DataOps: The new way of data management
Data management has become one of the major challenges faced by organizations today. The conventional data processing methods are proven to be inefficient to handle the voluminous data generated by them. Hence, a new supplant for the traditional data management is required that can manage and prepare data analysis and reporting, giving scope to make strategic plans for better business outcomes. Data Operations, also known as DataOps, had revolutionized the data management process and has streamlined data ecosystems in the most unique way.
Data has an integral part to play in today’s digitally transformed world. With digitization further strengthening and reshaping all the industries, the data volumes are also increasing, giving rise to big data scenarios. The demand for rapid decision-making and real-time analysis is also on the rise to keep up with the new data-driven trend. DataOps is the most suitable method to automate the data management process for better analytics and decision making. It is also most compatible with modern DevOps environment, agile methodologies, and AI (Artificial Intelligence) ML (Machine Learning) integrations.
Understanding DataOps
DataOps is an innovative data management approach that combines advanced data governance and analytics delivery practices. It includes data management across the entire data life cycle ranging from data retrieval, data preparation, data analysis, to data reporting. It also protects data privacy, prevents data integrity, and offers restricted data usage. DataOps is popularly being used by organizations to manage data analytics in the most compelling ways.
Benefits of DataOps
- Improving data utilization: DataOps helps in efficiently exacting maximum value from the data. This process offers great scope to streamline the process, incorporate updates with ease, and improve competitiveness, thereby adding to higher business revenue.
- Solving issues of unstructured data formats: Organizations generate huge data which are both in structured and unstructured formats. It is difficult to extract insights from the unstructured data formats, which is considered to be one of the major business challenges. DataOps has the capability to efficiently identify, collect, and use data from unstructured formats.
- Improving speed: Extracting, analyzing, cleaning, and improving the data is a highly complex task and often not performed efficiently using conventional methods. The speed of the entire process can be improved drastically while retaining accuracy.
- Maximizing data productivity: In modern times, companies are depending on a data-driven approach for decision making. With organized and analytical data provided by DataOps, companies are making quicker decisions while improving productivity. The time-consuming, tedious data extraction process is replaced by automated DataOps systems.
- Offering right insights: Offering timely insights is one of the key functions of the data in any operating/development system. In conventional data management systems, the data insight is often provided late. Whereas, in DataOps the insights are given at the right time allowing the team to plan the future steps to be taken.
DataOps addresses some of the above-listed challenges efficiently, which are often a matter of concern for the other data management systems.
Increasing focus on DataOps with the emergence of Big Data
The emphasize on big data is growing with the onset of digitization. Companies are generating big data to sustain in today data-driven world. Big Data is an accumulation of voluminous data from which meaningful data can be extracted. The use of a conventional data management process is seldom preferred in big data as it is huge and highly complex. Management of such huge data requires advanced DataOps systems. DataOps helps companies to easily extract and use data for further business development processes.
Though DataOps offers an easy data management system for big data, one of the major concerns while handling big data is QA. Big data testing is a critical step that ensures that the big data functionalities and operations are performed as expected. Furthermore, it assures the big data system operates seamlessly without any flaws while maintaining security and performance.
Big data is the latest buzzword and big data testing is a crucial parameter associated with it. Though many companies are having a tough time performing efficient testing of big data, here’re a few tools that one can depend on for big data testing.
- Cigniti: Cigniti offers big data automated testing services. It’s testing solution leverage the power of AI-based predictive analytics to ensure best outcomes. It also provides big data migration testing solutions, instant data collection and deployment, live integration support, and real-time scalability
- Tenjin: Tenjin is the flagship test automation platform of Yethi, the global leader in offering software QA to banks and financial institutions. It is the 5th generation test automation platform with robotic UI capabilities that offers efficient testing solutions for BFSI software. If you are dealing with big data migration testing for any banking/financial systems, Tenjin is the test automation solution you can consider.
- Qualitest: Qualitest deals with extensive and automated big data testing to ensure impeccable data quality. It performs end-to-end test coverage, strategically customizes data, identifies errors, analyzes for optimal test efficiency while preserving the data integrity.
- XenonStack: XenonStack offers automated big data testing solutions. It helps structured and unstructured data and follows advanced procedures to check the informational index, patterns, and approach. It offers continuous end-to-end testing to ensure the over quality of big data while extraction and migration are retained.
- KiwiQA: KiwiQA is another big data testing company one can rely on. It offers efficient big data and analytics testing services that help in evaluating the collected data and deliver comprehensive data quality analysis.
The Future of DataOps
The need for DataOps is growing as companies are dependent on data-driven approaches like big data for decision making. Data being a key part of digitization, and digitization already transforming the course of various industries, the scope of DataOps is only going to increase. The rate at which data-oriented approaches are used across all the business landscape, the adoption of DataOps management systems is also increasing. Further, the incorporation of AI/ML will only strengthen the DataOps by offering higher agility and improved efficiency.
The pace at which organizations are leveraging data to transform their legacy and advanced systems, DataOps will be seen widely adopted. The scope of DataOps management system will remain prominent in the future and its use will not reduce anytime soon.
