There is no shortage of data in any industrial company, but there is a general lack of understanding on how to extract it, bring it together, and use it in an actionable way.
There are two discomforting truths within digital transformation across our key industries; energy, utilities, and manufacturing:
Digitalization PoCs are commonplace. Real ROI isn’t.
Billions are invested in cloud data warehouses and data lakes. Most data ends there, unused by anyone for anything.
At the heart of this data-driven value dilemma lies a confluence of challenges, ranging from the technical (How can we best organize our diverse and fluid data universe?) to the operational (How can we create new information products and services?), to the financial (How can we treat data as an asset?), to the human (How can we improve data literacy and ensure digital solution adoption in the field?).
With more and more of our industrial operations data readily in the clouds, Chief Data Officers (CDO) are confronted with the hard reality that moving data to the cloud is not even a third of the journey to value. As Forrester (2021) put it elegantly, “data has no value unless the business trusts it and uses it.”
Solving the data quality challenge is however not as straightforward as filling cloud data warehouses and data lakes have been. Investing millions into another doomed MDM project — only this time for the cloud — is equally erroneous. Instead, adopting a data product-centric mindset, along with a DataOps practice to create and manage data products, is needed.
The convergence of data and analytics has made Industrial DataOps an operational necessity. The focus of DataOps is the delivery of business-ready, trusted, actionable, high-quality data, available to all “smart engineers” or “data consumers.”
The shift from data availability to data products as a service is what will transform our data swamps into operational data lakes of real business value. Implementing Industrial DataOps to collaboratively develop, manage, and operationalise data products is how you get there.
To guide you, we've summarized the journey from data fabric to DataOps to data products:
Industrial DataOps, or data operations for industry, is the clear frontrunner to become a driving force for transformation in the industry.
- This happens first by making your data available, by identifying where it is, how to get at it—and how to store it for later use
- The next step is then making your data useful—freeing it from silos and making it speak “human”, so that its value can be released across operations. Contextualize it and design homes for it such that all of the minds and functions in your organization can actually understand it, use it, and innovate on top of it.
- And finally, making data valuable. Extracting the maximum value depends on being able to obtain insights that inform better decisions, and enabling all business users to become solution creators. Scaling these benefits brings data value to the whole organization.
Cognite is a global industrial AI Software-as-a-Service (SaaS) company supporting the full-scale digital transformation of heavy-asset industries around the world. Their key product, Cognite Data Fusion® (CDF), empowers companies with contextualized OT/IT data to drive industrial applications that increase safety, sustainability, and efficiency, and drive revenue.
By Team Cognite
Cognite is a global industrial SaaS company that supports the full-scale digital transformation of asset-heavy industries around the world. Our core Industrial DataOps platform, Cognite Data Fusion®, enables data and domain users to collaborate to quickly and safely develop, operationalize, and scale industrial AI solutions and applications. Cognite Data Fusion® codifies industrial domain knowledge into software that fits into your existing ecosystem and enables scale from proofs of concept to truly data-driven operations to deliver both profitability and sustainability.