The DataOps Manifesto: 4 Keys to Creating a Truly Data-Driven Business
February 17, 2022

Sri Raghavan
Teradata

DataOps has emerged as an Agile methodology to improve the speed and accuracy of analytics through new data management practices and processes, including code automation. Simply understood, DataOps is data management for the AI era, powering both automation at scale and friction-free collaboration between humans and machines.

In the digital era, organizations commonly serve their frontline workers' needs with hundreds of applications, collectively generating anywhere between thousands and millions of queries every day. The challenge for IT teams is that these applications are not static; they must constantly evolve to meet the organization's ever-changing needs. By introducing and enhancing automations, DataOps can improve application performance, security, and data analytics with only modest human oversight.

Dataops Enables Organizations to Improve Data Quality and Efficiency

Implemented correctly, DataOps is an enterprise-wide Agile approach designed to ensure every person, system, or machine has secure access to the right data, when and where it’s needed. Rather than simply streamlining the flow of ever-increasing quantities of data, DataOps focuses on improving the quality and speed of data analytics from initial data preparation to final reporting.

Additionally, integrating AI and machine learning can improve productivity by reducing development time — intelligently identifying, suggesting, and testing solutions for issues with code. Automations introduced by DataOps can also augment security, using machines to spot and triage vulnerabilities. Given the vast number of cybersecurity threats enterprises now face, turning vulnerability detection and prioritization over to a machine means freeing precious IT team human resources to focus on bigger issues.

Reality Check: Dataops Depends on Validated, Respected Solutions

DataOps innovations can be incredibly valuable, particularly during a talent crunch that has limited organizations' abilities to expand their human IT resources. However, operationalizing AI at a scale sufficient to meet the demands of today’s data-driven enterprises is no easy feat.

In reality, very few organizations are presently capable of widespread deployment of scaled ML and AI solutions in production environments. Deploying DataOps requires an organization to be aligned with the correct change-focused mindset and select a data platform with trustworthy tools — ones that have already been validated as beneficial by other, comparable companies.

A Successful Dataops Strategy Requires Purposeful Organizational Changes

In order to implement successful and sustainable DataOps practices, companies must ensure that the correct processes are in place to drive operationalization of their results, and that their business cultures are receptive to analytical insights. Broadly speaking, if a DataOps strategy aspires to truly realize the next evolution of data management, it will require the following four steps:

1. Embracing change. Effective operationalization begins with the organization evaluating its existing structure and processes, then welcoming rather than impeding change. Deep adoption may require changing the culture of the organization or specific business units to embrace continuous change through constant learning from both stakeholders and customers.

2. Exalting quality. While AI can rapidly produce high-quality results, unexplained or underexplained conclusions can undermine human trust in the technology. Data governance is important and taking a human-guided approach is key. Without the ability to self-police, the data set will be at risk of bias and drift, negatively impacting the organization's desired or intended results.

3. Mandating teamwork. Historically, enterprises allowed individual business units to manage their own data, leading to everything from incompatible data formats to separately stored and managed information. In the modern era, identifying and improving utilization of high-value data depends upon breaking down old data silos — a step that enables IT teams to work on the entire data set, and determine appropriate levels of aggregation and pre-analysis.

4. Adopting new techniques and tools. Identifying fit-for-purpose AI tools and adopting the agile "test and learn" approach, which enables key stakeholders to see the tools' results and provide feedback to continuously improve their performance, will play a key role in driving AI workflows. As suggested above, the organization's culture needs to embrace and internalize this feedback to improve AI results over time.

Introducing DevOps into data-driven organizations means raising the bar for agility — a structural, cultural upgrade that many businesses will realize is long overdue — and making them more competitive. Moreover, pairing DevOps practices with well-governed AI solutions that are capable of scaling to data warehouse environments will position data-driven businesses for success in an increasingly dynamic world.

Sri Raghavan is Director of Data Science and Advanced Analytics at Teradata
Share this

Industry News

June 29, 2022

Progress announced the latest release of Progress Flowmon.

June 29, 2022

CodeSee announced the launch of Open Source Hub (OSH).

June 29, 2022

Ambassador Labs announced the newest release of Ambassador Edge Stack, an integrated edge solution that empowers developer teams to quickly configure the edge services required to build, deliver, and scale applications for Kubernetes.

June 29, 2022

Ondat released into general availability version 2.8 of its Ondat platform for stateful workloads in Kubernetes.

June 28, 2022

Hewlett Packard Enterprise (HPE) unveiled platform enhancements and new cloud services for HPE GreenLake, the company’s flagship offering that enables organizations to modernize all their applications and data.

June 28, 2022

Sysdig announced Drift Control to prevent container attacks at runtime. Teams can detect, prevent, and speed incident response for containers that were modified in production, also known as container drift.

June 28, 2022

ShiftLeft announced an investment from and go-to-market partnership with Wipro Ventures.

June 27, 2022

Delinea announced the latest release of DevOps Secrets Vault.

June 27, 2022

Jit announced a $38.5 million seed funding round and launched a free beta version which automates product security.

June 27, 2022

Platform.sh raised $140 million in Series D funding.

June 23, 2022

Akana by Perforce now offers BlazeMeter to customers, previously a solution with Broadcom Layer7.

June 23, 2022

Coder announced the release of a new open source project that gives developers and data scientists a consistent, secure, yet flexible way to create cloud workspaces in minutes.

June 23, 2022

GitGuardian is announcing a series of new features to address developer experience in securing the software development lifecycle.

June 22, 2022

OctoML released a major platform expansion to accelerate the development of AI-powered applications by eliminating bottlenecks in machine learning deployment.

June 22, 2022

Snow Software announced new functionality and integrations for Snow Atlas, a purpose-built platform that provides a framework to accelerate data-driven technology decision-making.