Data as a Factor of Production

The World Economic Forum has described data as a “new factor of production”—one with the potential to reshape business models, shift industry boundaries, and create entirely new market structures. That framing resonates across industries: whether you work in finance, retail, gaming, or telecoms, the question executives are increasingly asking is not whether to invest in data analytics, but how to make those investments actually work for their teams.

The answer, increasingly, is not simply hiring more data scientists. It is about removing the bottlenecks that prevent every layer of the business—sales, marketing, logistics, operations—from acting on data independently and confidently.

The Bottleneck Problem: Why Most Data Teams Are Stretched Thin

In most organizations today, data access follows a chain of dependency that creates serious throughput problems:

• Business users (in sales, marketing, logistics) need data insights to do their day-to-day jobs—but they rely on data analysts to surface that information.
• Data analysts handle reporting and dashboards, but for predictive or advanced analytics they depend on data scientists.
• Data scientists build models, but they depend on data engineers to ensure that data pipelines, ingestion jobs, and processing infrastructure are running reliably.

This cascade means that a question from a sales manager can take days or weeks to become an answer—filtered through multiple specialists, each with their own queue of work. Restricting advanced analytical tools to a select few is not scalable, and it frustrates every layer of the team.

Democratizing Data Access: What It Actually Means

Democratizing data does not mean giving everyone unrestricted access to raw systems. It means providing role-appropriate interfaces so that each user can independently get the answers they need, without blocking someone else’s work. A well-designed modern analytics platform achieves this through layered access:

• Standard SQL interfaces for data analysts who already know the language—enabling them to write queries, build dashboards, and manage pipelines without waiting on engineers.
• Spreadsheet integrations (such as connected sheet views) for business users who are not comfortable with SQL but are fluent in tools like Excel or Google Sheets.
• Natural language query interfaces that allow non-technical users to ask plain-language questions of their data—no query syntax required.
• Notebook and API integrations (supporting environments like Jupyter, and libraries like Spark, Pandas, and Presto) for data scientists who want to stay in their preferred analytical environments while working against centralized data stores.
• Automated ML capabilities embedded in the SQL layer, so that data analysts can train and evaluate predictive models using familiar syntax—without requiring a dedicated data science handoff.

When data analysts can self-serve, data engineers are freed to focus on higher-value work: data quality, lineage, governance, and architecture. When business users can self-serve, data analysts shift their attention to complex analysis and model validation. Each layer moves up the value chain.

Real-Time and Predictive Analytics: Why They Matter Now

The COVID-19 pandemic demonstrated, more clearly than any business case document, that the ability to react to rapidly shifting conditions is not a luxury—it is a survival capability. Businesses that could observe customer behavior changes in near-real-time and act on predictive signals adapted more effectively than those relying on weekly or monthly reporting cycles.

Streaming analytics pipelines—where data is processed and surfaced as it arrives rather than in scheduled batch jobs—combined with ML-based forecasting give operational teams the situational awareness they need to make decisions in hours rather than days. This is especially relevant in logistics, retail demand planning, fraud detection, and customer service operations.

Practical Steps: Building a Data-Empowered Organization

Moving toward a data-driven operating model is a significant undertaking, but it does not need to happen all at once. A pragmatic sequence typically looks like this:

1. Centralize your data. A single, governed data warehouse or lakehouse that all teams can query eliminates the silos that make cross-functional analytics nearly impossible.
2. Standardize on open interfaces. Avoid proprietary query languages or formats where possible. SQL remains the most widely understood analytical language across data roles.
3. Layer access by role. Deploy the right interface for each user type: SQL for analysts, spreadsheet connectors for business users, notebook APIs for scientists.
4. Introduce AutoML gradually. Embedded ML capabilities (such as building a classification or forecasting model inside a SQL query) allow analysts to experiment with predictive analytics before committing to a full data science engagement.
5. Measure the bottleneck reduction. Track time-to-insight for common business questions before and after tooling changes. This is your primary ROI signal.
6. Evolve governance alongside access. As more users interact with data, invest in data cataloguing, lineage tracking, and role-based access controls to maintain trust and compliance.

The Strategic Payoff: Freeing Your Specialists for High-Value Work

One of the most underappreciated benefits of data democratization is what it does for your most skilled people. When data engineers are no longer spending the majority of their time making data available to analysts—running one-off extracts, fixing broken ad-hoc pipelines—they can concentrate on the architectural work that has lasting impact: building reliable ingestion frameworks, enforcing data quality standards, and designing scalable infrastructure.

Similarly, when data scientists are not bottlenecked on answering routine business questions, they can focus on developing more sophisticated, production-ready models and working on the operationalization (MLOps) challenges that turn a working notebook into a reliable business capability.

The organizations that get this right—where every person who needs data can access it in a form that works for them—are the ones that develop the institutional capacity to innovate continuously, rather than in occasional, expensive bursts.

