DataOps – Opportunities And Challenges
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, since this is a race speed is a prime requirement.
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. While this is about processing vast volumes of data. It is also becoming, increasingly, about vast volumes of data FAST!
This sets the stage for the introduction of 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 which is already a key pillar of successful technology operations in almost every business.
DataOps is becoming more popular among enterprises largely due to the importance that quality data plays 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 different 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 present for enterprises today:
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 but 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 a considerable amount of automation within data management across different business systems. There is a constant supply of real-time data-driven insights which ultimately helps 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 the way enterprise data is collected, managed, and shared between teams. DataOps sets the stage for a more collaborative environment that has uninterrupted communication between people from diverse teams aligned to a common company objective around data analytics and data management.
Emphasize on reusability of data assets
The implementation of 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 against 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 from across different business systems. The structured data is then channeled quickly across different 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, not everything is smooth sailing while adopting DataOps.
While enabling a better data-driven culture of operations within a business, there is often the challenge of unrealistic expectations from the available data pipelines. 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 deliver 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’s not always easy to drive through and establish such a radical transformation.
Data management and governance is another key stumbling block. Processes need to be designed, codified, and tracked to ensure data quality. For security, it’s crucial to acquire visibility into 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.