What’s Next for Industrial IoT and Advanced Analytics

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Industry 4.0 is still a continuous evolution, with the Internet of Things at its heart. Digital transformation in industrial environments continues today and has been accelerated by the recent pandemic. What does the future of industrial IoT and advanced analytics look like? What priorities should the C-Suite have as we approach 2022 and beyond?

McKinsey estimated that by 2020, the total value captured by IoT was $1.6 trillion, with the B2B market likely to grow between $3.4 and $8.1 trillion by 2030. This valuation shows that there are still significant value opportunities to be realized in the years to come.

In order to realize this value, certain barriers and opportunities need to be addressed in business and digital strategies across the industry. The foundations for growth have been laid with the rapid development of IoT hardware, as well as the ability to store large data, with costs for both reducing significantly over the years. And the focus is now on how we use this acquired data to generate value.

1. Interoperability of systems to acquire better data

Scaling digital transformation has proven to be one of the toughest hurdles faced by businesses in the IoT space. Many pilots have failed to scale, limiting adoption rate and value realization. One of the causes is a systemic barrier that has been created by the use of proprietary closed ecosystems, as well as the mixing of legacy systems, the mixing of different data architectures and bespoke IoT sensor languages. In order to benefit from advanced analytics, data must be obtained and shared between systems, so that insights can be gathered across the enterprise. To do this, companies must require interoperability in all future purchases and plan to address legacy issues from the past.

2. Plan data storage for future advanced analytics

Advanced analytics, artificial intelligence, and machine learning use Big Data, in its raw, unstructured format. Companies need to change their approach to capturing, storing and managing this data. For predictive analytics, time-series data is essential, so businesses should plan to use cloud data warehouses and adopt graph databases to make the most of new advanced analytics technology. available.

3. Advanced analytics, an enterprise-wide initiative

Value will be realized when organizations evolve and start using advanced analytics such as artificial intelligence and machine learning throughout their operations. Rather than small pilot programs or restricting the use of solutions to internal data science teams, companies need to start planning for the use of advanced analytics across the organization. Data democratization occurs when individuals across the organization begin to analyze data to help inform their day-to-day tasks. McKinsey believes that “the greatest potential for creating value lies in optimizing manufacturing operations, making the day-to-day management of assets and people more efficient.”

4. No-Code Machine Learning and MLOps

Advanced analytics automation is the next big opportunity for industrial companies. Technology has advanced and no-code machine learning (ML) is now being deployed by organizations around the world. No-code ML enables subject matter experts and operators to quickly build models of their assets or operations without any coding or programming knowledge. Models are automatically deployed, learn from live and historical data, and provide critical insights to help the individual optimize their operations. We see this being used for predictive maintenance and real-time condition monitoring. ML Ops is the application of continuous integrated testing and continuous deployment through automation to deliver scalable and up-to-date data models to industrialize machine learning. It is through the industrialization of machine learning that model automations can be implemented, contributing to the scalability of advanced analytics across the enterprise.

5. Enabling Remote and Automated Operations

The shift to remote working and centralized operations has driven innovations such as remote monitoring and increased automation in many settings. These innovations will help reduce operating costs, security risks for personnel and help realize more of the value that can be generated by the IoT. The ability to remotely monitor and receive alerts when productivity, an outage or an error is predicted improves team efficiency. Advanced analytics provides root cause analysis ensuring the correct personnel and parts are called to site, as well as information that enables operators to make informed decisions, such as adjustments in processes or equipment used to ensure that no loss of productivity occurs.

6. Compliance and emission reduction

Industry-wide companies set emissions targets, the next step is to ensure they are in compliance with these targets. IoT and advanced analytics can help organizations determine accurate baselines for setting goals and can monitor ongoing usage. Areas of significant energy use can be identified along with opportunities for potential improvement. Auto ML can be used to forecast peaks in energy consumption to aid in energy storage and waste minimization.

7. Overall business analysis

Merging data and advanced analytics across the enterprise provides an opportunity to improve forecasting, reporting, and compliance. Data can be used to drive growth, optimization and diversification strategies. The information can be used to improve processes and can help in knowledge sharing between different divisions and business units.

The value possibility of each IoT and advanced analytics use case can vary greatly. And so the ultimate goal to capture the total value possible is to embed innovation across the entire organization from the top down. Digital transformation no longer resides within the IT department or the innovation team. For real value to be recognized, it must be integrated into the life of the organization.

The challenge is to scale and do so at a rapid pace so that value can be realized quickly. This in turn will help to change internal cultures, procedures and methodologies. Momentum will increase as pilots turn to deployments and improvements are made to reduce bottlenecks, increase decision-making accuracy, and improve overall organizational results.

Trevor Bloch, Group Founder and CEO, VROC AI

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