What is DataOps?
An effective DataOps strategy helps companies accelerate digital transformation, enhance operational efficiency, and increase revenue
DataOps improves business outcomes by breaking down silos between people, process, and technology to enable better data agility for modern application development, IT operations, and data science initiatives.
Businesses implement multiple initiatives to achieve digital transformation. Many target improving customer experience by developing better applications designed to maintain and capture new business. Other initiatives focus on modernizing enterprise IT infrastructure and operations by adopting cloud technology.
Each of these initiatives generates hundreds and thousands of data operations that an enterprise must support and fulfill. These data operations are often inefficient, create unwarranted financial burdens, impact innovation velocity, and expose companies to governance and compliance risks.
DataOps enables companies to reduce costs and increase revenue by having a direct impact on development velocity, IT staff productivity, IT infrastructure costs, business risk mitigation, and regulatory risk mitigation.
DataOps Breaks Down Data Barriers to Digital Transformation
Data is the environment in which every enterprise operates and that environment is continuously growing and becoming more complex due to:
- The need for shorter innovation cycles
- Increasing demand for data from all corners of the enterprise
- Exploding volumes of data across multi-generational systems and apps
- Complexity of the modern hybrid cloud
All of these factors are testing the limits of traditional data management practices and technologies. Data users — including developers, testers, analysts, and data scientists — can’t always get the data they need to do their jobs in a timely, streamlined, and secure manner.
This results in a drain on enterprise resources, and impacts revenue growth due to:
- Lower customer satisfaction
- Unnecessary exposure to data governance and compliance risks
- Decreased application release velocity
- Increased risk of production errors
- Lack of accurate business insights
- Longer recovery from production downtimes
- Increasing spend on data storage capacity
The ability of an enterprise to overcome data barriers determines success with digital transformation, emphasizing why DataOps methodologies and technologies are important for unblocking data for innovation.
Why DataOps Has Become an Imperative for Innovation
The reality is that every company is a data company and data is becoming the most valuable resource. Using this data to shape the customer experience and accelerate innovation is key to success as customers expect the digital experience to be as rich or richer than offline. Therefore, it is imperative that enterprises get a handle on accessing, delivering, and protecting their data. A DataOps implementation that unifies people, processes, and technology is key to surviving and thriving in this changing world.
Improving Outcomes Through the Benefits of a DataOps Platform
The right DataOps platform can:
Accelerate digital transformation
Improve time to market for innovative digital solutions
Reduce business risk
Lower operational costs
DataOps improves outcomes by eliminating friction at every stage of the data value chain, and at every level of data use:
- Strategy: Enabling digital transformation and strategic intelligence by giving developers and analysts access to data that is as close as possible to the actual state of the business
- Operations: Increasing communication and reducing friction between data managers and data users. And protecting sensitive and private data from users who don’t need to see it by building governance into data distribution.
- Technology: Empowering users to control the data they use. Enabling data managers to automate access to a wide variety of on-premises and cloud data sources.
Modern DataOps processes, technologies, and platforms should be designed from the ground up to reduce friction, give users the data they need to do their best work, and empower data managers to get things done without compromising security or privacy.
Multiple Teams Benefit from a DataOps Implementation
- Reduces business risk by as much as 76%
- Decreases data operations costs by as much as 39%
- Reduces storage infrastructure costs by 72%
- Increases IT staff productivity by an average of 24%
Development and testing teams
- Decreases development cycles by an average of 40%
- Reduces the time to develop new apps by 30% and upgrades by 41%
- Reduces application errors by 70%
Security and Compliance
Improves security management productivity by 28%
Site Reliability Engineering
Reduces production downtime and MTTR outages
- Increases primary data access by 50%
- Enables more complete and accurate modeling
Steps for How to Start with DataOps
Unify the enterprise around the value of data
Before investing in DataOps tools, IT leaders must evangelize the value of continuous data flow to fuel digital innovation and customer experience transformation. Data-driven initiatives need top-down and bottom-up support across all teams joining together with a unified vision and plan around investing in a DataOps methodology and platform.
Enable continuous data delivery and self-service
A DataOps platform automates the data delivery process and enables continuous data delivery. API-driven automation integrates data delivery into workflows across hybrid and multi-cloud environments, from structured, unstructured, SQL, NoSQL, and cloud-native data sources. Self-service data access, with the ability to refresh to a current data state, provides developers, testers, analysts, and data scientists with production-quality data in minutes.
Mitigate data compliance risks
A complete DataOps platform integrates data masking with data virtualization to ensure that sensitive customer information is protected in dev/test and other non-production environments. It enables you to find sensitive data non-production environments and irreversibly mask data values with realistic, yet fictitious equivalents. With a DataOps platform-based approach to masking referential integrity is preserved across all data sources.
As enterprises embrace cloud platforms for added efficiency and agility, they are starting to realize that managing and distributing data across highly distributed environments is a major impediment to speed,