Camo newsroom

Industrial analytics thought leadership-series.

How will industrial analytics, Industry 4.0, IIoT and digital transformation change businesses? And ultimately the world? We ask industry leaders to share their view on these technologies, how we use them, and where they will take us.

Colin Shearer:

Real time is too late

With no equivalent of GDPR to limit exploitation of machine data and given the fact that any industry is already based on extensive domain expertise, you should utilize streaming data and keep building your analytical engine with a scientific approach. Avoid self-learning algorithms in a black box. 

“In manufacturing and industry real time is too late. A lot of cost and effort has gone into deploying condition monitoring, but the end result is usually a blizzard of alarms that surface when the equipment is already seriously damaged, often too late to prevent unscheduled downtime. It is so much more powerful and valuable to get an early warning hours, days or weeks ahead, in time to adjust and avoid unplanned production failure or quality issues. And today, the streams of data painting a highly complex picture of system and process behavior give you the possibility to predict and prevent through the knowledge of correlations and causality in data,” says Colin Shearer.

“To me it is quite obvious that success and value in industrial analytics comes from bringing together a relevant business problem, the scientists or engineers knowing the process and product, and the data scientists with an advanced tool box. While you may now utilize big data, sensor data and a whole array of new analytics capabilities, you cannot get the outcome you need unless you include the layer of human expertise.”

Empowering analysts

Colin Shearer is now working with three companies, keeping closely involved in driving the advanced use of data and drawing upon decades of analytical experience. He designed the Clementine data mining workbench, which is now IBM SPSS Modeler, and led the team that developed it.

“We found that we could empower analysts and enable them to undertake train of thought analysis; build and fail quickly; reduce complicated techniques such as combining models to a few clicks. We were ahead of the market at a time when the data mining discipline was only just emerging.”

Obsessed with AI

Today there are new challenges in the industrial analytics space in the triangle between advanced analytics software, the profession of data scientists and deep domain knowledge.

“The biggest trend I am seeing is that people are obsessed with…. Sorry… seeing the potential of AI. In around 2010 we saw the Big Data wave which has now blended into the AI wave. Both have had a positive effect in that most senior executives now appreciate there’s potential value in these technologies.  But their organisations often have no clue how to proceed, and so they embark on what end up being science projects,” says Colin Shearer.

“Data excitement is a pitfall here – the idea that simply throwing data at smart technologies will give transformational results. It doesn’t, and that’s leaving a lot of would-be adopters disappointed. Blind faith in AI will eventually fade,  as the value of incorporating human expertise becomes more and more evident. And you will probably see this happening very fast in industrial analytics.”

Complex processes

Industrial analytics is characterised by highly complex processes, the huge potential in streaming data and the high volume and high value in getting it right. In pharma, manufacturing, process production, utilities and more, companies build on sciences like chemistry, biology and geology, and the success of the companies build on domain expertise within these scientific fields. Big Data is a source for improvement for companies, but its value is only released when it’s combined with human expertise.

Machines and GDPR

“In contrast to the challenges we currently face in other application areas for advanced analytics: machines do not care about GDPR! So adding sensors, adding more and more streams of data to be exploited with analytics is the way to go. The key point is, though, that you can’t reap the benefits unless you analyse in the context of your domain knowledge. Today’s analytical technologies can deliver new levels of knowledge, and are key to more automation, greater predictive power and significant value contributions. But you need to align these powerful tools with scientists and engineers who understand the complexity of the systems and processes being analysed,” says Colin Shearer.

Business outcome

The combination of data, advanced analytics tools, data scientists and domain experts is powerful, but Colin Shearer emphasises the need to have the business outcome in sight. It’s business first and tools later. In industry it may be prevention of unplanned shutdown, solving quality problems, efficiency gains and much more.

“Projects have to be driven by business problems and goals because these determine which analytics approach to use and which data to apply it to. And you have to plan from the start around how your business will benefit. It doesn’t matter how technically brilliant your analytical work is; until you do something effective with the results and enhance your current operations it is meaningless. Getting AI right has the potential to transform virtually every aspect of business, with improved decision making driving better outcomes across the board. This is also the case for industrial analytics, where it is possible to embed analytics in the process.”

The industrial revolution of analytics

The craft of the data scientist will be more integrated in production and other business processes.

“Most “data science” today consists of one-off analyses done by mathematical or statistical geeks working at the coding level. The notorious labelling of data scientist as “the sexiest job of the 21st century” is probably partly responsible for this “craftsman” approach. But we’re going to have to see changes to that going forward,” he says.

“Just like the way manufacturing was transformed, the craftsmen in analytics will also face an industrialisation. Stand-alone one-man analytics doesn’t deliver results efficiently and it doesn’t scale. Data science needs to go through its own equivalent of the Industrial Revolution, with more focus on automation and deployment,” says Colin Shearer.

“The base line for me is, that it’s not just a matter of applying smart technology; it’s essential to incorporate human domain expertise. In industry deep knowledge about systems and process engineering is at the heart of the analytical approach, and this knowledge is key to interpreting the output of the analyses. Breakthrough recommendations derived from analytics in a complex process are most effectively delivered delivered “expert to expert”.”

Colin Shearer:

Industry veteran on a career-long journey in analytics

Colin Shearer has been a pioneer and thought leader in analytics for over 25 years. His experience ranges from successful start-ups and the creation of market-leading tools and technology, to worldwide executive roles with the largest vendors. Today, he provides advice and assistance to end-user organizations and to vendors, helping them set their vision, plan and execute within analytics.

Colin’s career path has been one of acquisitions: From UK-startup Integral Solutions Ltd., to SPSS, to IBM. He decided to semi-retire two years ago but failed at executing that strategy, so he is now engaged in three companies. He is Chief Strategy Officer at Finnish Houston Analytics, one of the strongest and most experience applied data science teams in Europe. He is also Chief Business Development Officer at Danish Agillic, merging AI capabilities into marketing automation tools to boost marketing effectiveness. And Colin furthermore advises Aberdeen-based OPEX Group, who help oil and gas operating companies to eliminate production losses and reduce maintenance costs by leveraging AI and analytics technologies.

“The base line for me is, that it’s not just a matter of applying smart technology; it’s essential to incorporate human domain expertise. In industry deep knowledge about systems and process engineering is at the heart of the analytical approach, and this knowledge is key to interpreting the output of the analyses. Breakthrough recommendations derived from analytics in a complex process are most effectively delivered delivered “expert to expert”.”

Find the right solution for your analytics needs.

Get in touch if you have questions about our products, platform, how to get started or how best to address your analytics needs.

Contact form

Topics