DataOps is like the DevOps version of anything to do with Data Engineering

DataOps, or data operations, is the new kid in the block to spring from the collective realm of Big data and IT professionals. DataOps aims to nurture data management practices and processes to improve accuracy and speed of analytics, the core of which includes improved data access, data quality control, automation, integration, model deployment and the ultimate data management.

DataOps is all about reimagining how enterprises manage their data with a long-term goal for data vitality. Better data management has long term repercussions, it leads to better and more readily available data. Extensive data both in terms of quality and quantity makes way for lucid analysis, which paves way for better insights, business strategies, and the quintessential higher profitability. In a nutshell, DataOps strives to foster an integrated collaboration between data scientists, engineers, and technologists to leverage data for more power.

DataOps aims to introduce Agile Development into data analytics in a quest to balance innovation and collaboration so that data teams and users work together more effectively and efficiently. In a bid to lessen the end-to-end cycle time of data analytics, a process which starts from the origin of ideas to developing visualizations and models, data lifecycle relies upon the joint effort of people and tools.

Data Crystallization

DataOps aims to eliminate some of the ongoing turmoil within the operations of the enterprise involving developers and stakeholders. Lack of communication is often the reason behind bad data management, consider this for example, when someone in an organization requests for a new report there is often a sense of vagueness as to who will follow through on that request. It often happens, that someone may make a request, and data engineers deliver according to their understanding, which may not be the requisite information sought. This cycle can result in missed deadlines and mounting frustrations.

There is no definitive approach to implement DataOps, there are however few key focus areas to begin with-

Data Democratization

Data democratization suggests data accessibilityby all. The amount of data generated is growing by leaps and bounds. Estimates point that by 2020, we would have generated 40 zettabytes of data on earth which is 5,200 GB of data per human, however much of this data is trapped in silos, the need to access data is increasing. 96% of chief data officers voice that business stakeholders are demanding more data access, and 53% address that lack of data access acts as a barrier to drive better decision making.

Leverage data Platforms

DataOps practise requires data science platform with languages and framework support like Python, R, data science notebooks and GitHub. Besides, platforms for data movement, orchestration, integration, performance, are equally important too.

Automate for Momentum

To achieve faster turnaround time on data-intensive projects, data managers must automate manual steps that unnecessarily are time-consuming like data analytics pipeline monitoring and quality assurance testing.

Cautious Governance

When it comes to the relatively new DataOps, the traditional pillars of governance, the Centre of Excellence approach may not work. Until an enterprise establishes a blueprint for success and brings together processes, tools, infrastructure, priorities DataOps is contentious to govern.

Foster Collaboration

To implement DataOps, collaboration is indispensable. Tools, platforms, framework, governance must seamlessly support the people behind them. The goal is to achieve a larger objective of bringing teams together to use data more effectively.

As discussed, the long-term success of DevOps lies in bringing together two separate groups which make up traditional IT. The classic partnership between development work and operational work will determine how an enterprise has been able to revolutionise its Data Management unlocking the power of DataOps.

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