By Umme Sutarwala - March 10, 2022 4 Mins Read
Data management and delivery get delayed due to the rapid growth of data volume and types, along with the many types of data citizens—data scientists to data users. As a result, most businesses and Chief Data Officers (CDOs) struggle to make effective use of the data they acquire in order to produce value or deliver timely insights.
Enterprises are actively investing in information technology (IT) to ensure the efficiency, productivity, and innovative endeavors of their data teams in this Digital Age, where data and technology take center stage. CDOs are required to add value to the business by effectively leveraging available data, responding to requests, and ensuring team productivity while overseeing all data management processes.
In today’s data-driven competitive environment, disregarding the benefits of data, or even failing to harness its full potential, can only lead to disaster for businesses. Many of these businesses amass a lot of data, but, they either don’t want to use it, don’t know how to use it, or don’t have the systems in place to do so.
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Here are four critical factors to bear in mind as businesses implement a DataOps strategy to help turn their business into a data business:
One of the biggest hurdles to the adoption of intelligent technologies that have the potential to transform an organization’s decision-making capabilities is data access. For greater insights related to dynamical factors and real-world scenarios, AI technologies require regular training with new data.
For collaborating DataOps teams working on data initiatives, democratizing data access—while remaining compliant with regulatory and security frameworks—can be a game-changer.
Data is brought together by people, such as data or ETL engineers. There are also data scientists and artificial intelligence (AI) specialists, as well as professionals that undertake data visualization or work in a self-service capacity. In addition to the people that handle the production operations, there are also the more business-oriented and data governance people who focus on data hygiene.
Everyone has their own set of tools and methods. A successful DataOps approach requires the right individuals who understand their ability to eradicate silos, either by their actions or their tools.
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Data processing necessitates a great deal of automation, from data transformation to infrastructure provisioning and capturing metadata. The proper application of automation in the repetitive data flow saves time on resource-intensive operations.
However, automating everything, may not be the ideal approach. Automating unnecessary processes will deteriorate productivity
The premise of DataOps is that data assets do not belong to certain teams or individuals. Data engineers, operations teams, IT, and teams from other business units should be able to work on data assets and insights within the confines of security and governance standards. DataOps considers data to be a shared asset that crosses organizational boundaries.
While most people leverage tools to monitor and provide observability to their entire data pipeline, performance is the true scorecard. It is critical to design and constructs for performance by default.
Observability should be present throughout the data pipeline. Before anything goes into production, data teams must monitor and tune it all the way back to the design stage. Before going into production, it’s critical to look at all of the nonproduction systems and the different consumers of the data, as well as how well the dependant apps interact with it.
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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain
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