Streamline the Delivery of Trusted Data

Agile Delivery of Reliable Data Pipelines, Reports and AI




DataOps (data operations) supports cross-functional data analytics teams with an agile methodology to streamline the delivery of trusted, reliable analytics through clear communication, automation and continuous improvements

DataOps blends DevOps, Agile and Lean Principles
DataOps blends DevOps, Agile and Lean Principles

DataOps: An Agile methodology for Data Products

We help you to automate tedious, repetitive tasks to keep your data pipeline healthy — improve collaboration, prevent defects, speed up incident resolution, reduce the cycle time of data analytics, and increase the value of analytics.

"Organisations that have implemented DataOps have seen a 40% reduction in the number of data and application exceptions and errors, and a 49% improvement in the ability to deliver data projects on time"

Stewart Bond, Research Director, IDC

Benefits of implementing a DataOps approach include:

  1. Improved Data Quality: By automating data processing and testing, DataOps helps to ensure data accuracy and completeness, reducing the risk of errors and inconsistencies in the data.

  2. Faster Time-to-Insight: DataOps enables organizations to quickly process and analyze large volumes of data, providing faster insights and enabling faster decision-making. 

  3. Increased Agility: By streamlining data processes and improving collaboration, the approach helps organizations to be more agile and responsive to changing business requirements. DataOps helps data teams to troubleshoot data issues more quickly, before they snowball into larger issues, and fix them more easily.

  4. Better Collaboration: DataOps emphasizes collaboration and communication between different teams, enabling data professionals to work together more effectively and reducing the risk of miscommunication or misunderstandings.

  5. Improved Scalability: DataOps enables organizations to scale their data processing and analysis capabilities, supporting the growth of the business. 

  6. Increased Uptime: DataOps practices help data teams assess and understand the potential impact of changes to data pipelines and other data products before they are made, radically reducing the risk of errors being introduced, resulting in more reliable data pipelines
  7. Enhanced Security: DataOps practices can help to improve data security, reducing the risk of data breaches and ensuring that sensitive information is protected.

Overall, DataOps can help organizations extract greater value from their data, improve the efficiency of their data processes, and drive better business outcomes.

Can DevOps be used in data management?

DevOps can be used in data management, but it’s not as effective as DataOps. 

DevOps is primarily focused on software development, while DataOps is focused on data management. DataOps has additional components such as data integration, data quality, data security, and data governance, which are not part of DevOps. DataOps extends DevOps to enable the agile delivery of reliable, accurate data pipelines and analytics products.

IDC Research Director, Stewart Bond, highlights the advent of DataOps as a distinct discipline in the IDC spotlight report, Improving Data Integrity and Trust through Transparency and Enrichment. According to the report, “Organizations that have implemented DataOps have seen a 40% reduction in the number of data and application exceptions or errors and a 49% improvement in the ability to deliver data projects on time.”

Learn more about the Difference Between DevOps, DataOps, and MLOps in our blog post.

How do we help you to implement DataOps?

Gartner defines DataOps as "a collaborative data manager practice, really focused on improving communication, integration, and automation of data flow between managers and consumers of data within an organization."

DataOps - an iterative approach based on automation

DataOps embraces the core ideas of DevOps - an agile approach to software development that emphasizes communication and collaboration to build cross-functional teams that can deliver software quickly and reliably. For data analytics, the intention is to bring together the data scientists, data analysts and data engineers that, together, are able to activate data for business value.

According to data integrity leaders, Precisely, these different teams have not always collaborated closely

"The folks who set up MySQL databases usually don’t know much about using Hadoop, for example. By embracing the core ideas of DevOps, however, organizations can achieve DataOps to make these different teams collaborate more effectively."

Collaboration is a key principle of DataOps

The use of a value-driven data catalogue, like Data360, improves communication between stakeholders and developers, ensuring that data requirements are clearly understood and linked to business goals and objectives.

The development team, whether from the engineering or analytics perspective, can quickly and easily find the data sets that they need to do their jobs, understand the scope and impact of required changes, communicate decisions, and reduce the cycle time for delivery. Testers and operations staff are kept in the development loop, allowing them to engage proactively and plan for deployment. And so on...

Agility is key to DataOps.

In a rapidly evolving technology space, we need to future-proof our technology investment as today's preferred analytics platform may become tomorrow's old news. The complexity of modern data architectures means that data pipelines can become the bottleneck for analytics delivery. 

Data sets may come from legacy platforms (like the mainframe or RDMS solutions), enterprise applications (via API) or external sources including social media and cloud-based analytics platforms. Through our technology partners, we offer a variety of data integration options for agile pipeline delivery to reliably connect your data.

future proof your data

Impact Assessment is key to DataOps

With the growing complexity of data systems, manual methods to track data movement and transformation are very time-consuming and nearly impossible to achieve.

Worse still, without this deep understanding of data pipeline and agile development requirements, every change to the environment carries a high risk of broken releases. To deal with this complexity, data teams allocate up to 40% of their resources to carry out manual impact analyses. 

