Focusing on how to design & develop your teams, this month, reminded me of rule elicitation work I did over 20 years ago.
We have previously focussed on the breadth of skills, including different technical skills, needed within an analytics team. As well as the modern passion for new role titles (Data Engineer, Data Journalist etc), it can be worth learning from the past.
Back in the early 1990s, I was fortunate enough to work in an R&D team, piloting different AI applications.
It still surprises me how many of the techniques available then would be regarded as cutting edge now. As well as a wider range of Neural Network architectures, I also piloted Case Based Reasoning, Fuzzy Logic, Genetic Algorithms & Rule Induction.
One of the lessons from that wave of AI, was the importance of knowledge elicitation from human experts. As skill that is perhaps overlooked today. So, I’m delighted to welcome as a guest blogger Akeel Attar. Akeel was implementing rule based expert systems, while I was still learning about these techniques, so has much practical wisdom to impart.
In this post, Akeel shares the relevant of rules-based intelligence & knowledge elicitation to the modern challenge of how to automate industrial applications of Internet of Things (IoT). I’m delighted to be reconnected with Akeel after all these years & to see the practice; advice he is sharing.
Over to Akeel, to help your team learn from the lessons of over 20 years in this industry…
There is more to intelligence than automated analytics
Automated analytics (which can also be referred to as machine learning, deep learning etc.) are currently attracting the lion’s share of interest from investors, consultants, journalists and executives looking at technologies that can deliver the business opportunities being afforded by the Internet of Things. The reason for this surge in interest is that the IOT generates huge volumes of data from which analytics can discover patterns, anomalies and insights which can then be used to automate, improve and control business operations.
One of the main attractions of automated analytics appears to be the perception that it represents an automated process that is able to learn automatically from data without the need to do any programming of rules. Furthermore, it is perceived that the IOT will allow organisations to apply analytics to data being generated by any physical asset or business process and thereafter being able to use automated analytics to monitor asset performance, detect anomalies and generate problem resolution / trouble-shooting advice; all without any programming of rules!
What else is needed for automating IoT applications?
In reality, automated analytics is a powerful technology for turning data into actionable insight / knowledge and thereby represents a key enabling technology for automation in Industrial IOT. However, automated analytics alone cannot deliver complete solutions for the following reasons:
- In order for analytics to learn effectively it needs data that spans the spectrum of normal, sub normal and anomalous asset/process behaviour. Such data can become available relatively quickly in a scenario where there are tens or hundreds of thousands of similar assets (central heating boilers, mobile phones etc.). However, this is not the case for more complex equipment / plants / processes where the volume of available faults or anomalous behaviour data is simply not large enough to facilitate effective analytics learning/modelling. As a result any generated automated analytics will be very restricted in its scope and will generate a large number of anomalies representing operating conditions that do not exist in the data.
- By focussing on data analytics alone we are ignoring the most important asset of any organisation; namely the expertise of its people in how to operate plants / processes. This expertise covers condition / risk assessment, planning, configuration, diagnostics, trouble-shooting and other skills that can involve decision-making tasks. Automating ‘Decision making’ and applying it to streaming real-time IOT data offers huge business benefits and is very complementary to automated analytics in that it addresses the very areas in point 1 above where data coverage is incomplete, but human expertise exists.
Capturing expertise into an automated decision-making system does require the programming of rules and decisions but that need not be a lengthy or cumbersome in a modern rules/decision automation technology such as Xpertrule. Decision making tasks can be represented in a graphical way that a subject matter expert can easily author and maintain without the involvement of a programmer. This can be done using graphical and easy to edit decision flows, decision trees, decision tables and rules. From my experience in using this approach, a substantial decision-making task of tens of decision trees can be captured and deployed within a few weeks.
How symbolic analytics can help with automating IoT
Given the complementary nature of automated analytics and automated decisions, I would recommend the use of symbolic learning data analytics techniques. Symbolic analytics generate rules/tree structures from data which are interpretable and understandable to the domain experts. Whilst rules/tree analytics models are marginally less accurate than deep learning or other ‘blackbox models’, the transparency of symbolic data models offer a number of advantages:
- The analytics models can be validated by the domain experts
- The domain experts can add additional decision knowledge to the analytics models
- The transparency of the data models gives the experts insights into the root causes of problems and highlights opportunities for performance improvement.
Combining automated knowledge from data analytics with automated decisions from domain experts can deliver a paradigm shift in the way organisations use IOT to manage their assets / processes. It allows organisations to deploy their best practice expertise 24/7 real-time throughout the organisation and rapidly turn newly acquired data into new and improved knowledge.
Below are example decision and analytics knowledge from an industrial IOT solution that we developed for a major manufacturer of powder processing mills. The solution monitors the performance of the mills to diagnose problems and to detect anomalous behaviour:
The Fault diagnosis tree below is part of the knowledge captured from the subject matter experts within the company.
The tree below is generated by automated data analytics and relates the output particle size to other process parameters and environmental variables. The tree is one of many analytics models used to monitor anomalous behaviour of the process.
The above example demonstrates both the complementary nature of rules and analytics automation and the interpretability of symbolic analytics. In my next posting I will cover the subject of the rapid capture of decision-making expertise using decision structuring and the induction of decision trees from decision examples provided by subject matter experts.