Navigating DataOps Roadblocks

DataOps was born out of the DevOps philosophy of improving overall delivery by integrating development, testing, and operations. For DataOps, this means bringing data engineers, scientists, analysts, and other IT professionals together to create more efficient data pipelines. Leaning into the idea that all data is dynamic, DataOps creates a continuous, iterative flow of data by improving the quality and speed of execution and overall collaboration.

Data enablement and activation are at the heart of the DataOps mandate. DataOps can foster greater data enablement by unifying data throughout operations, so data teams can finally deliver on the promise of enabling data-driven decisions and business insights.

Collin Graves, founder of international data consultancy North Labs, compares DataOps to the foundation of a house: “Everyone gets excited about putting up the walls and moving in, but what’s most important is pouring the concrete slab and laying the joists so that the house is safe and functional.”

As businesses increasingly seek to build a data-driven culture and the popularity of DataOps increases, common pitfalls emerge that can easily turn potential growth into wasted time, effort, and cost. With the help of Graves and Chris Tabb, Co-Founder and Chief Commercial Officer at LEIT DATA, a data strategy, management, and analytics consultancy, we’ll unpack four of the most common stumbling blocks of DataOps, so you can skip the struggle and get straight to the insights.

Pitfall #1: Chasing the next big thing (or trend)

The data ecosystem is expanding at a breakneck pace, with new tools released every few months. It’s easy to get caught up in the excitement of shiny new objects and lose sight of what the business needs. “People try to short-circuit the conversation around the foundational parts,” Graves said. “They get ahead of themselves building more walls, adding on, so by the time they get to the roof, things fall down pretty quickly.” Everything should come back to utility—how software, tools, and data solve business problems.

How to avoid it: Focus on maximizing the capabilities you have and keepthings simple in the near-term.

“Add functionality only when you’ve hit your current ceiling and need new capabilities,” Graves said. “That’s how you stay agile and iterative. That’s howyou find success.”

Tabb agreed: “When we’re talking about optimization, we mean speed, getting to business value quicker. When you optimize the tools that you have, you accelerate business value and reduce cost — and the better off you are for it.”

Pitfall #2: Lack of focus on business value and KBQs

“Everything a DataOps team does should add business value,” Tabb said. “Any metric that is created should underpin the company’s business strategy and its mission.” Yet, many businesses struggle to deliver on data’s promise due to laborious systems that hinder higher-level work. At that point, DataOps risks becoming “just a research and development experiment,” Tabb warned — and often a costly one.

How to avoid it: Define your key business questions (KBQs) and set up your DataOps to support them.

KBQs are fundamental inquiries that businesses must answer to make informed decisions that support their goals. Without KBQs directing the data, it becomes more difficult to generate measurable business impact. With KBQs driving data frameworks, it’s easier to build toward measurable impact in the data enablement phase, and teams can sidestep the “undifferentiated heavy lifting” of working in tedious, manual systems, Graves explained. Instead, DataOps can focus on optimizing systems so that energy can be devoted to higher-level work. “That confusion and time-consuming overhead is why a lot of data projects fail,” Graves said.

Pitfall #3: Jumping into sexy data projects with bad data

Everyone wants to build the next best large language model (LLM). But when you train machine-learning programs on biased data, you get biased results. Foundational integrity of your data is everything and without it, you run into the age-old problem of “garbage in, garbage out,” Graves said.

How to avoid it: Establish data testing standards.

Established testing standards are central to data quality and the scalability of a DataOps program. Without proper testing, data models suffer from damaging, far-ranging issues such as the inability to roll back; messy, long-living branches; and changes pushed directly to production schemas creating incidents of highly visible flaws. Yet, with appropriate checks in place, “you can build trust and repeatability,” Tabb said. “And with that you get to value quicker—which is what it’s all about.”

“It’s the most boring piece of data analytics, but it’s the most important aspect of any data program,” Graves said.

Governance, transparent data processing, secure handling, education, and data quality assurance build trust. Testing is a crucial piece of quality assurance that ensures data’s utility throughout its lifecycle. With high-performing data, management and alignment become easier. The only way to ensure data will perform is through rigorous testing.

Pitfall #4: Lack of internal alignment

Another element that establishes trust in data is alignment between executive teams and DataOps. Executive buy-in brings clarity to data objectives and makes for more efficient use of resources.

“Without buy-in from a business, we’re just technologists,” Tabb said. “We can build something shiny, but what for? Does it add value? The people running the business are the ones that are really positioned to see the value.”

How to avoid it: Create open channels of communication between DataOps and the business, especially executives.

Creating open channels of communication with DataOps teams is a huge part of building a data-driven culture and increases participation in the DataOps team’s work. “The more everyone is working together — from Finance and Supply Chain to executives and DataOps—the more everyone can benefit,” Tabb said.

Derive value from your data with DataOps

In a world where global data storage is already at hundreds of zettabytes and climbing, data-driven businesses are poised to leverage insights from that raw information and create new value for a global customer base. Yet in most businesses, data hasn’t delivered on its promises. Insights aren’t channeled appropriately or delivered fast enough, analysis is off base, or processing is inconsistent.

DataOps aims to change that.

Accelerating the pipeline to quickly deliver insights with demonstrable value can change the future of a business. DataOps offers a way to manage the complexity of a fast-moving, ever-innovating field with a dedicated team agile enough to adapt to any changes. While every organization will develop a different DataOps team, the end goal is always to streamline design, development, and maintenance of data applications to deliver value faster and more predictably.

“The point is to help the business make better decisions, and to do it quickly,” Tabb said. By circumventing common issues before they become costly, businesses can realize the potential of DataOps and, in turn, change the way they do business for the better.