The Business Case for DataOps

Savvy executives maximize the value of every budgeted dollar. Decisions to invest in new tools and methods must be backed up with a strong business case. As data professionals, we know the value and impact of DataOps: streamlining analytics workflows, reducing errors, and improving data operations transparency. Being able to quantify the value and impact helps leadership understand the return on past investments and supports alignment with future enterprise DataOps transformation initiatives.  Below we discuss three approaches to articulating the return on investment of DataOps.

Resource Redeployment

In a recent Gartner survey (figure 1), data professionals spent 56% of their time on operational execution and only 22% of their time on innovation that delivers value. An effective DataOps strategy can help a team invert this ratio and provide more value to the company. 

Figure 1: Data professionals spend only 22% of their time on innovation.


Gartner describes the time spent on “operational execution” execution as using the data team to implement and maintain production initiatives. A big percentage of the time that data scientists spend on operational effort is consumed servicing data errors.

In teams with mature DataOps practices, including some long-time DataKitchen customers, data professionals have indeed flipped the ratio and spend much less time on nonvalue-added activities.  Instead, these organizations commit 20% of their time implementing automation and writing tests. As a result, they reduced the time spent on errors and manual processes to nearly zero. This allows the team to spend significantly more time focusing on high-value efforts and meaningful collaborations.  Good rules of thumb are:

  • If you’ll perform an operation twice in a year, then automate it.
  • If it can be wrong, test it.

Implementing DataOps automation requires about 20% of a data professional’s time, but it completely eliminates data team participation in operations, saving them 56% of their time; a net savings of 36%. For a team of ten data professionals, this savings is the equivalent of adding more than 3.5 full-time employees to value-added activities. These newly available resources can be redeployed to create more capacity for the company’s analytics-hungry product teams.

Another way to demonstrate the impact of DataOps on FTEs is by showing the math.

Thirty-six percent of the total time of a ten-person team, based on a full-time employee (FTE) cost of $156,000 amounts to $561,000. This significant sum can be redeployed to higher value-add activities.

Insourcing Through DataOps

Many companies overcome their staffing limitations by outsourcing critical work to third parties. When internal analytics workflows are automated, there is little advantage to outsourcing. With DataOps, the work can often be performed much less expensively through automated orchestrations that are developed and managed in house. Automation can free up both direct and indirect resources. It enables companies to redirect the utilization of their own staff and reduce the dependency on external resources. If your company spends millions on consulting fees and outside contractors, DataOps automation could make a significant contribution to the bottom line. In one real-world example, a DataKitchen customer realized a net savings of $70 million dollars as effort transitioned fully from outside agencies to internal resources.

Cost of Slow Decision Making

What can you do with the resources that are freed up from DataOps automation? One approach applies these resources to business analytics that expedite and improve decision-making.

Analytics agility leads to business agility. When the data team delivers analytics rapidly and accurately, analytics do a better job supporting decision-makers. When an organization can make decisions faster and better, it is able to capture opportunities that it would have otherwise missed or misjudged. With analytics playing a central role in corporate strategy, analytics agility can be a competitive advantage.

In one example, using analytics to understand customers and markets significantly improved product launch success at one DataOps enterprise. With rapidly produced analytics, they were able to improve market segmentation to maximize revenue in the early product lifecycle, boosting lifetime product revenue.


When executives evaluate whether to invest in a DataOps initiative, they need to understand the business benefits. Improved productivity, reduced outsourcing costs, and greater business agility together build a strong business case for DataOps. It may help to start with a mini or pilot project that demonstrates DataOps benefits. Improvement of a key metric may provide the justification that you need to secure investment in a larger DataOps program.

About the Author

James Royster
James Royster led Data Strategy and Operations for the Otezla brand at Celgene, a pharmaceutical company recently acquired by Amgen. James is a regular user of the DataKitchen DataOps Platform.

Sign-Up for our Newsletter

Get the latest straight into your inbox

Monitor every Data Journey in an enterprise, from source to customer value, in development and production.

Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.

Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.

recipes for dataops success

DataOps Learning and Background Resources

DataOps Journey FAQ

DataOps Observability basics

Data Journey Manifesto

Why it matters!

DataOps FAQ

All the basics of DataOps

DataOps 101 Training

Get certified in DataOps

Maturity Model Assessment

Assess your DataOps Readiness

DataOps Manifesto

Thirty thousand signatures can't be wrong!


DataKitchen Basics

About DataKitchen

All the basics on DataKitchen

DataKitchen Team

Who we are; Why we are the DataOps experts


Come join us!


How to connect with DataKitchen


DataKitchen News


Hear the latest from DataKitchen


See DataKitchen live!


See how partners are using our Products


DataKitchen DataOps Consulting Services

Product Implementation

We help you succeed with DataKitchen's products.

Commercial Pharma Data Engineering

We build, operate, train, and transfer data products.

DataOps Transformation Advisory

We help you bring DataOps to your organization.

Customer Data Platform Engineering

We build an open CDP, then transfer it to you.


Data Leaders Brief
Testing Data Analytics Data Architecture Business Analytics Customer Analytics More >>