Key Takeaways for DevOps and Data Engineering Teams

• Data bottlenecks are an organizational problem as much as a tooling problem—address both together.
• Self-service analytics platforms reduce ticket queues and free engineers for strategic infrastructure work.
• Embedding ML capabilities in SQL-based tools lowers the barrier to predictive analytics across data roles.
• Real-time streaming pipelines are increasingly a baseline expectation, not a premium feature.
• Governance and access control must scale alongside democratization—open access without guardrails creates data quality and compliance risk.
• Measure time-to-insight as a concrete engineering KPI to demonstrate the value of platform improvements.

Originally sourced from Forbes.com. The Google Cloud platform examples referenced (BigQuery, BigQuery ML, Connected Sheets, Data QnA) reflect the product landscape as described in the original 2020 article. Verify current product names, features, and availability in the official Google Cloud documentation.

Key Finding: DevOps Adoption Is Not Uniform Across Sectors

DevOps encompasses a broad set of development, operational, and security practices — and not every industry is adopting them at the same pace. That is one of the central findings of the 2019 State of DevOps: Industry Report Card, a 40-page report published by DevOps vendor Puppet in late January 2020.

The report card is derived from Puppet’s eighth annual State of DevOps survey, which gathered responses from approximately 3,000 technical professionals. Rather than presenting aggregate findings, this edition slices the data by industry vertical — grading financial services, retail, telecom, government, and technology on both DevOps practice adoption and security integration within the development pipeline.

“As the creator and one of the authors of the State of DevOps Report for the past eight years, and working closely with numerous customers through the various stages of their DevOps evolutionary journeys, the findings were more validating than surprising.”

— Alanna Brown, Senior Director of Community and Developer Relations, Puppet

Industry Grades at a Glance

The report card assigned letter grades to each industry across two dimensions: overall DevOps adoption and security integration. The results highlight meaningful gaps between sectors.

Note: The source article did not publish specific grades for all five industries. The table above reflects only the grades explicitly reported.

Technology Sector Leads the Pack

The technology industry earned an “A” for DevOps adoption and an “A−” for security integration — the highest scores across all sectors surveyed. According to Brown, this outcome was expected: DevOps principles tend to be embedded in the culture and operating model of technology companies from an early stage, making security integration a natural extension of existing practices rather than a retrofit.

Financial Services Held Back by Technical Debt and Silos

Financial services and insurance firms received a “B” for DevOps adoption but only a “C−” for security integration — a significant gap between operational maturity and security practice. Brown attributed this disparity to several structural factors common in established financial institutions:

• Accumulated technical debt from decades of legacy system investment
• Outdated technology stacks that are difficult to modernize incrementally
• Heavy regulatory requirements that slow the pace of change
• Functional silos between development, operations, and security teams that impede collaboration

These constraints make it harder for financial services firms to fully integrate security into the DevOps pipeline, even when they have made meaningful progress on the operational side.

Retail Prioritizes Deployment Frequency

While the technology sector scored highest overall, the retail industry stood out for one specific metric: on-demand deployment capability. According to the report, 57% of retail respondents said they are capable of deploying to production on demand, and 28% reported that they actually do so regularly.

“We work with a lot of big retailers, so it’s no surprise to us that they are heavily focused on deployment frequency because deploying changes faster gives them a clear competitive edge.”

— Alanna Brown, Puppet

For retailers, the ability to push updates quickly — whether for pricing, product availability, or customer-facing features — is a direct competitive advantage, which helps explain the sector’s emphasis on deployment pipeline maturity.

Government Struggles with Both Adoption and Security

The government sector received the lowest overall assessment in the report, earning only a “C” for DevOps adoption. Only 41% of government agencies reported the ability to deploy code on demand — well below the retail sector’s 57%.

Security integration was an even more significant weakness. The report stated:

“Out of all industries, government had the lowest percentage of respondents reporting that security tools are integrated into the development ecosystem, and security and development teams collaborate on threat models (9 percent).”

— 2019 State of DevOps: Industry Report Card, Puppet

A 9% collaboration rate between security and development teams is a stark indicator of how siloed government IT environments remain. Without integrated threat modeling and shared tooling, the gap between development velocity and security assurance is likely to widen rather than close.

Most Large Enterprises Are “Stuck in the Middle”

Beyond the industry-specific findings, Brown highlighted a broader challenge facing large enterprises across all sectors: many have adopted DevOps practices in isolated pockets but have not been able to scale those practices organization-wide.

“We have been working very closely with large enterprises on their DevOps initiatives, and we’ve found that most are stuck in the middle. They’ve adopted some practices and seen success in pockets, but there are still considerable barriers to scaling DevOps.”

— Alanna Brown, Puppet

This “stuck in the middle” dynamic is a well-recognized challenge in DevOps transformations. Pilot successes in individual teams or product lines do not automatically translate to enterprise-wide adoption, particularly when organizational structure, culture, governance, and tooling remain inconsistent across business units.

