“So, when is it time for HighByte?”

Adding a new software application to your technology stack requires care and planning. It will require onboarding, training, and maintenance. So, when manufacturers ask our team, “When is it time for DataOps? When do I need HighByte?” we know they are weighing the advantages of the solution with the time and money they will need to invest to make it successful.

Of course, I would argue that it’s better to maintain a secure application than hidden repositories of fragile, custom code. Moreover, I believe DataOps is becoming the prevailing approach to addressing the challenges posed by industrial data. The ROI is real.

DataOps solutions like HighByte Intelligence Hub have proven themselves necessary in manufacturing environments where data must be aggregated from industrial automation assets and systems and then leveraged by business users throughout the company and its supply chain. These solutions are purpose-built to improve the orchestration, observability, quality, and governance of industrial data at scale. According to Gartner’s Market Guide for DataOps Tools, a data engineering team guided by DataOps practices and tools will be 10 times more productive than teams that do not use DataOps by 2025.

But when does it make sense to introduce HighByte Intelligence Hub into your own digital architecture? We commonly see 4 signs that justify this investment.

4 Signs That It’s Time for DataOps

1. You have more than two consuming applications. DataOps is useful in data architectures containing multiple target applications being fed data by the same source. Modeling data independently of the application allows users to reuse the engineering as they onboard new applications and makes building new integrations much simpler. If all you have is a single application requiring data, you probably don’t need HighByte Intelligence Hub. Even a second application can be onboarded using data modeling from the first. If you need to onboard any applications beyond those two, it becomes unscalable without a dedicated DataOps solution.

2. You have use cases with evolving requirements. If your use case evolves or requires additional data points, a DataOps solution is the right choice. Manually altering code every time a use case requires an update is an unscalable process and makes the integration inherently fragile. This is especially important as use cases scale and grow in quantity. If the integrations are handled inside of an IIoT platform, they may suffer from many of the same problems as point-to-point integrations—namely, lack of scalability and fragility. The question you should ask to discover if the integrations are scalable: Can I move this engineering work to a different site, use case, or application? If not, it’s time to evaluate a DataOps solution that provides a single pane of glass to manage these integrations and transformations. If the use case is simple and data requirements are static, a direct integration will likely suffice.

3. You have data variability across sites. For manufacturing and industrial companies, the data produced by different sites is often unique. Industrial data sources vary widely in how they present data, and different sites use different machinery, all producing data in different ways. Some may publish on-change data streams and others may provide updates at 10-minute intervals, but all sources present their data with their own contextualization and shape.

If you have variability across lines and sites, a DataOps solution can provide the central governance and necessary contextualization to ensure data consistency at scale. This consistency makes powering up new use cases, integrating new applications, and onboarding new sources much simpler and faster. Furthermore, using a DataOps solution to govern lines and sites prevents vendor lock-in, ensuring that data can be shaped to fit any purpose with ease.

4. You have a team of stakeholders. Constructing a data architecture to power integrations should benefit stakeholders across the organization. Highly functional use cases require application owners, data scientists, data consumers, OT domain experts who understand source systems, and architects to instill best practices and accelerate deployments.

Integrations and use cases constructed without DataOps are often difficult for non-domain experts to understand. They offer little engineering that can be repurposed, and can’t be easily altered to benefit additional users beyond the original need. These challenges all contribute to a discouraging environment for further adoption.

In contrast, a DataOps solution should provide a low-code/no-code interface to give everyone the visibility and accessibility required to accelerate adoption of use cases and encourage the development of new use cases across the organization.

Solving Problems with DataOps

At the end of the day, DataOps solutions provide a toolbox to solve a wide range of problems depending on the unique needs of the manufacturer. These include:

• Lack of data contextualization and normalization across machines, systems, and other operational technology (OT) data sources.
• Difficulty preparing and correlating manufacturing data for Quality and Maintenance users to analyze product or asset information, respectively.
• Accessibility constraints from Operations to IT, from edge to cloud, and from the business to third parties.
• Difficulty orchestrating, monitoring, and securing data flows.
• Strain on operations personnel caused by time-consuming manual contextualization and point-to-point integrations to IT systems.
• Writing and maintaining custom code, leading to technical debt and inability to scale projects across sites.
• Data governance requirements and regulatory pressures.

These problems have existed in manufacturing for more than a decade and are becoming more pronounced as sensors proliferate, cloud usage expands, and generative AI strives to meet its potential. Isn’t it time we solved them?