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

Sri Raghavan

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

November 30, 2023

Parasoft, a global leader in automated software testing solutions, today announced complete support for MISRA C++ 2023 with the upcoming release of Parasoft C/C++test 2023.2.

November 30, 2023 achieved the Amazon Elastic Kubernetes Service (Amazon EKS) Ready designation from Amazon Web Services (AWS).

November 29, 2023

CircleCI implemented a gen2 GPU resource class, leveraging Amazon Elastic Compute Cloud (Amazon EC2) G5 instances, offering the latest generation of NVIDIA GPUs and new images tailored for artificial intelligence/machine learning (AI/ML) workflows.

November 29, 2023

XM Cyber announced new capabilities that provide complete and continuous visibility into risks and vulnerabilities in Kubernetes environments.

November 29, 2023

PerfectScale has achieved the Amazon Elastic Kubernetes Service (Amazon EKS) Ready designation from Amazon Web Services (AWS).

November 28, 2023

BMC announced two new product innovations, BMC AMI DevX Code Insights and BMC AMI zAdviser Enterprise.

November 28, 2023

Rafay Systems announced the availability of the Rafay Cloud Automation Platform — the evolution of its Kubernetes Operations Platform — to enable platform teams to deliver automation and self-service capabilities to developers, data scientists and other cloud users.

November 28, 2023

Bitrise is integrating with Amazon Web Services (AWS) to provide compliance-conscious companies with greater access to CI/CD capabilities for mobile app development.

November 28, 2023

Armory announced a new unified declarative deployment capability for AWS Lambda.

November 27, 2023

Amazon Web Services (AWS) and Salesforce announced a significant expansion of their long standing, global strategic partnership, deepening product integrations across data and artificial intelligence (AI), and for the first time offering select Salesforce products on the AWS Marketplace.

November 27, 2023

Veracode announced product innovations to enhance the developer experience. The new features integrate security into the software development lifecycle (SDLC) and drive adoption of application security techniques in the environments where developers work.

November 27, 2023

Couchbase announced a new Capella columnar service on Amazon Web Services (AWS), enabling organizations to harness real-time analytics to build adaptive applications.

November 21, 2023

Redgate announced the launch of Redgate Test Data Manager, which simplifies the challenges that come with Test Data Management (TDM) and modern software development across multiple databases.

November 21, 2023

mabl announced an integration with GitLab, the AI-powered DevSecOps platform.

November 21, 2023

FusionAuth announced the availability of new software development kits (SDKs) that support Angular, React and Vue JavaScript front-end frameworks.