Something interesting is taking place in the world of DataOps right now. A big something. But what’s really interesting is what’s at play behind the scenes.
According to our friends at Global Market Insights (GMI), the calculated annual growth rate (CAGR) of the DataOps platform marketplace is projected to grow 22% from 2023 to 2032 (i.e., less than one decade). By comparison, this is potentially 1% more than the much-buzzed-about low-code application marketplace is projected to grow during the same time.
However, the factors driving DataOps aren’t quite as clear compared to low-code.
Data orchestration capabilities need to keep pace with the growing volume and complexity of Big Data, and this is part of the reason for this exceptional amount of growth. Improving customer service and experience, increasing operational efficiency, and maintaining competitive advantages are ongoing priorities for businesses.
However, this near-exponential investment in DataOps may be due to a reckoning between two other causal trends—the increasing business need for real-time data insights and the growing impact of data governance and compliance globally.
To better understand why, let’s start by breaking down DataOps, covering its core principles, best practices, and implementation challenges within modern organizations.
A basic definition of DataOps
For those who work in leadership or data-adjacent teams, DataOps is a lot like shifted paradigms—no doubt you’ve heard it mentioned while doing business. However, lacking any direct experience, what exactly DataOps is may be less than clear. Let’s correct this now.
DataOps is short for Data Operations. It is a process-oriented methodology that shares and builds on many characteristics of Agile methodologies, DevOps, and lean manufacturing. Simply, the goal of DataOps is to reduce the cycle time of data analytics. In practice, it does so by streamlining collection from data sources to consumption by data consumers.
Broadly speaking, the DataOps process is driven through an organized emphasis on automation, collaboration, and communication. For good reason, this means DataOps and DevOps cross streams quite often in practice. Both are instrumental in keeping Big Data and business value from being at odds.
We’ve done our best to compare and contrast both processes elsewhere. But know that exploring the core pillars of DataOps helps clarify the distinct role it plays.
Core principles of modern DataOps
Andy Palmer coined the term "DataOps" in 2015, describing it as the intersection of data engineering, data quality, and data integration with a focus on various data professionals in a given organization communicating and collaborating to increase the velocity, reliability, and quality of data analytics.
While the concept and practice of DataOps has evolved and expanded beyond Palmer’s definition the core principles that enable professionals to put DataOps into practice remain largely the same:
1. Automation and Continuous Integration and Deployment (CI/CD)
The beating heart of DataOps is automation, which aims to reduce manual efforts related to data validation, integration, and deployment processes.
This often includes automating data ingestion, testing, monitoring, and deployment within a build/deployment pipeline to ensure both consistency and reliability.
2. Agile and lean methodologies
Practitioners work to streamline data management processes by incorporating agile and lean methodologies.
Agile’s focus on flexibility, continuous improvement, and the delivery of high-quality products in short cycles enables data teams to accelerate experimentation and adaptation based on feedback. Lean methodologies, in turn, guide teams to streamline data pipelines, minimize inefficiencies, and ensure that data delivery remains firmly coupled to business needs.
3. Collaboration and alignment
Effective DataOps encourages a culture of collaboration and alignment among cross-functional teams, which typically includes data engineers, scientists, and business stakeholders.
Fostering collaboration in this fashion ensures data products align with business needs and that data analysts can deliver all insights efficiently.
4. Cloud-first and scalable infrastructure
Modern DataOps ecosystems should prioritize cloud-based solutions for optimal scalability and flexibility.
This principle specifically supports distributed data processing and storage, enabling organizations to scale data operations as needed.
5. Highly automated and continuous processes
DataOps ecosystems should feature high levels of automation to manage the scale and scope of enterprise data efficiently and effectively.
In practice, this often includes automating the cataloging, movement, and organization of data, in addition to testing and releasing processes.
6. Openness and interoperability
An ecosystem in which DevOps practitioners leverage best-in-class, open-source, and potentially free tools is essential for innovation. The inverse also holds true—DataOps practitioners should avoid reliance on single, proprietary platforms (as opposed to a variety of tools that are easy to integrate and replace).
This ease of integration should also apply to a broad spectrum of potential data sources (e.g., data lakes, data warehouses) to improve data quality and governance.
7. Data quality and governance
Implementing robust data governance guidelines and practices is essential to ensure high data quality.
At a minimum, governance should involve data validation, cleaning, and reconciliation processes which can ensure accuracy and reliability throughout the data lifecycle.
