DEVOPSdigest

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.