Getting Started with DataOps

Your organization is committed to becoming data-driven. But you know that it’s not easy to deliver the clean, curated, comprehensive data your organization needs to make data-driven decisions. You’ve heard about DataOps, and you believe it’s the key to managing the growing volume, variety, and velocity of data. But you’re not sure where to start. Sound familiar?

If so, then you’ve come to the right place. Here, we’ll share how to start with DataOps, including how to build a DataOps framework. And we’ll cover DataOps benefits, in the event you still need to convince the naysayers. Let’s dig in.

The origins of DataOps

Over the past 10+ years, we’ve seen the emergence of DevOps, an approach to software development that accelerates the build lifecycle (formerly known as release engineering) using automation. This merging of software development and IT operations reduces time to deployment, accelerates time to market, minimizes defects, and shortens the time required to resolve issues. Now, data engineers and data scientists are embracing a sister discipline: data operations or DataOps.

DataOps applies the rigor of DevOps to speed analytic outcomes for the enterprise. It is a set of practices, processes, and technologies for building, operationalizing, automating, and managing data pipelines from source to consumption.

How to build a DataOps framework

Getting started with DataOps may seem like a tall task, but it’s actually quite simple, once you know where to begin. And the best place to start is with these three basic components of a DataOps framework:

1. Technology

Starting with the right technology, including both the architecture/tools and infrastructure, or a platform to support the architecture, is a critical first step. And while the exact architecture for your organization will likely be unique, there are several important caveats to consider.

When designing an architecture, organizations should think “cloud first” and assume that data will always change. Choose open/best-of-breed technologies and be sure that humans are always at the core. Every infrastructure, too, needs to have several important components: management, search, compute, storage, and a cloud-based foundational infrastructure.

2. Organization

Next, consider your organization, including the division of labor across multi-disciplinary teams (data suppliers, preparers, and consumers) and a working structure for projects across technical and business teams. Be sure to define the roles of every participant, from data source owners to end-users making business decisions.

3. Process

Finally, you’ll need to determine your process model. The appropriate model will fluctuate with the scale of your DataOps project work, but it’s best to embrace an agile, incremental delivery model. There are several options you can consider, including an advisory model that bootstraps projects with best-of-breed tools and approaches. Another option is the shared services model with full-service data applications, developed in collaboration with the business. Any process model you choose will have its unique set of pros and cons depending on your organization.

The important thing to remember about process is that rules-based approaches that rely on modeling and testing are too labor-intensive, monolithic, and IT-driven. In today’s fast-paced business world, DataOps initiatives that are automated, incremental, and collaborative are a must.

How do you know if your organization needs DataOps?

If your goal is to become data-driven, then it’s almost certain that you’ll need DataOps. But here are a few of the most telling indicators.

Your data team is in full burnout mode. And it’s probably because they’re being inundated with too many minor request tickets. Business users don’t understand why it takes so long to get data, and even when they do get it, they often don’t trust it because the data contains too many errors.

Data analysts write the same jobs and reports with minor variations. Data scientists may wait for months for data and computing resources. Your organization may have started self-service initiatives, but this strategy has spawned hundreds of data silos. It may take months to deploy a single predictive model. Your organization also might not know about the trade-offs of adopting on-premise or cloud-based solutions for their projects.

If this sounds familiar, don’t worry. You’re not alone. According to an Eckerson Group survey, 43% of organizations do not have DataOps initiatives and only 30% “somewhat” do. Only 27% say they have established active DataOps programs.

So why is adoption so slow?

There are many benefits to adopting a comprehensive DataOps strategy, including faster cycle times, fewer data defects and errors, faster change requests, increased scalability and reliability, lower costs, more innovation, improved data governance, and happier business users, among others. Despite these advantages, the percentage of organizations with initiatives remains low.

However, in this same Eckerson Group survey, organizations cited many common reasons why they have struggled with getting started with DataOps, including difficulties with:

  • Establishing formal processes (55%)
  • Orchestrating code and data across tools (53%)
  • Staff capacity (50%)
  • Monitoring the end-to-end environment (50%)
  • Building rigorous tests upfront (47%)
  • Lack of adequate automation tools (42%)
  • Getting business users to buy into the process (35%)
  • Adopting agile methods and teams (34%)
  • Data too hard to find (26%)
  • Getting technical users to buy in to the process (23%)

By embracing DataOps, you’ll introduce your data organization to the practices, processes, and technologies needed to accelerate the delivery of analytics and overcome the challenges listed above. You’ll also bring rigor to the development and management of data pipelines, enabling CI/CD across your data ecosystem.

Get Started Today

People have been managing data for a long time, but we’re at a point now where modern enterprises can no longer manage the quantity, velocity, and variety of data available without a significant change in the fundamental infrastructure and supporting DataOps processes.

To learn more about how to start with DataOps, download our e-book Becoming a DataOps Expert: Putting DataOps Strategies into Practice.