For a long time, DevOps has been a long standing practice in the software development world that has helped monitor, validate and automate workflows. In the midst of the dotcom era, software products have grown to be more robust, thus the probability of bugs and major technological malfunctions due to human error has increased. So, the implementation of a formal system that constantly observe the efficiency and quality of product code is necessary to reduce the consequences of human fallacy—and rather capitalize on the benefits of collaboration.
Data engineers also have a similar practice that helps protect and govern the sophistication of data-related workflows called DataOps. Similar to DevOps, DataOps is a collaborative data management system that:
- Automates workflows through CI/CD (continuous integration/continuous deployment).
- Validates and Tests data for accuracy and quality.
- Continuously monitors data pipelines for best practices and anomaly spotting.
As businesses are becoming more data-driven, the demand for high-quality datasets at scale has become much more prominent. However, without a systematic effort to build logically sound data management practices, ingestion bottlenecks could easily arise and end up hurting the business more than helping, these bottlenecks can manifest in the form of: data outages, inaccurate dashboards and poor data quality.
On the other side, when a business has put a concerted effort into instilling DataOps, the benefits could be innumerable. With an secure data pipeline, several other actors within the organization can develop key products that help drive the right decisions:
- The Data Analyst can build KPIs and dashboards for internal reporting.
- The Data Scientist can feed the data into statistical and predictive models to de-fog the uncertainty of business decisions.
- The ML Engineer can use the dataset to train algorithms that help improve products and services.
Altogether, DataOps is a perfect model to implement if one wants to wrangle their data at scale and make sound data-driven decisions.