Industrial DataOps refers to the practice of effectively managing and utilizing data in industrial settings to drive business value and decision-making. It involves addressing the challenges associated with data acquisition, storage, processing, quality assurance, security, and privacy in operational technology (OT) environments.
The Dexcent IDS paper highlights several key perspectives and challenges related to Industrial DataOps which include the following:
- Organizational Challenges: The paper emphasizes that the pace of change in leveraging data is dependent on how well organizations establish strategies, engage stakeholders, communicate consistently, and execute plans. The focus is on sustainable transformation, scaling, and sustaining change within the organization.
- Data Challenges: The complexities of transforming data into useful information in an OT environment are discussed. These challenges include data acquisition, optimization of data collection rates, storage, retrieval, missing data, outliers, noise, time alignment, data integrity during transit, loss of data context, integration of different data types, errors in manual entry/transposition, and more. The paper stresses the importance of data curation to ensure that data is ready for business consumption, considering different data curation standards based on business criticality.
- People Challenges: The paper acknowledges the evolving workforce landscape, where fewer people are responsible for operating industrial facilities. The rapid pace of technological advancements surpasses the ability of people to adapt, and the paper suggests the need for the next generation of “Industrial DataOps Practitioners.” These practitioners are described as senior generalists equipped with the skills to handle the convergence of information technology (IT) and OT.
- Process Challenges: The paper emphasizes a methodical approach to unlock business value by identifying and leveraging various assets, including individuals, teams, projects, skills, knowledge bases, and datasets. Baseline assessments of the current state of data availability, governance models, and confidence factors are recommended. The establishment of governance frameworks is also crucial for data collection, storage, processing, analysis, quality assurance, security, and privacy.
- Technology Challenges: The paper highlights the vast array of technology options available, along with the challenge of selecting the most appropriate ones. Insufficient attention to critical success factors beyond technology, such as data-related issues, can hinder the success of projects. Balancing the choice between best-in-class point solutions and a unified platform is discussed, as well as the need for adaptable technology approaches to keep pace with rapid changes in the technology landscape.
- Innovation Challenges: The paper underscores the importance of fostering a culture of continuous improvement and driving innovation. It emphasizes the need for organizations to encourage experimentation, risk-taking, collaboration, and knowledge sharing. Understanding different types of innovation (incremental, disruptive, and radical) and identifying opportunities in the market are crucial. Execution and mitigation of risks associated with change are also essential for successful innovation.
In summary, the paper emphasizes the multifaceted nature of Industrial DataOps and addresses challenges related to organizations, data, people, processes, technology, and innovation. By addressing these challenges, organizations can unlock the full potential of their data, make better business decisions, and stay competitive in the rapidly evolving industrial landscape.
Please feel free to download the paper and enjoy: https://www.dexcentids.com/wp-content/uploads/2023/06/IDS.whitepaper.1.2.pdf
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