5 Data Monetization Tools That Help AI Initiatives

Successful AI programs rely on capabilities that take time to establish. This can make creating AI programs a daunting task.

According to researchers from MIT Center for Information Systems Research. When companies identify and accumulate the expertise and practices of their AI teams, they can create reusable and tweakable practices and build their capabilities. This accelerates new AI projects and prepares future teams for success.

Specifically, companies are better equipped for AI programs when they have advanced enterprise data monetization skills in five areas: data science, data management, data platforms, understanding customers and the acceptable use of data. new research briefing by Barbara Wixom, Ida Someh and Cynthia Beath.

The five skills are interdependent, the researchers found, and companies need to develop similar levels of maturity across the five areas to be able to drive returns from data.

In particular, researchers looked at how Microsoft leveraged its data capabilities to create a new AI program in 2015. The company’s Real Estate and Facilities group, which was responsible for 190,000 people working in more than 600 buildings, took advantage of its data capabilities. to build AI programs that reduced building facility costs. The programs would then be deployed within Microsoft and offered to external customers.

Here’s what the five skills looked like at Microsoft:

Data Science

Researchers have found that businesses need advanced data science capabilities—the people, processes, and technology needed to create, train, deploy, and manage machine learning models. At Microsoft, a team of about 20 data scientists helped the Real Estate and Facilities group identify problems that AI could solve, such as using machine learning techniques to understand how space was used and find cost reduction opportunities. Data scientists have developed a model that can be reused for things like optimizing buildings and parking lots.

Data management

To develop AI models, companies must be able to organize accurate, complete, large, dynamic and well-understood datasets. The Microsoft project team identified the data needed to train machine learning models, and acquired, cleaned, and validated the data. Data scientists also integrated facility data with external data, such as weather information.

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Data platforms

Businesses need a scalable, on-demand platform like a data lake to serve as a centralized repository. At Microsoft, the team created a data lake to store and analyze data and encourage internal data sharing. When the company finally created customer services based on its AI models, it worked to provide data lake access to external users.

Customer understanding

Companies need to involve internal and external customers when training and developing AI models and eventually creating products. The Microsoft team discussed the findings with key stakeholders such as facility managers and project sponsors to get their perspective. When it was discovered that the AI ​​programs had potential business application, they worked with customers and Microsoft’s consulting team to develop this product.

Acceptable use of data

Businesses should establish data protection practices – ideally, frictionless oversight that minimizes decision risk, bias, and unintended consequences. And when data is used for business purposes, data protection practices must evolve. For example, Microsoft realized that combining data in new ways created the potential to reverse engineer individual identities, so the team made sure to establish privacy and security practices.

Read the brief

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