You want success over the coming months and years? The number-one way to get there is through people — building businesses through their creativity, passion, and full participation in decision-making.
But right behind empowered people is the number-two vital ingredient for success: data. Data that can reveal to you what your customers want, how your business is running, and what’s around the corner. Now, we have the key that unlocks the patterns that have long been hidden away in databases and applications. The question is: are we paying enough to the care and feeding of this data?
“Some may think it’s a magical line of code that all of a sudden makes a process much faster,” says Moses Guttmann, CEO and co-founder of ClearML. “But in reality, AI requires meaningful data to make noticeable improvements and drive commercial innovation.”
It turns out that data may even be a finite resource. One study out of Aston University predicts that we’re quickly running out of storage space for all the data being generated. Plus, there’s even the specter of running out of general training data, as recently reported by MIT Technology Review’s Tammy Xu.
But let’s keep things at the enterprise level for now, where lack of data is already proving to be the most vexing roadblock to AI. Succeeding with AI requires “availability and access to data; and understanding how to apply that data to specific use cases to improve business outcomes,” says Umesh Sachdev, co-founder and CEO at Uniphore.
Successful AI “requires data diversity,’ says IDC analyst Ritu Jyoti in a report from earlier in 2022. “Similarly, the full transformative impact of AI can be realized by using a wide range of data types. Adding layers of data can improve accuracy of models and the eventual impact of applications. For example, a consumer’s basic demographic data provides a rough sketch of that person. If you add more context such as marital status, education, employment, income, and preferences like music and food choices, a more complete picture starts to form. With additional insights from recent purchases, current location, and other life events, the portrait really comes to life.”
To enable AI to scale and proliferate across the enterprise, “stakeholders must ensure a solid data foundation that enables the full cycle of data management, embrace 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, businesses need a way to parse through huge tracts of data and surface what’s 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 from student interactions to make meaningful adjustments to how classroom content is taught?”
A quality dataset “is crucial to supporting successful AI, as models are only as good as the data put into them,” says Guttmann. “This idea of data quality is an important part of having a solution that delivers consistent results, and this also needs to be understood ahead of adoption. Not enough decision makers understand that AI is a never-ending process and also that as the data changes, the AI needs to adopt those changes in tandem.”
For most businesses today, “it is a struggle to tap the immense value present in the data they generate daily,” says Bhowmick. “Hence integrating sufficient business context and change management practices is critical to get the interplay between scale and innovation right. Businesses can have a tangible, measurable impact on their bottom line by using the right data models to operationalize their AI investments. Building an AI-led connected intelligence has never been more cohesive, from demand forecasting and stock alerts to IoT-based remote monitoring for patients. That’s just one of the many ways businesses realize AI investments’ benefits — by connecting insights to action and value.”
IDC’s Jyote makes the following recommendations to strengthen the data backbone essential for AI:
- Enable data from both internal and external sources. “Machine learning models need the most relevant data, which may not always be inside the organization,” Jyote points out. “Internal data only allows companies to see their own operations or customer information. That doesn’t provide a complete picture. Companies need access to secure data sharing. Create a workflow for bringing in third-party and or net-new data sources into the organization, including testing, buying, and seamless integration with existing internal data sets and processes.”
- Bring in data expertise. “Build a talent pool of industry domain and technical experts like data engineers, data scientists, and machine learning engineers.”
- Develop a data strategy. “Get employee buy-in and trust for the data strategy with inclusivity and transparency,” Jyote advises. “Embrace an intelligent data grid that helps automate and enforce universal data and usage policies across multicloud ecosystems. The grid also should “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 cloud landscapes without the need of doing any data movement or replication.”