Industrial AI

How to convert your data into a critical business asset

Published on:
May 11, 2022

Is your manufacturing company one of the many collecting industrial data today? If so, you are aware of the positive business outcomes it can provide. From greater productivity, improved quality control, asset optimization, waste reduction and emission control – the opportunities are endless. According to a report released by IDC, worldwide data will grow 61% to 175 zettabytes by 2025. However, a study conducted by BCG shows only 30% of digital transformation projects meet or exceed targets that result in sustainable change. This study is representative of the many challenges plaguing manufacturing companies today, who are investing in collecting large volumes of data but are unable to derive actionable insights that deliver business value.

Data can be a liability for organizations who are unable to analyze it correctly to drive continuous improvement that supports their overall business strategy.

There are several ways to make sure your data is a critical business asset that enhances decision-making:

1. For data-driven initiatives to be successful, organizations need to invest in upskilling and reskilling the workforce to drive impactful results and measurable ROI. This can give employees the opportunity to use digital skills to solve a business problem they’re directly involved in.

2. For data to be useful, it needs to be representative of a specific use case or problem you are trying to solve. For instance, if your goal is to improve productivity, the data needs to represent everyday operations. The quality and depth of the data will determine the level of AI integration you are trying to achieve.

3. For AI to scale, industrials must have the data infrastructure to support it. Whether your data is stored in the cloud or on premise, it requires robust, standards-based, systems for securely storing it and making it available to other systems (such as the Canvass platform). Manufacturing companies should have a data governance program in place to define how data is stored and managed, how new data and data sources are added and how data is accessed. Companies that have mature data governance practices will be able to get started with AI much faster.

Therefore, the convergence of people, process and technology is critical to the success of data-driven initiatives. People are the centre of any AI-powered transformation and empowering your workforce to back your AI strategy is critical to success. This means creating a high-performance culture where employees are well equipped to make decisions based on data-driven insights. Processes enable people to work efficiently, provide them the operational tools they need to succeed and prevent them from reinventing the wheel every time they begin a project. This in turn can drive greater efficiency and maximize impact. Technology provides the tools people need to execute the process and deliver success. The seamless interaction of people and processes with technology leads to insights that are data-driven, actionable and measurable. This integration can enable industrials to architect the scalability of their AI use case around their learnings and success, build processes that can be tested; and subsequently roll that framework, scale with it and then take it enterprise-wide.

Conclusion

As more and more manufacturing companies ramp up their digital investments, it is imperative to start small – with one asset or one work stream and then apply that understanding on a large scale. This will allow you to test your people, processes and technology, succeed, and scale-up the benefits across the enterprise.

Canvass’s AI platform enables industrials to transform their data into insights 12X faster than other platforms. Comprising of pre-built AI solutions, industrial engineers are using the Canvass AI platform to optimize processes and operations, improve production yield and improve energy efficiency faster and more effectively than ever.