Data has become a critical asset for organisations of all sizes and industries. Effective data management has become a top priority for many organisations with the growing volume of data being generated and the increasing importance of data-driven decision-making.
This is where DataOpscomes in – a discipline that focuses on optimising the delivery, quality, and management of data.
As organisations look to achieve their data-driven goals, it’s important to consider the future trends that will shape DataOps.
Before we start discussing the future of DataOps in 2023 and beyond, let’s brush through the basics.
What is DataOps?
DataOps is a set of practices and processes aiming to improve the collaboration and communication between data engineers, data scientists, and other stakeholders during software development or a data analytics project.
It emphasises automation and monitoring of data workflows, continuous testing and integration, and promoting a culture of data quality and collaboration.
The goal of DataOps is to increase the speed and reliability of data-driven decision-making by reducing the time and effort required to deliver relevant data insights.
Why Are Organisations Adopting DataOps?
DataOps is becoming increasingly popular since it helps organisations overcome common data analytics challenges.
These include slow and error-prone data pipelines, lack of transparency and collaboration between data teams, and difficulty in ensuring data quality and governance.
By implementing DataOps practices and processes, organisations can enjoy the following benefits:
- Improve data pipeline efficiency and reliability: Automation and monitoring of data workflows using IT management tool can help prevent errors and ensure that data is processed in a timely and accurate way.
- Foster collaboration and communication between data teams: DataOps promotes a culture of collaboration and communication between data engineers, data scientists, and other stakeholders, which helps ensure that everyone is working towards a common goal and can make more informed decisions.
- Ensure data quality and governance: DataOps emphasises continuous testing and integration, which helps ensure that the data being used is accurate and complies with relevant regulations and standards.
- Speed up the data analytics process: By reducing the time and effort required to deliver useful data insights, DataOps helps organisations make faster and more informed decisions based on data.
What are the DataOps trends that IT organisations should Definitely Know?
Sophistication of AI Systems
Organisations are increasingly using AI systems to make critical decisions that impact individuals, businesses, and society.
- To ensure that AI systems are trustworthy and reliable, there must be a clear understanding of how decisions are made, why certain decisions were made, and how to monitor the performance of these systems over time.
- This requires the creation of data pipelines and using IT management tool that allows for the tracking, monitoring, and auditing of AI models and the data used to train them.
This trend is driving the development of new tools and practices in the field of DataOps that aim to increase transparency and accountability in AI systems.
Shift Towards Cloud Computing
The cloud provides on-demand scalability, flexible resources, and a highly-available infrastructure that can support the demands of modern data-driven organisations.
- Cloud-native technologies also support the development of microservices and container-based architectures, which can increase the agility and flexibility of DataOps processes.
- This allows organisations to quickly and easily deploy new data applications and services and to manage their data infrastructure in a more efficient and cost-effective manner.
- Furthermore, cloud-native technologies offer a high level of security and data privacy, making them well-suited for use in regulated industries and managing sensitive data.
Consequently, cloud-native DataOps is becoming an increasingly popular choice for IT organisations looking to streamline their data management processes and achieve their data-driven goals.
Increased Collaboration
Collaboration is becoming increasingly important in DataOps as organisations look to break down the silos that often exist between different data-related teams.
- By increasing collaboration between data engineers, data scientists, and business stakeholders, organisations can ensure that data is being used in the most effective and efficient manner possible.
- For example, the collaboration between data engineers and data scientists can help to ensure that data is being used to drive insights and inform decision-making.
- Collaboration between data scientists and business stakeholders can help to ensure that data-driven insights are being used to drive positive business outcomes
DataOps Engineer Role & Designation To Gain Importance
As organisations look to achieve their data-driven goals, they recognise the need for specialised expertise in DataOps.
This is leading to the creation of new roles. And DataOps Engineer is one of them. DataOps Engineers help organisations to effectively manage the delivery, quality, and management of their data.
- The role of the DataOps Engineer is to bridge the gap between data engineers, data scientists, and business stakeholders.
- They are responsible for ensuring that data is being used in the most optimal and efficient manner possible and that data-driven insights are being used to drive business outcomes.
- They also play a key role in implementing and maintaining DataOps processes and tools, such as data pipelines, testing frameworks, and monitoring systems.
- These engineers bring a unique skill set to the table, including expertise in data management, automation, and collaboration.
- This combination of skills is becoming increasingly important as organisations look to effectively manage the growing volume of data generated and the increasing importance of data-driven decision-making.
As organisations continue to embrace data as a critical asset, the demand for DataOps Engineers will only increase in the coming years.
Wrapping Up
The future of DataOps is bright, and it’s being shaped by trends such as AI, cloud-native technologies, increased collaboration and demand for data engineers. Whether it’s improving data quality, streamlining data delivery, or ensuring that data is being used to drive business outcomes, DataOps will continue to be a critical discipline for years to come.
