A recent Gartner survey shows how data professionals spend their working time:
- More than half (56%) of their time is focused on data management activities.
- Only 22% of their time drives innovation with enhanced analytical insights.
The rapidly growing concept of DataOps aims to balance innovation and management control on your data pipeline. Yes, it may take significant investments and resources to rethink your data management practices and pipeline. But the opportunity cost—slowing down data-driven decisions due to inadequate data management agility and control—is far more expensive in the long run.
So, let’s take an introductory look at DataOps and how it helps you become a data-driven organization.
What is DataOps?
Short for Data Operations, DataOps is a process-oriented, automated, and collaborative approach to designing, implementing, and managing data workflows and a distributed data architecture. As more teams across the business build, deploy, and act upon data-driven models, DataOps aims to:
- Deliver high value
- Manage risks
DataOps adopts the guiding principles of DevOps, Lean manufacturing, designing thinking, and Agile, applying them to data engineering and infrastructure management and operations tasks. These are three key domains of DataOps:
- Communication & Collaboration. DataOps methodology encourages communication and collaboration between data engineers, developers, and operations personnel.
- Automation. Like DevOps, DataOps promotes using advanced technology solutions to automate the data management and operations processes while incorporating appropriate governance controls.
- Culture. The engineering process is Agile, driven by collaboration and rapid use of technology to automate repeatable processes. In a DataOps environment, data is considered a shared asset, So, any data models must follow the end-to-end design thinking approach.
As more companies and organizations harness big data, getting the data quality and data processes right will be critical to success.
How to apply DataOps
Similar to DevOps, DataOps is not a technology solution or an exhaustive best-practice guideline. It’s a methodological approach that identifies principles to manage data, operations, people, and processes.
DataOps effectively brings the data management discipline under a single umbrella: data policies, data governance, and data management are abstracted from the underlying data assets and applications. The decisions are applied consistently form a single point of management across all apps and infrastructure.
The result? Value-driven analytics processes used at scale across the organization.
Best practices for implementing DataOps
The following considerations and best practices can help your organization implement DataOps:
Data access is one of the biggest barriers to adoption of intelligence technologies that have the power to transform your organization’s decision-making capabilities. AI technologies require continuous training with new data for improved insights relevant to the dynamical parameters and real-world situations.
Democratizing access to data—in compliance with security and regulatory frameworks—can be a gamechanger for collaborating DataOps teams working on data projects.
Go open source
DataOps encourages the use of open-source solutions that follow industry standards and integrate with existing (and legacy) technologies without the need for heavy customization. The flexibility and availability of open-source solutions means you can build your own data processing pipeline, which has its pros—and cons when not done right.
(Understand three keys to building resilient data pipelines.)
Automate where possible
Data processing requires a lot of automation: from infrastructure provisioning to transforming data and capturing metadata. The correct use of automation saves time on resource-intensive tasks in the repetitive data pipeline.
A word of caution on automaton, though. Similar to DevOps, automating everything may not be the best approach. Automating waste processes only deteriorates productivity.
Communicate & collaborate
DataOps follows the philosophy that data assets don’t belong to specific teams and individuals. Within the allowed provisions of security and governance policies, IT, data engineers, operations teams and teams from other business functions should readily collaborate on data assets and insights.
DataOps treats data as a shared asset and breaks downs organizational boundaries. A true DataOps environment will make two critical changes:
- Systematically remote the instinct to control ownership of information assets (data).
- Provide the tools and training necessary for teams across organizational departments to take advantage of data.
End-to-end design thinking
Reconsider the end-to-end data processing pipeline based on the guiding principles from DevOps, Agile, and Lean manufacturing. This may require your organization to drastically transform your operational workflows and your culture.
Any changes should draw on:
- The perspective of end-users
- The overall business impacts
Use automation tools and advanced AI technologies to empower DataOps teams with the capabilities necessary to maximize the value potential of data.
Strict governance processes often slow down development and data pipeline. They also stifle innovation as users are limited to using only a few approved technology solutions that may require a high learning curve, offer limited functionality and therefore, impact productivity. This leads to security lapses, like users adopting Shadow IT at large scale.
DataOps organizations can address this challenge by operating a library of tools and governance measures that streamline the process of requesting, vetting, and delivering new data solutions to end-users.
DataOps delivers value from data
By following these guidelines, you’ll also adhere to the 18 guiding principles of The DataOps Manifesto and deliver true value in data analytics across the organization. As a result, you’ll address many key challenges facing data pipeline processes and the associated IT infrastructure operations.
- BMC Machine Learning & Big Data Blog
- BMC Guides, which offers series of data solution-based tutorials
- Data Architecture vs Information Architecture: What’s The Difference?
- Comparing Data Analytics vs Data Analysis
- 3 Simple Data Monetization Strategies for Companies
- Data Ethics for Companies
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
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