Data infrastructure: generating revenue for AI investments

AI Magazine explores the importance of data infrastructure and other strategies as businesses look to make the most out of their AI and ML investments

Undoubtedly the era of big data has helped enterprises enormously as it has created a wealth of data for companies to analyse and generate actionable insights. 

However, when formulating a robust artificial intelligence (AI) and machine learning (ML) programme, a robust data infrastructure is needed before the data becomes accessible and valuable.

Here, we look at the different steps that companies need to take to ensure they are generating as much revenue from their AI investments as possible.

Overcoming challenges with data management

If companies ignore their data infrastructure and jump to data science, their data scientists will be needed to categorise, validate and prepare data, rather than searching for insights. Ultimately it will waste their time and distract data scientists from utilising their most valuable skills which add the most value to the company.

It is important that automated processes for integrating are created to give engineers time back and ensure companies have all the data they need for accurate ML. Not only does this help to cut costs, but it also maximises efficiency as companies build their data science capabilities.

Narrowing down data for consistent results

Although large amounts of data can be beneficial as companies look to improve and streamline operations, it is also important that the data store is comprehensive. If there are gaps in the data, or it isn’t formatted properly, ML can fail to function or give inaccurate results.

If data science teams are tasked with manually labelling the data as part of supervised machine learning, it could bring additional risk to the project as it is a time-intensive process. On the flip side, training examples could be trimmed too far with this process, therefore, narrowing the scope. In doing so, the ML model would only tell the company what it already knows.

To avoid these issues, companies must ensure the team can draw from a comprehensive, central store of data that encompasses a wide variety of sources and provides a shared understanding of the data. 

In doing so, companies will improve the ROI from the ML models by providing more consistent data to work with. It is crucial as a data science programme can only improve if it has the foundation of reliable, consistent data. 

Preparing the workforce for AI projects

It is equally important that employees are prepared for the shift their companies will go through to become more data-driven and insight-focused. To prepare employees, companies need to address their culture, make it more data-driven and then encourage employees to take a more central role in the data culture.

Staff should be trained in data as AI projects will fail if the company’s employees lack data expertise. This is crucial and AI/ML systems will not be able to reach their full potential if the employees operating them do not apply data-driven decision-making methods.

Additionally, lack of change management can be a big barrier as AI requires significant organisational changes, including in strategy and mindsets. Change management therefore should be an integral part of an AI implementation plan and leaders should be equipped with the knowledge and drive necessary to create an AI-centric culture.

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