A common cause of frustration of clients is a difficulty in advancing from a successful industrial predictive analytics pilot project to a full enterprise rollout.

Predictive Analytics has the capability to improve how industrial businesses operate, by revealing insights from their data which highlights improvements that can be made to processes, and can transform the reliability and performance of critical infrastructure using predictive maintenance. However the transformational benefits are only obtained when the solution is applied enterprise wide.

So we’ve come up with five helpful tips so you don’t get stuck at the proof of concept stage;


Most organisations will want to test out a solution to assess its merits before adopting it more broadly. When it comes to selecting the problem statement you want to address in your initial Proof of Concept, we recommend picking an area that is costing the business a significant amount of money (perhaps from frequent downtime or an undiagnosed root cause) and already has data already recorded. No-code Artificial intelligence is at its most powerful when it has historical and live data to analyse, ideally 12 months of historical data is sufficient. If you have a significant issue but no data, we suggest starting to record the data now and addressing that issue in the future. Most companies will give AI one shot, and therefore you want to make sure the stakes are high enough to get the buy-in from the C-suite, and to show-off the true benefits of predictive analytics.

If you discover you have unreliable data, DataHUB4.0 can help. 


During the pilot and as you rollout your predictive analytics solution enterprise wide, it is important to highlight your successes. These could be successful predictions or catches of future equipment failures. Record the savings and benefits from the predictions, number of incidents avoided through early intervention, number of days without a major equipment failure, production loss avoidance, improvement of maintenance planning, number of people logging into the system, improvements to productivity, new teams implementing the solution, and general maintenance savings. No matter the size of the success, make sure it is highlighted. It is important for the project to remain highly visible across the organisation. This ensures on-going support from upper management and stakeholder buy-in.


After a successful pilot it is important to be ready to scale quickly in order to reap the true benefits. A projects momentum can be lost if there is a delay in rolling it out company wide. We’ve even see companies suddenly change their strategy as something urgent comes along, halting the implementation altogether. The goal is to embed the solution into the companies framework so it becomes business as usual. It may require a change in processes by operational and maintenance teams, such as the incorporation of reviewing predictive dashboards and AI models during shift changes or daily stand up meetings, if so, this needs to be planned, prioritised and communicated clearly.  Most of the time these teams will appreciate having technology that provides insights into why their equipment is failing, however they may be afraid of it replacing their jobs or being over-worked and so push-back may be experienced. If you need help building your team this article on ‘The players in predictive maintenance‘ may help.

We recommend being ready with a list of processes and equipment you want to model next, keeping high value problems high on your agenda. This will help you scale and continue to obtain buy-in from all stakeholders.


Companies that breakdown internal silos, and remove competitiveness between divisions, position themselves for success. No code AI and predictive analytics is most powerful when working with data from un-seemingly related processes, as it identifies unexpected root cause of failures. However for this to be effective, teams need to communicate freely and work together to optimise not only their processes but the whole plant. We encourage companies to look at the way they incentivise staff to promote team work and the adoption of the technology.  Some companies have started to adopted multi-skilled centralised teams which have a mandate to look after the reliability and productivity across a whole plant, as opposed to individual processes.  


We have all become more accustomed to change as a result of the COVID Pandemic, and just like that, advancing predictive analytics will take some navigating and change management with your team. By promoting the wins, increasing communication and reassuring the team of the purpose behind the change, you’ll be able to navigate any issues that arise and embed the technology within your organisation. Be prepared this may take time, however any worthwhile business transformation always does.


We’d love to hear how you are advancing predictive analytics in your workplace, and if you need any assistance or would like to know more about implementing OPUS, our no-code AI predictive analytics solution please contact our team.

Useful Resources

You might be interested in

Comparison of DataHUB+ and OSIsoft PI

Compare OSIsoft PI System to VROC's DataHUB+ and see how the two stack up against one another

Read Article

DataHUB+ : A Modern Alternative to PI ProcessBook

With Pi ProcessBook retiring, DataHUB+ emerges as a strong contender as a Process Historian and Visualization Tool.

Read Article