DataOps—a phrase I had not heard before. Now I know. Last week while I was in California I ran into John Harrington, who along with other former Kepware leaders Tony Paine and Torey Penrod-Cambra, had left Kepware following its acquisition by PTC to found a new company in the DataOps for Industry market. The news he told me about went live yesterday. HighByte announced that its beta program for HighByte Intelligence Hub is now live. More than a dozen manufacturers, distributors, and system integrators from the United States, Europe, and Asia have already been accepted into the program and granted early access to the software in a exchange for their feedback.

Intelligence Hub

HighByte Intelligence Hub will be the company’s first product to market since incorporating in August 2018. HighByte launched the beta program as part of its Agile approach to software design and development. The aim of the program is to improve performance, features, functionality, and user experience of the product prior to its commercial launch later this year.

HighByte Intelligence Hub belongs to a new classification of software in the industrial market known as DataOps solutions. HighByte Intelligence Hub was developed to solve data integration and security problems for industrial businesses. It is the only solution on the market that combines edge operations, advanced data contextualization, and the ability to deliver secure, application-specific information. Other approaches are highly customized and require extensive scripting and manual manipulation, which cannot scale beyond initial requirements and are not viable solutions for long-term digital transformation.

“We recognized a major problem in the market,” said Tony Paine, Co-Founder & CEO of HighByte. “Industrial companies are drowning in data, but they are unable to use it. The data is in the wrong place; it is in the wrong format; it has no context; and it lacks consistency. We are looking to solve this problem with HighByte Intelligence Hub.”

The company’s R&D efforts have been fueled by two non-equity grants awarded by the Maine Technology Institute (MTI) in 2019. “We are excited to join HighByte on their journey to building a great product and a great company here in Maine,” said Lou Simms, Investment Officer at MTI. “HighByte was awarded these grants because of the experience and track record of their founding team, large addressable market, and ability to meet business and product milestones.”

To further accelerate product development and go-to-market activities, HighByte is actively raising a seed investment round. For more information, please contact [email protected]

Learn more about the HighByte founding team —All people I’ve know for many years in the data connectivity business.

Background

From Wikipedia: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.

DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals.

DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, quality, security, access and ease of use.

From Oracle, DataOps, or data operations, is the latest agile operations methodology to spring from the collective consciousness of IT and big data professionals. It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and, ultimately, model deployment and management.

At its core, DataOps is about aligning the way you manage your data with the goals you have for that data. If you want to, say, reduce your customer churn rate, you could leverage your customer data to build a recommendation engine that surfaces products that are relevant to your customers  — which would keep them buying longer. But that’s only possible if your data science team has access to the data they need to build that system and the tools to deploy it, and can integrate it with your website, continually feed it new data, monitor performance, etc., an ongoing process that will likely include input from your engineering, IT, and business teams.

Conclusion

As we move further along the Digital Transformation path of leveraging digital data to its utmost, this looks to be a good tool in the utility belt.

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