Companies are increasingly aware of the importance of Big Data to make the best decisions, able to determine a competitive advantage over other market players.
The same phenomenon of the Analytics Divide is symptomatic of the fact that to date the issue is shifting more and more from the possibility of “accessing” big data, to the ability to “manage” them in an agile, efficient and strategic way, reducing waste of resources and work overloads.
In this context, DataOps provides the tools, processes and organizational structures to manage and make sense of the huge volume of data generated by the increasingly massive use of big data, IoT and AI.
What is the DataOps methodology
DataOps (or Data Operations) is a methodology, set of agile software engineering practices, processes and technologies (DevOps) applied to data, to improve the quality, speed, collaboration and promote continuous innovation of data analysis.
According to Gartner’s definition, DataOps is “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data management and users.”
Basically, its goal is to make available the data needed, at any time, to all users who need it:
- making processes more “streamlined”
- eliminating waste of resources and work overloads
- avoiding interruptions in the production flow
- accelerating the collaborative work
- through a model of continuous improvement.
DataOps therefore not only acts on the infrastructure (improving data warehouse and data lake management alone are not enough) but also at the level of:
- data: controlled, reliable, real-time
- people: facilitating the collaboration
- processes: Reduce time to value
Let’s see how.
Continuous process and pipeline improvement
DataOps based on continuous integration practices and continuous distribution.
Continuous integration allows you to make changes to the pipeline at any time: changes are saved and tested in parallel environments without affecting the source code. Once successfully verified, the changes are integrated into the source code and distributed continuously, without disrupting functionality (continuous distribution).
These features are essential to automatically detect and correct any errors and malfunctions. In particular, the DataOps method is effective in eliminating biases (cognitive distortions) that AI learns from humans through ML and Deep Learning algorithms.
In a fully automated (end-to-end) data pipeline, quality verification tests are automated too. It is easy to imagine the impact that the DataOps methodology can have for Clinical Trials and in general in companies operating in the Life Science sector.
Benefits of DataOps in the Enterprise
The data available to companies continues to grow exponentially, as a first effect they generate increased pressure on workloads. The natural consequences of such a condition are the slowdown of performance, the increase of the times in order to obtain meaningful analytics, the loss of the optimization of the resources used and a general negative impact on the business competitiveness.
The DataOps methodology helps to redefine the proper management of business data flows, for example:
- Data from different sources: how to organize the collected data in order to avoid duplication and overload?
- Data governance: who has control and responsibility over data?
- Data integration: How to unify a data flow that includes on-premise/cloud systems such as databases, data lakes and data warehouses?
Among the companies that have already successfully implemented the DataOps methodology, we primarily have those operating in the Heathcare and Clinical Trials sectors: the quality of the data collected and the speed with which the information is made available to the various participants, can greatly shorten the time of creating a new drug.
Logistics and Supply sectors can also benefit from real-time analysis to connect production and shipping processes, optimizing resources.
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