big data not only refer to data stored in relational databases but also includes both structured and unstructured data. Analysis of the unstructured data plays an important role in decision making.
Before analysis of the big data, data should be modeled. Big data modeling focuses on finding similarities between data from the various sources and also confirm whether the data describes the same thing.
Characteristics of big data systems.
- Large volumes of data: High volumes of data are collected from social media, business transactions, and machine-to-machine data sources.
- Variety of data formats: The data collected is in various formats- it can either be structured or unstructured. E.g. data from documents, files, audio, video or web content.
- The velocity of data: Data from the various sources comes in at unprecedented speed and it must be processed in a timely manner.
Integrating these data sources into the business ecosystem will enable you to understand and gain a valuable insight into the data.
Organizations use big data modeling techniques to organize corporate data in order to meet the business processes needs.
These modeling techniques give insights into your organizational data. The data is defined, categorized and standard descriptors are established so that all the information system departments in the organization consume the data.
SQL relational database technology links datasets keys and data types together to provide information needs of the organization business processes. Due to large volumes of data from a variety of sources, big data no longer run on relational databases architecture. Non-relational databases like NoSQL is used to run and model big data.
Tips on how to model big data.
- Use design system instead of a schema
Big data models are built on systems, unlike traditional data which relies on the relational database schema. Big data require NoSQL database which does not support schema to store different types of data from multiple sources.
- Use Big data modeling tools: Modeling tools like Hadoop have made big data analytics simple.
- Focus on core business data: There are big volumes of data that stream in every day, therefore, before any data modeling, always choose data that is relevant to your organization.
- Deliver quality data: Instead of focusing on creating metadata to define and describe where the data come from or its purpose; you should focus on knowing on each piece of data collected and how you can properly place it in the data model to support your business. This improves the quality of data.
- Determine the key inroads into your data: There are many common entry points of big data in the market. Determine your key data entry point and design data model that support this key information access into your company.
Big data modeling techniques
- Understand the business requirements and the needed results
Data collection and analysis of stored data helps in determining the organization needs. Knowing the business requirements helps you to know which data to collect, prioritize or transform.
- Visualize on the data to be modeled
Graphically presented data helps you to normalize, inspect and join table data. Using these data visualization tools, you will be able to clean the data and ensure it is consistent, complete and free from any errors and duplications. You will also spot data record types that correspond to the same entity.
- Start from simple to complex modeling
Make your data models small and simple at the beginning to easily identify and correct any errors. Once there is accuracy in the original data, you can add more datasets and eliminate any inconsistency in the data.
- Split business inquiries into facts, filters, dimensions, and order.
Organizing your data into the above elements helps you to easily analyze data and use historical data to determine future trends in the business.
- Only use the needed data rather than the available data
Dealing with large data sets can reduce the performance of your computing system. You only need a fraction of the data to answer most of your business needs therefore, you should focus on the small datasets needed for your decision making.
- Verify each step before going to the next
You need to verify each step of the data modeling before going to the next step to ensure all the required datasets are satisfied.
- Make the data models flexible
Business objectives change continually, therefore, the models used should be easy to update and change over time. You should store them in a data repository which can allow you to modify the data sets.
Benefits of Big data modeling
- To manage your data resource
Data modeling will enable you to normalize the large volumes of data based on information contained and its attributes. It also enables you to query the database and generate reports.
- Integrate existing information
Sometimes you may have data in different systems which do not communicate with each other. Modeling the data will enable you to see the relationships between the data, any data duplication and then integrate the systems to work together.
- Helps you design your data repository and database
Modeling helps you to understand your organizational data and make the right decisions in regard to an organizational database or data repository. The ability to describe the organizational data helps you to understand the organizational data storage needs.
- Helps in understanding the business
It helps you to understand how your business works so that you define data that drives it. That is, know what data is gathered and how it is used. Knowing this information will help you understand your business processes.
- Business intelligence
After collection of data from multiple sources, including report generation represents you with business intelligence opportunities which were nonexistent before. Through modeling of the data and reporting, you will spot business trends and patterns which help you make the right decisions.