Key steps (and technologies) for achieving successful DataOps methodology

Nov 4th, 2021

Gary Allemann – MD at Master Data Management

Agility is key when it comes to data analytics. A report that takes six months to deliver is of little value to the business, because any insights will be stale and irrelevant. The historical disconnect between IT and business remains a problem, and this is where DataOps comes in.

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 organisation.”

Essentially, this means that DataOps is about adopting an agile approach that brings together the data scientists, data analysts and data engineers so that together, they are able to activate data for business value.

There are four crucial steps, and four associated data management technologies, that are critical in successfully enabling DataOps methodology and unlocking the real business value of data.

Step 1: Improve communication between stakeholders and developers

Collaboration is key when it comes to DataOps. It has become extremely important to bridge that gap between business and IT, because without collaboration among business stakeholders and developers, data will never be able to drive business insight.

Technology such as a value-driven data catalogue helps organisations ensure that decision makers’ requirements are clearly understood and linked to business goals and objectives. IT is able to find the data sets required, understand the scope and impact of required changes, communicate decisions, and reduce the cycle time for delivery.

Step 2: Agile pipeline delivery

As an agile methodology, agility is obviously another important component of DataOps. However, modern data architectures are inherently complex hybrids of next-generation and legacy platforms. This makes achieving agility in data pipelines a challenge, and often, this is the bottleneck in delivering analytics.

Data sources today include mainframe solutions, enterprise applications and external sources such as the cloud and even social media, all of which need to be incorporated for effective analytics and insight. In addition, technology continues to rapidly evolve, so it is imperative to futureproof technology investment.

Data integration is key in bringing together various disparate data sources, today and in the future, facilitating the agile pipeline delivery that organisations need to achieve DataOps.

Step 3: Enhanced data pipeline observability to avoid production defects

Data systems continue to grow, evolve and become increasingly complex. This has made  data pipeline observability is very time consuming and, in fact, nearly impossible to achieve.

The challenge is that without a deep understanding of the data pipeline, and of agile development requirements, every change to the environment carries a high risk of broken releases – in other words, of a system or process failing to work, because the impact of the change has not been fully understood.

Often, data teams allocate up to 40% of their resources to carry out manual impact analysis, but this is neither agile nor cost effective. Automated data lineage tools can assist in reducing manual effort, enabling agile change management with fully automated impact analysis, incident resolution, and debugging.

Step 4: Secure your analytics environment

The final step is to ensure that sensitive data, related to both customers and other key stakeholders, is protected, both on-premise and in the cloud. This has become more challenging with the enactment of the Protection of Personal Information Act (PoPIA) and other data privacy legislation, because there is now a tug-of-war of sorts between access to data and data privacy. The DataOps team requires access to the data that they need for insights, without compromising privileged information. Role- and location-based security policies must be defined and enforced throughout the data chain, from ingestion to consumption. In addition, audit trails must be maintained to satisfy regulators, particularly when information is moved to the cloud.

Impactful business analytics

The goal of DataOps is to support cross-functional data analytics teams with an agile methodology to streamline the delivery of trusted, reliable analytics. Using the right technology solutions helps businesses automate tedious, repetitive tasks to keep the data pipeline healthy. Not only does this improve collaboration, it also prevents defects, speeds up incident resolution, reduces the cycle time of data analytics, and increases the value of analytics. DataOps is an invaluable tool to unlocking the value of data and information and of measuring the business impact of analytics.

By Gary Alleman, Managing Director at Master Data Management