We have progressed considerably from when enterprises used to believe that secure, accessible, and reliable storage was the only major obstacle to their data initiatives. In today’s digital economy, data is the new oil, and businesses that tap deep into their data pools uncover value faster. They lead the race to create winning customer experiences, make workflows smoother and more efficient, and ultimately improve business outcomes. Enterprises are in a race to deploy data for ever more use cases. As you would imagine, speed is a prime requirement since this is a race.

Data takes multiple dimensions today, and organizations strive to achieve a perfect balance of discovery of data and consumption of insights from the data between different stakeholders in the business. At the same time, this is about processing vast volumes of data. It is also becoming increasingly about vast volumes of data FAST!

This sets the stage for introducing DataOps into an enterprise’s data management environment.

DataOps refers to any initiative that aims to streamline data management across an enterprise and accelerate the supply and consumption of quality data on-demand within the organization by different stakeholders. Driven by automation and other technology tools, DataOps draws inspiration from DevOps, a key pillar of successful technology operations in almost every business.

DataOps is becoming more popular among enterprises due to the importance of quality data in decision-making across a business’s operational environment. From finance to marketing, different departments consume a wide range of analytical insights derived from the underlying operational data flowing between other enterprise systems and digital channels. Both speed and quality are extremely important from a data perspective, as the relevance of a data point may change significantly with every passing second. Businesses that have the agility to create analytical infrastructure designed to address real-time data are more likely to enjoy success in today’s highly competitive markets.

Let us have a closer look at the key opportunities that DataOps presents for enterprises today:

Encourage Automation

Automation will be a fundamental driver of success in any digital-ready business process. This holds for data processes too. An organization’s data flow is a constantly evolving framework. However, underneath the numerous platforms and protocols being leveraged to handle data, the core activity across stages is the entry of data in one format into a system and the exit of the data in another or the same format depending on the level of processing occurring in the system. As organizations gear up to meet the dynamic needs of customers across their digital channels, this flow of data needs to be quicker.

Both data and insights must be available in real time. With DataOps, enterprises can deploy considerable automation within data management across different business systems. There is a constant supply of real-time data-driven insights which ultimately help in deriving better ROI from data investments.

Unifying Data Operations

With DataOps, team members from different departments can align and follow a common standard for data quality, incorporating best practices and tools that support interoperability and faster data exchange. Thus, DataOps enables a cultural shift in how enterprise data is collected, managed, and shared between teams. DataOps sets the stage for a more collaborative environment with uninterrupted communication between people from diverse teams and a common company objective around data analytics and data management.

Emphasize on Reusability of Data Assets

Implementing DataOps will usually result in better standardization of the data artifacts and models being leveraged across the organization. This will further aid the reusability of data assets across the enterprise. Their repeatability provides much more value and cost efficiency when compared to building a data asset from scratch and following up with synchronization of the same with the current digital ecosystem of the business.

Improve Data Quality

DataOps brings in more complete and transparent processes to enable technology-driven cleansing and structuring of raw data across different business systems. The structured data is then channelled quickly across other data pipelines, and the requisite insights are transferred to dependent business systems. This greater emphasis on data quality is achieved by using technology. More often than not, the end products, such as data assets and models, as well as the related artifacts, are guaranteed to be of the best quality.

The Challenges of Adopting DataOps

Of course, only some things are smooth sailing while adopting DataOps.

While enabling a better data-driven culture of operations within a business, unrealistic expectations from the available data pipelines often need to be revised. It’s not always clear that the business has the means of the processes to collect, clean, secure, and manage the data such an initiative demands. The technology to process that data and deliver the real-time insights DataOps can provide is also not easy to adopt, develop, or implement.

Then there is the people and culture challenge. Driving a data-driven business forward requires a greater emphasis on following and accepting greater standards of transparency, accountability, and agility within the organization. It is not easy to drive through and establish such a radical transformation.

Data management and governance is another key area for improvement. Processes need to be designed, codified and tracked to ensure data quality. For security, it’s crucial to determine where and who manages different types of data entering the system.

Also, for the success of any DataOps program, it is important to incorporate the right technology tools and digital infrastructure to handle the influx of data in modern enterprise systems. Changing or moving away from legacy data management practices and tools into modern distributed and cloud-based data management and security tools calls for a more specialized focus on controlling the overall DataOps ecosystem.

This is where your enterprise can rely on our dedicated big data managed services to create a self-sustainable data infrastructure with high growth potential. Get in touch with us to know more.

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