DataOps - Money Multiplier for Industries
Today, "operationalization" is one of the most popular buzzwords in the technology industry, and it can be seen in almost every area, such as ‘analytics operations,’ ‘app operations,’ ‘cloud operations,’ ‘dev operations,’ ‘dev security operations,’ ‘data operations,’ and so on. In fact, according to the DataOps Platform Market Report 2022, by 2032, the DataOps Platform Market is expected to grow from USD 1,198 million in 2022 to USD 3,856 million at a CAGR of approximately 25.7% during the forecast period. Every company runs on people and data; obtaining the right data at the right time can bring tremendous value to any company.
Today, numerous organizations across the world are focusing on gaining insightful data and are working towards streamlining their data infrastructure and operations. Every piece of data has a life cycle that goes through before it is consumed. Majority of the companies experiment to design a highly scalable data platform with all the latest technologies in order to run their operations seamlessly. In today’s work scenario, using scalable technology and implementing an end-to-end data pipeline is a great solution, but there are certain challenges apart from the functional and data pipeline development that have led to customer dissatisfaction and revenue loss –
• Growing demand for data: According to the market Pulse Survey by Matillion and IDG report, global data creation is projected to attain 175 zettabytes by 2025. Organizations collect data from different sources which results in differences between types and structures. Out of this generated data, only 20% is structured, and the rest is unstructured. Transforming this data to make it analytics-ready is also one of the challenges faced by firms these days.
• Complexity of data pipelines and scarcity of skilled people: Since data is generated from multiple sources and has a variety of characteristics, the data pipeline becomes complex. It is also a major challenge for most companies to find skilled data engineers, data architects, and data scientists who can help build these scalable and efficient pipelines. Moreover, due to their complexity, many data pipelines are full of flaws even after rigorous quality checks.
• Speed and accuracy of data analytics: It’s very important to have efficient and accurate analytics, which helps in their fast decision-making. Hence, it’s essential to have good collaboration among business and data teams and identify requirements accurately before implementation. The goal of every corporate is to deliver faster, more reliable, and cheaper products to the customers and generate optimal revenue from them. Therefore, data operations play an important role in building such an ecosystem to help industries multiply their revenue.
Dimensions of DataOps is not a science, and it works across factors such as people, processes, and technology. It requires a team of data engineers, data scientists, and data analysts to promote collaboration with business teams and develop processes that transform existing data pipelines into DataOps pipelines, absorbs their value, and identify advantageous technical product features in a DataOps technology.
Addressing challenges and data monetization using DataOps
The data coming from different data sources could be in the form of structured or unstructured data, video, text, etc. The data is then ingested into the storage after processing it through the batch or the streaming engine and then is transformed to form meaningful information which is subsequently stored in the data lake. The stored data is then published for the downstream/consuming system through the consumer layer. In this overall data flow, the focus is usually on collecting the data, keeping the business objective in mind. It is also important to identify defects in the earlier phases of development that help companies monetize their data. A highly collaborative data and operations team that works together to set goals and optimize appropriate procedures, technologies, and techniques are equally crucial for the development of data pipelines. Furthermore, in order to automate problem reporting and create a self-healing system, businesses today primarily rely on artificial intelligence and machine learning technologies, which help these systems speed up the entire process, so that stated SLAs are met, and customers are satisfied.
All in all, DataOps is a set of processes, technologies, and practices to automate data delivery with quality to improve the value of data as per business requirements. It can reap the benefits of faster cycle time, fewer data defects, greater code reuse, more efficiency and agility, and finally accelerate the business values from timely data insights. Not only that, but DataOps values can also be created for SLA-based performance, high throughput, quality, and productivity. Proper use of processes, governance team, and technology can help industries monetize their businesses. In this digital era, data is the ‘new oil’ and it is imperative to harness its true power to scale businesses and drive innovations of tomorrow.
Today, numerous organizations across the world are focusing on gaining insightful data and are working towards streamlining their data infrastructure and operations. Every piece of data has a life cycle that goes through before it is consumed. Majority of the companies experiment to design a highly scalable data platform with all the latest technologies in order to run their operations seamlessly. In today’s work scenario, using scalable technology and implementing an end-to-end data pipeline is a great solution, but there are certain challenges apart from the functional and data pipeline development that have led to customer dissatisfaction and revenue loss –
• Growing demand for data: According to the market Pulse Survey by Matillion and IDG report, global data creation is projected to attain 175 zettabytes by 2025. Organizations collect data from different sources which results in differences between types and structures. Out of this generated data, only 20% is structured, and the rest is unstructured. Transforming this data to make it analytics-ready is also one of the challenges faced by firms these days.
• Complexity of data pipelines and scarcity of skilled people: Since data is generated from multiple sources and has a variety of characteristics, the data pipeline becomes complex. It is also a major challenge for most companies to find skilled data engineers, data architects, and data scientists who can help build these scalable and efficient pipelines. Moreover, due to their complexity, many data pipelines are full of flaws even after rigorous quality checks.
• Speed and accuracy of data analytics: It’s very important to have efficient and accurate analytics, which helps in their fast decision-making. Hence, it’s essential to have good collaboration among business and data teams and identify requirements accurately before implementation. The goal of every corporate is to deliver faster, more reliable, and cheaper products to the customers and generate optimal revenue from them. Therefore, data operations play an important role in building such an ecosystem to help industries multiply their revenue.
Dimensions of DataOps is not a science, and it works across factors such as people, processes, and technology. It requires a team of data engineers, data scientists, and data analysts to promote collaboration with business teams and develop processes that transform existing data pipelines into DataOps pipelines, absorbs their value, and identify advantageous technical product features in a DataOps technology.
Addressing challenges and data monetization using DataOps
The data coming from different data sources could be in the form of structured or unstructured data, video, text, etc. The data is then ingested into the storage after processing it through the batch or the streaming engine and then is transformed to form meaningful information which is subsequently stored in the data lake. The stored data is then published for the downstream/consuming system through the consumer layer. In this overall data flow, the focus is usually on collecting the data, keeping the business objective in mind. It is also important to identify defects in the earlier phases of development that help companies monetize their data. A highly collaborative data and operations team that works together to set goals and optimize appropriate procedures, technologies, and techniques are equally crucial for the development of data pipelines. Furthermore, in order to automate problem reporting and create a self-healing system, businesses today primarily rely on artificial intelligence and machine learning technologies, which help these systems speed up the entire process, so that stated SLAs are met, and customers are satisfied.
All in all, DataOps is a set of processes, technologies, and practices to automate data delivery with quality to improve the value of data as per business requirements. It can reap the benefits of faster cycle time, fewer data defects, greater code reuse, more efficiency and agility, and finally accelerate the business values from timely data insights. Not only that, but DataOps values can also be created for SLA-based performance, high throughput, quality, and productivity. Proper use of processes, governance team, and technology can help industries monetize their businesses. In this digital era, data is the ‘new oil’ and it is imperative to harness its true power to scale businesses and drive innovations of tomorrow.