What This Means for DevOps Practitioners

The industry-segmented view of DevOps maturity offered by Puppet’s 2019 report card provides useful benchmarks for teams evaluating their own progress. A few practical takeaways:

• Security integration is a lagging indicator across most industries. Even sectors with relatively strong DevOps adoption (financial services) scored significantly lower on security integration. DevSecOps remains an area where most industries have room to improve.
• On-demand deployment is a meaningful maturity signal. The gap between capability (57% in retail) and actual practice (28%) suggests that infrastructure readiness alone is insufficient — cultural and process changes are also required.
• Government and regulated industries face structural barriers. Technical debt, compliance requirements, and organizational silos are not problems that tooling alone can solve. Transformation in these sectors requires executive sponsorship and deliberate process change.
• Scaling beyond pilot programs is the defining challenge for enterprises. Teams that have demonstrated DevOps success locally should prioritize identifying and removing the organizational blockers that prevent broader adoption.

Puppet has published the State of DevOps Report annually since 2012, making it one of the longest-running longitudinal data sets on DevOps adoption. The full 2019 Industry Report Card is available through Puppet’s website.

Overview

Linker Networks, Inc. announced in late 2019 that it was extending its AI-based auto-labeling platform — which includes a continuous learning service — to Microsoft Azure Stack Hub. Azure Stack Hub is Microsoft’s on-premises extension of the Azure cloud, designed for organizations that need to run hybrid applications in environments where full public-cloud connectivity is restricted or undesirable.

The move was significant for regulated industries such as government, healthcare, security, and mobile network operators, where data sovereignty and compliance requirements often prevent organizations from sending sensitive data to public cloud endpoints. By deploying on Azure Stack Hub, Linker’s customers could keep all data within their own data centers while still accessing the company’s managed AI services.

What Is Auto-Labeling and Why Does It Matter for AI Development?

Training a machine learning model requires large volumes of accurately labeled data. Manual annotation is slow, expensive, and error-prone at scale. Auto-labeling platforms use existing AI models to pre-annotate new data automatically, dramatically reducing the human effort required to build training datasets.

Linker’s platform adds a continuous learning loop to this process: as human reviewers correct or confirm auto-generated labels, the underlying model improves over time. This feedback cycle increases accuracy and reduces the proportion of data that requires manual review in subsequent batches.

Use cases highlighted by Linker at the time of the announcement included:

• Medical imaging — labeling radiological scans and other clinical images for diagnostic AI models.
• Autonomous driving — annotating camera, lidar, and sensor data for perception model training.
• Manufacturing — detecting defects and labeling production-line imagery for quality-control AI.

Azure Stack Hub Integration: Edge Inference and Data Residency

Before the Azure Stack Hub extension, Linker had launched its auto-labeling platform on the public Microsoft Azure cloud in June 2019, targeting vertically integrated industries that could tolerate or preferred cloud-based data processing.

The Azure Stack Hub integration added two capabilities that are particularly relevant for edge and regulated deployments:

1. On-premises data processing — raw datasets never need to leave the customer’s data center, satisfying data residency and compliance requirements common in government and healthcare sectors.
2. Real-time edge inferencing — models trained with Linker’s auto-labeled data can be served directly on Azure Stack Hub hardware, reducing round-trip latency to a central cloud and enabling faster operational decisions.

Together, these features deliver what Linker described as an end-to-end solution: from raw data ingestion and annotation, through model training, to production inference — all running within a customer-controlled environment.

Medical Imaging Highlight at RSNA 2019

Linker noted that at the 2019 Radiological Society of North America (RSNA) annual meeting, Microsoft showcased the Linker platform built for medical imaging during one of its sessions. Medical imaging is a natural fit for auto-labeling technology: radiology datasets are large, annotation by qualified clinicians is costly, and model accuracy is safety-critical — making a high-quality, continuously improving labeling pipeline especially valuable.

Executive Perspectives

“We at Linker see auto-labeling dataset processing plays an essential part in AI machine learning development. Our goal is to provide end-to-end AI solutions for various businesses in different industries. We are excited to have the privilege to work with Microsoft and to realize our service on Azure and Azure Stack.”

— Paul Shieh, Founder and CEO, Linker Networks

“We are pleased to see Linker Networks extending its AI-based auto-labeling service to Azure Stack Hub and enable customers to label millions of data points for the development of their AI models. This new service will benefit a wide variety of customers looking to build models on the edge or prepare their data to move to the cloud.”

— Natalia Mackevicius, General Manager, Azure Stack Hub, Microsoft

Key Takeaways for DevOps and MLOps Teams

• Data residency is a first-class concern in AI pipelines. Hybrid infrastructure like Azure Stack Hub is increasingly important for organizations that cannot move training data to the public cloud.
• Edge inferencing reduces latency. Running trained models on the same infrastructure that processes source data avoids network round-trips to a central cloud, which matters in latency-sensitive applications like medical devices or industrial automation.
• Continuous learning closes the annotation loop. Auto-labeling platforms that incorporate human feedback into model updates can substantially reduce long-term annotation costs compared to one-time manual labeling efforts.
• Regulated verticals require specialized MLOps strategies. Government, healthcare, and security teams evaluating AI tooling should assess whether a vendor’s platform supports on-premises or sovereign-cloud deployment modes, not just public cloud.

This article is based on a press announcement from December 2019. Product capabilities, pricing, and partnership details may have changed since the original publication.