8. Ephemeral environments and code reuse
Ephemeral environments are temporary by nature, so using them for testing further enhances flexibility and accelerates development. As such, they reduce the risk of conflicts between environments while promoting resource efficiency.
Code reuse helps practitioners maintain consistency, reduce errors, and save time by avoiding duplications of effort. Additionally, containerization encapsulates applications and their dependencies into containers that can run reliably in different computing environments.
9. Monitoring, observability, and customer centricity
DataOps practitioners should prioritize continuous monitoring and observability of data pipelines, ensuring data availability, performance, and security. This is vital for identifying, addressing, and resolving issues as quickly as possible.
While DataOps practitioners focus on reducing cycle times for data delivery, enabling faster insights, and improving data-driven decision-making, ultimately everything done must remain rooted in delivering value to the organization’s end-users and customers.
Being such an elemental aspect of any data practice, it’s also beneficial to explore how other data leaders articulate their core principles of DataOps. For an excellent perspective to which we can compare and contrast the above, we recommend looking at DataKitchen’s seminal DataOps Cookbook (that is, after finishing our own article, here, of course).
DataOps best practices
If core principles are one side of the DataOps coin, best practices are the other. Without them, no amount of core principles would allow organizations to actively streamline their data management processes, accelerate their insight generation, and (perhaps most fundamentally) improve data quality.
Because of this, DataOps best practices naturally intertwine with the principles outlined above. But the key best practices are still worth detailing, as they are applicable across industries and organizations:
Foster a collaborative culture
The existence of departmental silos within organizations is understandable. But they aren’t acceptable. This is why DataOps best practices begin by breaking down obstacles and fostering communication and collaboration between teams—data engineers, data scientists, business users, stakeholders, and operations.
DataOps makes data quality and accessibility a shared responsibility across an organization, which, in turn, sets the stage for all best practices that follow.
Adopt a customer-centric approach
Too often, adopting a customer-centric approach emerges as an afterthought in the practice of DataOps. This borders on being a tragic irony, as the benefits of DataOps itself—enhanced value delivery, promoting agility and responsiveness, driving innovation, fueling trust and loyalty, etc.—can only be fully realized by engaging with end-users to understand their needs, challenges, and desires.
Implement agile methodologies
Look to agile practices like sprints and scrums to manage data projects, allowing for rapid iteration and responses to change. Practitioners can also use Agile approaches to prioritize work based on business needs and value.
However, it bears noting that when practitioners seek to leverage Agile as a DataOps best practice, they must ensure its impact is balanced and holistic within the organization. When employed dogmatically, these Agile methodologies can muddle the practice of proper documentation, over-complicate data management, erode data quality for the sake of responsiveness, and potentially introduce security and compliance risks.
For these reasons, make sure that Agile within your organization is used as part of the overall DataOps solution, not a solution unto itself.
Automate data pipelines
Actively implement CI/CD pipelines in order to automate data integration, testing, deployment, and monitoring processes. In addition to this best practice of reducing manual efforts and errors, CI/CD pipelines help ensure seamless, automated updates and deployments.
Embrace continuous testing
Leverage automation to test data quality, data integration, and performance throughout the organization’s data lifecycle.
Furthermore, test-driven development (TDD) approaches should be adopted for data-related code and pipelines. TDD encourages earlier issue identification and potential integration issues while contributing to overall data quality.
Make version control ubiquitous
Track changes and facilitate collaboration by utilizing version control for all data assets—code, configurations, and data modeling, to name a few.
Additionally, data versioning strategies should be considered to manage changes in datasets. Doing so enables reproducibility and replicability, supports the experimentation and iteration that DataOps champions, and allows organizations to demonstrate the lineage and provenance of their data.
Obsessively monitor data quality and performance
The ability to identify and solution for issues quickly requires the continuous monitoring of pipeline performance and data quality.
Establishing key performance indicators (KPIs) and metrics related to quality is itself key for operationalizing effective monitoring capabilities.
Ensure data security and compliance
Data security, privacy, and compliance checks should be embedded through data pipelines to protect sensitive information while helping the organization comply with all relevant regulations (e.g., GDPR, CCPA).
These compliance checks also require regular reviews, and security practices must be updated to keep pace with ever-evolving threats and regulations.
Invest in scalable and flexible infrastructure
Utilize cloud services and technologies that bolster operational pillars of effective DataOps, scalability, optimal data processing, and organizational flexibility.
In doing so, data teams should consider adopting data virtualization and containerization capabilities to improve agility and resource efficiency.
