The contemporary business environment is characterized by proliferating data, growing customer demands, and shrinking budgets. It, therefore, calls for organizations to remain competitive by making the right decisions at the right time.

The business world has witnessed a paradigm shift over the past several years. It’s no longer imperative for business leaders to only rely on their judgment to make the right strategic decisions. Successful leaders have to be equipped with as much information as possible to enable better decisions. The insights that enable businesses to make better-informed decisions come by using a combination of past data, responding to the existing business needs in real time, and using a predictive modeling method to design a roadmap for future growth. Thus the need for big data!

What is Big Data?

Big data are datasets that have been gathered, stored, managed and analyzed by standard software tools. They generate plenty of value for businesses of all types and sizes. Organizations that can harness the power of big data benefit from the quality and operational efficiency, leading to labor and cost savings and ensuring a competitive edge. Leveraging the big data is also useful for companies to reduce errors, fight frauds and streamline processes.

Testing: Big Data Versus Traditional

Big data testing is about the verification of the accuracy of data processing rather than the testing of one individual component at of a software application at a time. The performance and functional testing are the key components of the big data testing process. To be effective, leaders need to enrol their employees for software testing training.

Types of Big Data Testing

Constraint and range validation: There exists a certain range within which a user is supposed to input information. For example, a date field can contain ten characters. The test ensures that the maximum and minimum data range constrained is always maintained.

Data type validation: The data type validation checks whether the provided input by the user matches the number of characters as defined in the existing algorithm.

Code and cross-reference validation: The validation checks the conformity of the data provided to the existing rules, data types and any other validation constraints. The data input is cross-referenced with a predefined set of rules to determine whether it matches the criteria.

Structure validation: Structure validation is comprised of combined basic data type validation in addition to complex algorithms. It may include testing of complex processes within the system.

The Traditional Database

The traditional database is a made of simple databases that are stored in fixed formats and fields in a given file. Example of a traditional database is spreadsheets or the Relational Database system which can only answer questions regarding how something happened. It provides an insight into a problem at a basic level.
The Volume of Data in the Traditional System Database

The traditional system could only store a small amount of data that raged from gigabytes and terabytes. However, the big data aids in the storage of large data amounts that may consist of hundreds of terabytes and beyond. The storage of large data amounts helps in reducing the overall cost of data storage and in providing business intelligence.

The Data Schema

Big data uses dynamic schema for data storage. Both the structured and unstructured information can be easily stored in any schema and can only be applied upon the generation of a query. Big data, on the other hand, is stored in raw formats and the schema can only be applied when the data requires being read. This is beneficial as it preserves the information that is present in the data. In the traditional database, it is difficult to change data once it is stored and can only be possible during reading and write operations.

Organizational information is typically inaccurate and incomplete. For a forward-looking perspective, it needs to be enriched with external information (big data). Traditional databases and approaches are inflexible and slow and cannot handle the complexity and volume of the data. Part of the challenge for organizations in successfully executing the big data strategy is the development of sound fundamentals that are flexible enough to address the organization’s data requirements of today and tomorrow.