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DataOps and Hybrid-Cloud Models to Govern the New Data Security Strategy

DataOps and Hybrid-Cloud Models to Govern the New Data Security Strategy

Mayriya: DataOps and Cloud Computing

The unstoppable world of the 21st century stood still due to a pandemic, processing a transformation that no one imagined. While the health care and pharmaceutical sectors are the ones that experienced a complete overhaul, the impact on IT businesses is quite evident and significant to note. One of the most crucial transformations was the introduction and survival of remote work culture. The new norms have also conceived the necessity of distributed infrastructure for enterprises. However, that’s not the only trend within the IT realm that’s turning the heads.

 

Data management has become one of the most crucial pillars for businesses to survive the pandemic wave. With the internet activity increasing from the end-user point, the data influx is at an all-time high.  The amount of enterprise data is also expected to grow from 40 to 60 percent in a year.  Many organizations are facing new challenges in terms of data management, analysis, and interpretation because of the increased data volume.

That’s where DataOps is grabbing the spotlight. The new methodologies are being looked upon as the key players in transforming the data management processes and even extend functionalities to enable hybrid and multi-cloud management systems.

What is DataOps?

According to Michele Goetz, VP and Principal Analyst at Forrester, DataOps is “the ability to enable solutions, develop data products, and activate data for business value across all technology tiers from infrastructure to experience.”

In other words, it is an agile operation methodology that allows a company to improve its data usage by using advanced tools, automation, and collaboration.

Lending its principle from DevOps, it applies the idea of more agile and collaborative software development to data analytics, creating a smooth data pipeline. Utilizing DataOps not only enhances the accuracy and speed of data analytics but also offers better data quality, improved automation, and integration of the data. In addition, the methodologies also assist in the development and deployment of data products and applications

Benefits of DataOps

The benefits of DataOps run in a cycle, throughout the enterprise operations.

To begin with, the DataOps methodologies provide cleaner data. It means the organizations have access to better business insights and improved analytics. Since the organization has better analysis, the collaboration among the different teams is smooth and quick, including data engineers, data scientists, management, and development teams.

As the data becomes more manageable, actionable, and efficient, the companies are in a better state to achieve their financial goals and grow business. In addition, the deployment of DataOps lowers the cost of data management by creating and managing central data repositories for data models that further open deep layers of analytics, allowing the companies to leverage data fully.

However, limiting the benefits of DataOps to data management would be undermining its capabilities. With hybrid and multi-cloud management grounding its roots further, DataOps is expected to become a significant player for securing the new data environments.

Application of DataOps in Hybrid & Multi-Cloud Management

Cloud management is gaining strong momentum in the business world. Whether it is due to the large data inflow during the lockdown phases or a much-awaited move from companies, but more and more businesses are now moving towards cloud management.

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It brings us back to the data management over the cloud systems, and security is one of the key focusing points. With cyber hacks and breaches still haunting the corporations (It is still early to forget the SolarWinds cyberattack), companies are looking for a more feasible approach that involves adopting a fine-grained approach for data security. The organizations are now targeting the vulnerabilities that may exist in data, applications, devices, and user end-points. The underlying idea is to protect data at a granular level making the data secure as a whole. At the same time, adoption of the hybrid and multi-cloud environments will grow further, enhancing data security.

For some, DataOps may be a new approach. However, the methodologies will become crucial in securing the multi-cloud models.

The question comes how?

DataOps can help businesses creating a single point of management and ensure how data is being accessed and utilized throughout the network infrastructure. This can be achieved via an abstraction layer that acts between data and applications. The result is a uniform application of data policy and governance, including a strict check on the holes in the data pipeline.

To Conclude:

Few companies like Starburst and Denodo and open source frameworks like Hive and Spark have shown a keen interest in the new data security strategy. While cloud journeys are still young trends, a confluence with DataOps current will be a fascinating watch in the process of making ‘digital gold’ aka data safer and cleaner.

 

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