Leverage metadata management while planning for growth
Work to implement robust metadata management practices to improve data discoverability, understanding, and governance.
As part of planning for future (perhaps inevitable) growth, design data architectures and systems to allow for easy adaption and scaling. Use metadata to automate data lineage, data cataloging, and impact analysis.
Embrace ongoing learning
As part of laying the foundations for future growth, DataOps initiatives should encourage team members to continually seek out, evaluate, and adopt emerging technologies and practices that will benefit the organization over time.
DataOps is a journey, not a destination. As such, opportunities and areas of improvement should always be identified as part of a culture of testing, learning, and growing.
Common roadblocks to DataOps implementation (and how data contracts help)
Whether an organization is just getting off the ground or has already experienced some measure of success, DataOps practices may be poorly defined or functionality non-existent. This is also common and commonly attributed to one or a combination of many of the challenges that follow. Fortunately, drafting and implementing a robust data contract can help in all cases.
Change-averse cultures
It’s not uncommon for progressive thinkers to face resistance from within when trying to inspire a shift towards more collaborative, agile approaches to data management. Reluctance to adopt new processes (even those that are clearly beneficial) in addition to existing departmental silos can hinder DataOps initiatives before they can even begin.
In these situations, the data contract drafting process can act as a cultural balm, as it necessitates a clear definition of expectations, responsibilities, and roles of all parties involved in data management and usage (not just those directly involved in data analytics). The drafting process also mitigates resistance to new workflows by setting and/or codifying standardized processes all stakeholders must agree upon.
Skill or knowledge deficits
Even a modest implementation of DataOps requires a healthy mix of skills that span data engineering, data science, software development, and operations. Depending on their size or maturity, an organization may lack the diverse skill set and experience needed to integrate these distinct disciplines effectively.
No data contract is capable of directly addressing gaps in skills and experience. The implementation of data contracts does, however, clarify standards and requirements for data security, access, and quality. As such, they can indirectly guide employee training and development.
Inadequate data governance
As mentioned, effective DataOps relies on the foundation of solid data governance. Therefore, organizations may struggle to implement governance frameworks that are both robust and agile enough to support modern DataOps practices.
Data contracts directly support governance within an organization by establishing clear guidelines for data usage, sharing, and management. The framework for compliance and data quality standards established as they’re drafted makes governance more actionable and aligned with ensuing DataOps goals.
Complexities regarding data integration
The ability to integrate data from a variety of sources presents significant technical challenges, especially in complex or legacy IT environments. This complexity can stall the progress of DataOps implementation by making data pipelines hard to manage or brittle.
Conversely, data contracts simplify integration efforts by specifying data interfaces, standards, and formats. This ensures that raw data from any number of sources will be compatible with the organization’s data infrastructure and can be processed and analyzed with optimal efficiency.
Pervasive data quality issues
Poor data quality is (unfortunately) a common challenge in the business world, undermining analytics and eroding trust in data-driven decision-making. This can be an open secret too, with employees painfully aware of the issues substandard data is causing while struggling to quantify what exactly is to blame.
The metrics, validation processes, and remediation steps for non-compliance that data contracts define can make this a non-issue within organizations. This proactive approach (which is always ideal, especially in enterprise data environments) quickly becomes instrumental in maintaining high data quality across the organization.
Difficulties in scaling data operations
Finally, as organizations grow, it’s increasingly challenging to scale data operations efficiently while maintaining data quality. DataOps aims to address scalability issues but, ironically, a lack of foresight and flexibility regarding processes and infrastructure can prevent this from happening.
Data contracts circumvent these issues through the guidelines and expectations for data infrastructure, processing capabilities, and performance metrics they crystalize. This ensures that as the volume of data an organization must subsist on grows, systems and processes adapt and scale accordingly.
Drafting an optimal DataOps process
It’s clear that DataOps functions best at the genetic level of data-driven organizations. It’s also clear that, given such high stakes in the market, business leaders should be doing everything in their power to get their own DataOps right.
As covered in the last section, data contracts serve as an excellent solution to ensure this happens. That said, the benefits these contracts bring to a data-centric organization extend far beyond DataOps.
For a quick, digestible, informative primer on data contracts, give Why Data Leaders Opt for Ounces Over Pounds a read.
This article is part of our blog at Gable.ai — where our goal is to create a shared data culture of collaboration, accountability, quality, and governance.
Currently, our industry-first approach to drafting and implementing data contracts is in private Beta.
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