Business Intelligence over the last decade has been transformed beyond recognition by digital transformation. Every system and device is able to produce a digital trail of data, stored in datacentres or in the cloud, that can be reacted to in real time. This type of analytics is called DataOps.
The complexity of DataOps
This type of super modern analytics brings about three challenges:
1. The architecture of data systems is an increasingly complex beast, as relatively simple data warehouses evolve into a variety of complicated pipelines that may be controlled centrally, but also need to share data with each other.
2. There is a danger of operational blindness when data flows and problems can’t be tracked due to poorly instrumented tooling, coding, or frameworks.
3. Data drift – unexpected changes to the structure of data – is on the increase due to data diversity and fragmented change management. If data drift is not addressed, it can pollute data and interrupt the analytic workflow.
All three of these challenges can lead to escalating costs, destroyed opportunities, and a risk to data privacy.
The demands on DataOps of modern analytics
A new approach is required to overcome these stumbling blocks. One which is invariably focused on the improvement of engineering productivity, operational efficiency, and architectural agility. All of these improve business’ confidence in the completeness and management of the data.
The above challenges are due to the characteristics of modern data’s architecture and how the movement of data provides sticking points, rather than problems within the systems themselves.
Ten years ago, there was a similar crisis in the software engineering space which brought about the DevOps revolution, and a new agile automotive system for data monitoring.
Automation in software engineering meant more frequent, tightly controlled, and higher-quality application updates, as well as the standardization of code.
DataOps comes when DevOps principles are put into practice. This refers to the automation and monitoring of all data analytics in the hope of making a reduction in its delivery time, and aiding companies to provide improved and faster applications.
Automation and monitoring - the key to data integration
Automation is put into practice to facilitate a reduction in manual labour, cost of delivery, and quality control. Data pipelines are crucial, as well as best practices. Pipelines must be adaptable and able to respond to data source changes and storage demands. Automation needs to be attached implicitly to both – the build and the operation lifecycle of each line of dataflow architecture. This will bring about an increase in productivity, enable the fine tuning of pipeline logic, and make sure that changes can be adapted to quickly.
Monitoring counts most when it comes to unexpected problems and maintaining consistency of data delivery. Every point in a data pipeline must be monitored to keep a feedback loop maintained. This allows for data movement and performance across a whole bandwidth of dimensions, like data delivery, privacy, and quality. It also accommodates reliability and agility and keeps a check on the performance of data flow, its use, and characteristics.
Automation and monitoring, combined, enable the speedier delivery of all projects and a greater operational efficiency.
The DataOps mindset
Companies can make more reasoned decisions and take beneficial actions faster if they make good use of self-service analytics. However, they can put pressure on the data supply chain because they increase the number of moving parts in the pipeline and affect issues of urgency and changing consumable data.
The crucial principles of automation and monitoring in DataOps must be constantly applied to data integration.
DataOps is fundamental to the flow, collection, and storing of data and streamlining it through all pipelines. The concepts of DataOps provide a framework for data practitioners to become more operationally focused, which is crucial as we head into a data-driven future and need more and more solutions to data activity complexities.