How DataOps Strengthens Business Resilience and Agility

SubhamSeptember 7, 2022
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Agility is critical for any business looking to grow and remain relevant in today’s complicated industry landscape. Being able to quickly respond and reliably deliver actionable insights is vital for businesses — especially with changing market conditions and increasing customer demands.

One of the biggest market shifts in the last few years has been the rise in complex data sets and the speed at which data teams must respond. Data teams have been getting bogged down and experiencing burnout at an alarming rate. A recent study showed that 50% of data professionals feel constant pressure and stress from dealing with inefficient data integration – and it’s causing burnout.

This burnout has exposed one of the main barriers to the scalable delivery of value from data: not being able to respond to change quickly and reliably. Thankfully, because of the rise in cloud technology, methodologies have become available across the board in response, such as DataOps, DevOps, DevSecOps and MLOps, to name a few. Due to these innovations, teams can operate more efficiently and collaboratively to address this constant market change.

Scalable Operations

Cross-functional methodologies including DataOps, DevOps and MLOps have exploded over the last few years as a way to extend beyond traditional IT, tap into unique business functions, and drive faster time to insights. These all refer to architecture and operating principles that enable quick and reliable iteration in their particular discipline. Without them, trying to unnaturally force speed can result in design compromises and technical debt.


It’s said that 93% of organizations agree more automation opportunities exist – businesses are just not innovating fast enough to jump on the opportunity. Businesses should be taking more advantage of tools to fuel this need for automation since doing so will allow them to gain a competitive advantage. However, at the enterprise level, businesses are tackling adoption roadblocks due to skepticism of results, budget restrictions and generating buy-in from senior leadership.

By working to combat these challenges, and adopting DataOps and other methodologies as working practices in daily business operations and functions, organizations can alleviate stress and decrease their turnover rate. In tandem, changing the status quo of how things are traditionally done, will allow organizations to get the insights faster that customers and the business desire.

Where Data Integration Fits Into the Puzzle

Data integration is a key component of providing organizations with a 360 degree view of their data. But, as noted before, inefficient data integration is a contributing factor to burnout. Organizations can fix this by investing in solutions that streamline tedious tasks – such as data migration and maintenance. The goal is to free up data users’ time so they can focus on the most impactful projects and get a better handle on the influx of data across their organization. Enter DataOps.


Recently, DataOps has emerged as a new golden ticket of data management practices. More and more enterprise-level organizations are adopting the functionality born out of DevOps. Gartner notes that “DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models and related artifacts.”

As one of the objectives of DataOps, change management helps to solve common business issues by aligning the people and processes to ongoing initiatives that help push an organization towards its business goals. This all sounds great, but organizations typically don’t have an easy fix to track towards this goal. Data integration platforms are purpose-built to help solve this. Ultimately, DataOps helps organizations tease order and discipline out of the chaos and turn data into business value.

When maintaining and adapting workflows, DataOps is able to aid that demand for speed. DataOps has the capability to grant data teams the ability to respond to change – through the data extraction, transformation and integration process.

Low-Code/No-Code: Tools For Success

Rather than a never-ending barrier to progress, changes to information processing should be seen as an opportunity to maximize productivity and create an ongoing loop of working – through a system that delivers value and creates change. These optimized cycles can help the business stay agile and iterate quickly and effectively.

Organizations should also consider adopting a low-code/no-code (LCNC) platform to allow traditional “non-coders” to perform data processing. The ease of use of a LCNC platform makes it possible for anyone across the business – regardless of department and specialty – to create and deploy data analytics. A recent study found that almost half of small and midsize businesses (SMBs) are embracing LCNC tools to help with business innovation as well – with 79% of SMBs citing having greater business agility and improved speed-to-market (56%). These improvements, made possible through LCNC, ultimately save businesses time and allow data decision-makers to get valuable insights faster.

The demand for constant change cannot be ignored. If businesses choose to push back against the changing environment, they risk becoming irrelevant. The best way for businesses to grow and move forward is to accept that change happens, embrace it, and adopt the necessary tools that can help the business succeed. A data integration platform, purpose-built to enable DataOps, can be that tool that drives organizations into the future.

About the author: Ian Funnell is the Manager of Developer Relations at Matillion, where he works to create thought leadership and enablement material that brings the Matillion platform to life. Starting out in IT, Ian’s first role was in fintech developing real-time middleware, before shifting to a data-oriented outlook in data warehousing and data integration and has remained working in that area ever since.

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