The age of artificial intelligence is here. Many organisations leverage AI for planning and reporting functions such as business intelligence. Yet those examples often happened in the background, invisible to the average user, and AI grew stealthily. Then ChatGPT arrived and changed everything.
“ChatGPT and similar software bring something very big to the table,” says Wade Calenborne, Chief Operations Officer at Sithabile Technology Services. “Now we can talk to software as if it was a person. That one step collapses the barriers of entry for AI. The generative part is also important because AIs can respond in plain language and answer people’s questions in ways we understand.”
Data plays a central role in all AI to train their models and generate helpful responses from company data. But your data needs to be in good shape to take advantage of AI.
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“AI might seem very smart, but that all falls apart when you give it bad data, often poorly-formatted data. There is also the risk of giving the wrong data to the wrong service. You can’t just pass some spreadsheets and databases to an AI. It requires more structure than that,” says Calenborne.
How to prepare your data for AI
There are many different AI models and ways to train them. Thus there is no primary rule that says what AI-ready data looks like. The bigger challenge is whether the data owners know what is happening with their information.
To use AI to full advantage requires greater visibility and control over data. Organisations should prepare their data and get their information priorities in order. Several steps can help with this process:
Establish an AI team: One doesn’t necessarily need a group of crack data scientists. But establish a multidisciplinary team that looks at AI from the business perspective. The team should represent business leaders, platform owners, data owners, technology professionals, and human resources. Their job is to evaluate the organisation’s AI priorities and opportunities, and its process, policy and infrastructure shortcomings.
Identify prime use cases: Find particular problems that AI can alleviate. Such problems can include inventory management, sales forecasting, mundane process automation, reducing support tickets, or filtering alerts. The multidisciplinary team should invite and explore these options, then help select the easiest and simplest ones first. Determining use cases will help to select the best AI services for that job and that service’s data requirements.
Classify data: Not all data is the same. An organisation must decide what data it wants to use for AI. This journey starts by understanding what data it has and where it resides. Doing so has several benefits. It will create storage cost savings, reduce risks such as data leakage and successful cyber breaches, and help support the best use cases for AI. Data classification can take many forms, but consider starting with a discovery exercise in collaboration with a data management partner.
Sort out data storage: Some data must always be available, while others can live in archives. One type of data might be for specific groups. The other works best when aggregated across business services. Data might have to stay inside your premises or be ready for remote access. The right blend of data storage saves money and helps govern access. These are crucial once you start engaging with AI.
Create usage policies: If you want to ensure your data is used safely and encourage people to use that data effectively and responsibly, you will need governance and policies. Employees could accidentally feed protected private data into a public AI service. Likewise, employees could avoid using competitive AI tools because they don’t know where the boundaries are. Data policies are essential for proactive AI adoption.
Look at skills requirements: AI-focused companies will need professionals skilled in data management and preparation. Determine what those skills are, whether you have them, can hire them, or partner with a storage management solutions provider and their professionals.
Wade Calenborne
Don’t leap into AI without a firm grasp on your data. You don’t need to fix all your data issues, but you want a stable and transparent data environment to build your AI investments.
- Advertisement -
The age of artificial intelligence is here. Many organisations leverage AI for planning and reporting functions such as business intelligence. Yet those examples often happened in the background, invisible to the average user, and AI grew stealthily. Then ChatGPT arrived and changed everything.
“ChatGPT and similar software bring something very big to the table,” says Wade Calenborne, Chief Operations Officer at Sithabile Technology Services. “Now we can talk to software as if it was a person. That one step collapses the barriers of entry for AI. The generative part is also important because AIs can respond in plain language and answer people’s questions in ways we understand.”
Data plays a central role in all AI to train their models and generate helpful responses from company data. But your data needs to be in good shape to take advantage of AI.
- Advertisement -
“AI might seem very smart, but that all falls apart when you give it bad data, often poorly-formatted data. There is also the risk of giving the wrong data to the wrong service. You can’t just pass some spreadsheets and databases to an AI. It requires more structure than that,” says Calenborne.
How to prepare your data for AI
There are many different AI models and ways to train them. Thus there is no primary rule that says what AI-ready data looks like. The bigger challenge is whether the data owners know what is happening with their information.
To use AI to full advantage requires greater visibility and control over data. Organisations should prepare their data and get their information priorities in order. Several steps can help with this process:
Establish an AI team: One doesn’t necessarily need a group of crack data scientists. But establish a multidisciplinary team that looks at AI from the business perspective. The team should represent business leaders, platform owners, data owners, technology professionals, and human resources. Their job is to evaluate the organisation’s AI priorities and opportunities, and its process, policy and infrastructure shortcomings.
Identify prime use cases: Find particular problems that AI can alleviate. Such problems can include inventory management, sales forecasting, mundane process automation, reducing support tickets, or filtering alerts. The multidisciplinary team should invite and explore these options, then help select the easiest and simplest ones first. Determining use cases will help to select the best AI services for that job and that service’s data requirements.
Classify data: Not all data is the same. An organisation must decide what data it wants to use for AI. This journey starts by understanding what data it has and where it resides. Doing so has several benefits. It will create storage cost savings, reduce risks such as data leakage and successful cyber breaches, and help support the best use cases for AI. Data classification can take many forms, but consider starting with a discovery exercise in collaboration with a data management partner.
Sort out data storage: Some data must always be available, while others can live in archives. One type of data might be for specific groups. The other works best when aggregated across business services. Data might have to stay inside your premises or be ready for remote access. The right blend of data storage saves money and helps govern access. These are crucial once you start engaging with AI.
Create usage policies: If you want to ensure your data is used safely and encourage people to use that data effectively and responsibly, you will need governance and policies. Employees could accidentally feed protected private data into a public AI service. Likewise, employees could avoid using competitive AI tools because they don’t know where the boundaries are. Data policies are essential for proactive AI adoption.
Look at skills requirements: AI-focused companies will need professionals skilled in data management and preparation. Determine what those skills are, whether you have them, can hire them, or partner with a storage management solutions provider and their professionals.
Wade Calenborne
Don’t leap into AI without a firm grasp on your data. You don’t need to fix all your data issues, but you want a stable and transparent data environment to build your AI investments.
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