Adopting a smart data mindset in a world of big data
09/05/2021 10:14
Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution.1 AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever. Using months or even years’ worth of information, analytics models can tease out efficient operating regimes based on controllable variables, such as pump speed, or disturbance variables, such as weather. These insights can be embedded into existing control systems, bundled into a separate advisory tool, or used for performance management.
Many companies in heavy industry have spent years building and storing big data but have yet to unlock its full value. In fact, our research shows that more than 75 percent have piloted some form of AI, yet less than 15 percent have realized meaningful, scalable impact. In these companies, analytics teams typically take an outside-in approach to AI and machine learning, including using various stochastic methods on top of process data that have been engineered with minimal operational insight. That approach can work, but it usually produces models that exhibit a high parameter dependence, require frequent retraining, have a high number of inputs, or give unphysical or unrealistic results. Consequently, these models rarely endure in production or achieve meaningful impact before operators and engineers lose confidence in them.
To succeed with AI, companies should have an automation environment with reliable historian data. Then, they will need to adapt their big data into a form that is amenable to AI, often with far fewer variables and with intelligent, first principles–based feature engineering. We term the latter format “smart data” to emphasize the focus on an expert-driven approach that improves predictive accuracy and aids in root-cause analysis. This article describes steps for creating smart data, along with approaches to bolstering and upskilling expert staff. Our experience shows that success in both areas can result in an EBITDA3 increase of 5 to 15 percent.
A common failure mode for companies looking to leverage AI is poor integration of operational expertise into the data-science process. Indeed, we advocate applying machine learning only after process data have been analyzed, enriched, and transformed with expert-driven data engineering. In practice, we suggest the following steps
1. Define the process
2. Enrich the data
3. Reduce the dimensionality
4. Apply machine learning
5. Implement and validate the models
Deploying AI in heavy industry requires cross-functional teams made up of operators, data scientists, automation engineers, and process experts. We often find that companies have (or are hiring to fill) roles for data science, but they face three main challenges regarding process experts: there is a dearth of process expertise either at a specific facility or across the company; there are sufficient process experts, but they are not comfortable with modern digital or analytical tools; or process experts don’t know how to work effectively on digital teams
It can be challenging to create high-performing teams using cross-functional roles because of differences in approach. For example, it is common for operations employees to follow unidirectional stage-gated processes—often for safety reasons—whereas data-science colleagues are usually familiar with iterative workflows, such as agile. When deploying AI, our experience shows that iterative, inclusive, and colocated agile teams tend to realize the most impact. As a result, coaching is needed for colleagues unfamiliar with this approach.
Planning out the model development can be a good exercise to solidify a way of working and avoid linear approaches that include exhaustively completing one stage (such as data extraction) before proceeding to the next. Instead, pieces of each stage should be completed concurrently to quickly develop a fully working model with the intention of maturing individual components in future iterations. In practice, this usually means starting with a subset of sensor data, creating a limited list of features, and working with simpler algorithms. Then, the team can decide what to invest in for the next stage. As part of each iteration, there should be a discussion of what the definition of “done” is to align on the outcome and avoid scope creep.
Industrial companies are looking to AI to boost their plant operations—to reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations (such as mass-balance or thermodynamic relationships). They will also need to form cross-functional data-science teams that include employees who are capable of bridging the gap between machine-learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.