{"id":26987,"date":"2026-03-26T16:01:10","date_gmt":"2026-03-26T16:01:10","guid":{"rendered":"https:\/\/www.novotek.com\/de\/industrial-dataops\/getting-started-with-industrial-dataops\/"},"modified":"2026-03-27T14:56:15","modified_gmt":"2026-03-27T14:56:15","slug":"getting-started-with-industrial-dataops","status":"publish","type":"page","link":"https:\/\/www.novotek.com\/de\/en\/data-architecture\/industrial-dataops\/getting-started-with-industrial-dataops\/","title":{"rendered":"Getting started with Industrial DataOps"},"content":{"rendered":"
\n
\n

Getting started with Industrial DataOps<\/h1>\n<\/div>\n\n
\n

Industrial companies need contextualized data and a digital architecture that grows with them. Learn how Industrial DataOps helps organizations overcome these challenges.<\/p>\n<\/div><\/div>

\"\"<\/figure><\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n
<\/span>
\n

Key concepts and relationships in Industrial DataOps<\/h2>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n

DataOps has become an established concept in recent years\u2014but what does it actually mean, and which problems does it solve? This page provides a practical introduction. <\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

The Fourth Industrial Revolution has made one thing clear: production data must do more than simply control processes; it forms the basis for Industry 4.0 applications. Teams across all areas of the business, from quality assurance to maintenance, need access to this data in order to make better decisions more quickly. <\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

However, industrial data often lacks context. It is isolated in files or legacy systems.
Industrial DataOps<\/strong> links this data, makes it accessible and puts it to use.<\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n
\n
\n

Data Operations<\/strong><\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

Data Operations (DataOps<\/em>) orchestrates people, processes, and technology to deliver secure, reliable, and usable data to those who need it. Originating in IT around 2014, DataOps focuses on improving data quality and reducing the effort required for data preparation. <\/p>\n<\/div><\/div>\n<\/div>\n\n

\n
\n

Industrial DataOps<\/strong><\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

Industrial DataOps<\/strong> is an emerging discipline that provides flexible data architectures for digital transformation.
In manufacturing environments, it brings together data from various automation and production systems – making it accessible and usable for business users across the enterprise and supply chain. <\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

Data Engineering<\/strong><\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n

Data engineering is a core component of DataOps. Data engineers build pipelines to collect, transform, and validate data. Using models and pipelines, they standardize and contextualize data, combine multiple sources, and manage data flows, at the edge, on-premise, or in the cloud. <\/p>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div><\/div><\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n

6 signs you need Industrial DataOps<\/h2>\n<\/div>\n\n
\n
\n
\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

Your digital initiatives are slowing down due to data challenges<\/p>\n<\/div>\n\n

\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

Custom cloud scripts make scaling and security difficult
<\/p>\n<\/div><\/div>\n<\/div>\n\n

\n
\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

You are incurring high cloud costs for raw data without a clear strategy<\/p>\n<\/div>\n\n

\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

You lack visibility into who has access to operational data<\/p>\n<\/div><\/div>\n<\/div>\n\n

\n
\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

Analysts spend more than 50% of their time preparing data<\/p>\n<\/div>\n\n

\n
\"\"<\/figure>\n<\/div><\/div>\n\n
\n

Your OT team is overloaded with requests from business teams<\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n
<\/span>
\n

The limits of traditional approaches<\/h2>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n
\n
\n

Cloud dump<\/h3>\n<\/div>\n\n
\n

Raw data is pushed into the cloud without processing\u2014expensive, unstructured, and difficult to use.<\/p>\n<\/div>\n\n

\n


<\/p>\n<\/div><\/div>\n<\/div>\n\n

\n
\n

Custom code<\/h3>\n<\/div>\n\n
\n

Point-to-point integrations result in countless connections that are hard to maintain.<\/p>\n<\/div>\n\n

\n


<\/p>\n<\/div><\/div>\n<\/div>\n\n

\n
\n

In-house development<\/h3>\n<\/div>\n\n
\n

Internally developed solutions are difficult to sustain, as manufacturers are not specialized software vendors.
<\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div>\n\n

\n
<\/div>\n<\/div><\/div><\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n

The HighByte approach to Industrial DataOps<\/h2>\n<\/div>\n\n
\n

An Industrial DataOps solution for Industry 4.0 requires four key components:<\/p>\n<\/div>\n\n

\n
\n

Data orchestration<\/h3>\n<\/div>\n\n
\n

IT\/OT integration, pipelines, and real-time and historical data flows<\/p>\n<\/div><\/div>

\"\"<\/figure><\/div>\n<\/div>\n\n
\n
\"\"<\/figure>
\n

Data observability<\/h3>\n<\/div>\n\n
\n

Monitoring and diagnostics for data quality and performance<\/p>\n<\/div><\/div><\/div>\n<\/div>\n\n

\n
\n

Data quality<\/h3>\n<\/div>\n\n
\n

Reliable, consistent, and timely data for compliance and decision-making<\/p>\n<\/div><\/div>

\"\"<\/figure><\/div>\n<\/div>\n\n
\n
\"\"<\/figure>
\n

Data governance<\/h3>\n<\/div>\n\n
\n

Policies and roles for secure and compliant data management<\/p>\n<\/div><\/div><\/div>\n<\/div>\n\n

\n
<\/div>\n<\/div>\n\n
\n
<\/div>\n<\/div>\n\n
\n

What makes HighByte Intelligence Hub unique<\/h2>\n<\/div>\n\n
\n

In addition to the four key components above, HighByte Intelligence Hub <\/strong>stands out through:<\/p>\n<\/div>\n\n

\n