Do you want to succeed in the months and years to come? The number one way to do this is through people – building businesses through their creativity, passion and full participation in decision-making.

But right behind empowered people is the second vital ingredient for success: data. Data that can tell you what your customers want, how your business operates and what awaits you. Now we have the key that unlocks patterns that have long been hidden in databases and applications. The question is: are we paying enough to care for and nurture this data?

“Some may think this is a magic line of code that all of a sudden speeds up the process dramatically,” says Moses Guttmann, CEO and co-founder of ClearML. “But in reality, AI requires meaningful data to drive noticeable improvements and drive business innovation.”

It turns out that data can even be a finite resource. A study from the University of Aston predicts that we are quickly running out of storage space for all the data generated. Plus, there’s even the specter of running out of general workout data, as MIT Technology Review’s Tammy Xu recently reported.

But let’s keep things at the enterprise level for now, where the lack of data is already proving to be the main obstacle to AI. Succeeding with AI requires “availability and access to data; and understand how to apply that data to specific use cases to improve business outcomes,” said Umesh Sachdev, co-founder and CEO of Uniphore.

Successful AI “requires data diversity,” says IDC analyst Ritu Jyoti in an early 2022 report. “Similarly, the full transformative impact of AI can be achieved using a wide range of types of data. Adding layers of data can improve the accuracy of models and the potential impact of applications. For example, a consumer’s basic demographics provide a rough snapshot of that person. If you add more than background like marital status, education, employment, income, and preferences like music and food choices, a fuller picture begins to form.With additional information about recent purchases, current location and other life events, the portrait really comes to life.

To enable AI to evolve and proliferate in the business, “stakeholders must ensure a robust database that enables the full cycle of data management, adopt advanced analytical methods to realize the untapped value of data “, says Shub Bhowmick, co-founder and CEO. of Tredence.

“In terms of data availability and access, companies need a way to analyze huge amounts of data and highlight what is relevant for a particular application,” says Sachdev. “Is the data easily contained and categorized? Is there enough relevant data to form a meaningful assessment? Consider virtual learning – do educators have enough relevant data about student interactions to make meaningful adjustments to how classroom content is taught? »

A quality dataset “is crucial to supporting successful AI, because models are only as good as the data they contain,” says Guttmann. “This idea of ​​data quality is an important part of having a solution that delivers consistent results, and it also needs to be understood before adoption. Too few decision makers understand that AI is a never-ending process and that as data changes, AI must embrace those changes in tandem.

For most businesses today, “it is difficult to harness the immense value present in the data they generate daily,” says Bhowmick. “Therefore, embedding sufficient business context and change management practices is key to achieving the right interaction between scale and innovation. Businesses can have a tangible and measurable impact on their bottom line by using the right data models to operationalize their AI investments. Building AI-powered connected intelligence has never been so consistent, from demand forecasting and stock alerts to IoT-powered remote monitoring for patients. This is just one of the many ways companies are realizing the benefits of investing in AI – connecting insights to action and value.

IDC’s Jyote makes the following recommendations to strengthen the AI-critical data backbone:

  • Enable data from internal and external sources. “Machine learning models need the most relevant data, which isn’t always inside the organization,” Jyote points out. “Internal data only allows companies to see their own operations or information about their customers. This does not give the complete picture. Businesses need access to secure data sharing. Create a workflow to integrate third-party and/or new data sources into the organization, including testing, purchasing, and seamless integration with existing datasets and internal processes.
  • Bring data expertise. “Build a talent pool of technical and industry domain experts such as data engineers, data scientists, and machine learning engineers.”
  • Develop a data strategy. “Gain employee buy-in and trust for data strategy with inclusiveness and transparency,” advises Jyote. Adopt an intelligent data grid that automates and enforces universal data and usage policies across multicloud ecosystems. The Grid is also expected to “automate how data is discovered, cataloged, and enriched for users,” as well as “automate how to access, update, and unify data spread across distributed data and landscapes.” cloud without the need to move or replicate data. .”

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