Automated Data Lineage for DataOps

Avoid Production Defects with Data Lineage

MANTA cuts down on manual effort by enabling agile change management with fully automated impact analysis, incident resolution, and debugging. Read our blog post to understand Why Your DataOps Team Needs Automated Data Lineage!

MANTA's automated lineage solution is shown to:

  • increase the productivity of your data analytics teams by 30-40% by providing better visibility of your data pipeline
  • reduce time to resolve data-related incidents by up to 90% by making it easy to trace any data-related issue back to the source quickly
  • reduce the number of broken releases to fewer than 1% by proving accurate, proactive impact assessments to understand the potential impact of changes before they are made.

Watch MANTA's Joe Chmielewski explains the fundamentals of data lineage and why it is crucial for organizations with large, complex data environments who are looking to achieve DataOps.

Data Observability is central to DataOps

Data observability is an important aspect of data operations (DataOps) as it enables teams to proactively monitor, troubleshoot, and optimize their data pipelines and infrastructure. Data observability involves the collection, analysis, and visualization of key performance indicators (KPIs) that can help teams identify issues and potential points of failure in their data systems.

"In order to keep pace with data volumes and workflows, enterprises must automate the data observability process to the maximum extent possible. Automated data observability tools enable organisations to scale up their data pipelines while scaling down the human effort to monitor and manage them around the clock."

- James Kobelius, Senior Director, Research at TDWI

Succeeding With Data Observability
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By implementing data observability practices, DataOps teams can gain greater visibility into their data workflows, including how data is moving through the system, how long it takes to move from one stage to another, and where bottlenecks and errors may be occurring. This can help teams identify and address issues more quickly, reducing the time it takes to resolve problems and minimizing the impact on downstream systems and end-users.

Some key ways that data observability supports DataOps include:

  1. Improved system visibility: Data observability provides teams with a more comprehensive view of their data systems, including real-time insights into how data is flowing through the system, how different components are interacting with each other, and where errors or bottlenecks may be occurring. This increased visibility can help teams quickly identify and resolve issues before they become major problems.

  2. Proactive issue detection: By monitoring KPIs in real time, DataOps teams can identify issues before they impact downstream systems or end-users. This proactive approach to issue detection can help reduce the time it takes to resolve issues and minimize the impact on users.

  3. Faster root cause analysis: When issues do occur, data observability can help teams quickly identify the root cause. By analyzing data in real-time and correlating it with other KPIs, teams can gain a deeper understanding of what caused the issue and how to resolve it.

  4. Better collaboration: Data observability can also help facilitate better collaboration between different teams and stakeholders involved in the data workflow. By providing a shared view of the data system, teams can work together more effectively to resolve issues and optimize performance.

Overall, data observability is a critical component of DataOps that enables teams to more effectively monitor, troubleshoot, and optimize their data systems. By leveraging data observability practices and tools, DataOps teams can improve system performance, reduce downtime, and provide a better experience for end-users.

Secure your analytics environment

Last, but certainly not least, we accelerate access to data while ensuring that confidential data is protected, both on-premise and in the cloud. 

The DataOps team need access to the data that they need for insights, without compromising privileged information. Attribute- and location-based security policies must be defined and enforced from ingestion to consumption. Audit trails must be maintained to satisfy regulators, particularly when information is moved to the cloud.


Why is DataOps important?

Data is becoming increasingly important for organizations of all sizes and across all industries. As data volumes grow, it becomes more difficult to manage and deliver data in a timely and accurate manner. DataOps helps organizations overcome these challenges by creating a framework for managing and delivering data in a more agile and efficient manner.

DataOps can provide a range of benefits, including:

  • improved data quality,

  • faster time to value,

  • better collaboration between teams,

  • increased automation,

  • and reduced risk.

By implementing DataOps, organizations can create a more streamlined and efficient data management process that enables them to make more informed decisions based on high-quality data.

How does DataOps differ from traditional data management approaches?

Traditional data management approaches tend to be more siloed and rely heavily on manual processes. DataOps, on the other hand, emphasizes collaboration, automation, and continuous improvement. DataOps brings together stakeholders from across the organization to create a more agile and streamlined approach to managing and delivering data.

Can DevOps be used in data management?

DevOps can be used in data management, but it's not as effective as DataOps. DevOps is primarily focused on software development, while DataOps is focused on data management. DataOps has additional components such as data integration, data quality, data observability, data lineage, and data governance, which are not part of DevOps.

Some key principles of DataOps include:

  • automation,

  • collaboration,

  • continuous testing and monitoring,

  • and continuous improvement.

DataOps emphasizes the need for cross-functional teams to work together to create a more streamlined and efficient data management process.

There are a variety of tools used in DataOps, including:

  • data integration tools,
  • data catalogues,
  • data quality tools,
  • data lineage tools,
  • and data observability tools.

Who can benefit from implementing DataOps?

DataOps can benefit organizations of all sizes and across all industries. Any organization that relies on data to make informed decisions can benefit from implementing a DataOps approach to data management.

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