Democratizing AI: Everyone should be able to work with data


The time for experimenting with artificial intelligence (AI) is over. In a COVID-19 climate, it is important to be able to quickly scale up analytical models to industrial processes. SAS believes the solution can be found in democratizing AI.

Most manufacturing businesses have large amounts of data at their disposal. In the chemical industry, in particular, companies have taken important steps toward using AI and analytics. But do not expect a large army of data scientists. Such an investment is not feasible for any organization, and there are simply not enough profiles on the market. Therefore, it is important that companies get their existing engineers and operators on board the AI train.

Democratizing AI is about making the technology widely available and incorporating techniques into day-to-day operational processes so that more people can work with them. Of course, it is still necessary to have data scientists, but they will be more effective if they can focus on areas where they can really make a difference.

No more time to play

Artificial intelligence and machine learning are not new technologies. Data analytics has been the expertise of SAS for more than 44 years. In manufacturing, both concepts have become trendy buzzwords that everybody likes to use. Yet few people understand what you can really do with these technologies. As a result, experiments often remain a proof of concept (POC). COVID-19 has changed the world we live in, and for AI, too, playtime seems to be over.

Unless a POC is industrialized, it is no more than a research object. After all, predictions are worthless until they lead to actions. Whoever invests in data analytics wants to see rapid results. This means that companies must be able to quickly industrialize such a POC and then replicate it. This is only possible with an integrated platform on which many people work together.

By enabling more people to work with the same technology, flexibility increases – and a model can also evolve much faster to respond to changes in the market.

This used to be the sticking point, but fortunately, many managers are now starting to realize that they can apply AI in multiple domains. In the past, they often used separate techniques for different departments, but in doing so they limited their own scalability. By enabling more people to work with the same technology, flexibility increases and a model can also evolve much faster to respond to changes in the market.

In addition, the real value of AI is not in building a model for one installation but comes when you can replicate that model for other installations. If it takes about 100 days to implement the first model in an operational process, then its replication should be done in no more than 10 to 15 days.

Building bridges

For SAS, democratizing AI has become an important foundation to build on. More than a seller of technology, SAS wants to be a partner for customers within a wider ecosystem. No one has better knowledge of processes than the engineers and operators who work with them every day. Here lies the key to unlocking the true potential of data analytics.

SPG Dry Cooling, for example, is a manufacturer of cooling installations for power plants. Data analytics has allowed this company, with headquarters in Brussels, to extend its services. After selling an installation, they never received feedback from customers in the past. Now they collect the data that enables them to improve customer services, for instance, to predict when an installation needs maintenance and even increase the energy production of power plants.

Many companies are still developing maturity but now need to accelerate. That is why SAS builds bridges between data scientists and engineers while supporting the development of the right competencies. If you can get these two groups to work together, they will discover that they are looking at the same things in a different way. By merging both visions, you create a new dynamic. You can only experience the real power of data when you add context to it.

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About Author

Adriaan Van Horenbeek

Consultant Analytics

Adriaan holds master degrees in electromechanical engineering (2008) and industrial management (2009) and received a PhD (2013) in mechanical engineering at the University of Leuven for research on predictive maintenance in cooperation with several industrial companies like Bekaert and Atlas Copco. He worked two years as an industry asset management consultant for Stork where he performed reliability engineering projects at for example Umicore, BASF and VPK. Today, at SAS he holds the position of pre-sales manufacturing expert and generates value through analytics within the process and manufacturing industry. His background of engineering skills, management skills and data analytics skills makes him an ideal partner to his clients to develop and embed analytics within their manufacturing processes.

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