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Make your data AI-ready

AI can be a gamechanger, but do you know how to implement it effectively? Pursuing a new technology strategy requires time, effort, and resources, so make sure you’re investing in the right places. Get it right and you can strengthen your operations with enhanced automation and insights; get it wrong and you risk bogging down your team with obsolete technology. If you’re still figuring out your AI journey, then you’re not alone. A 2023 survey of IT decision-makers found that 60% of respondents were either new to AI or still piloting AI initiatives. This means it’s not too late to be a pioneer in AI, but you need to act quickly – and intentionally.

In these early stages, a thoughtful plan can make or break the entire program. AI solutions require accurate, organized data to deliver results and so it’s critical to prepare your data before you launch new programs. Many organizations today are working with hybrid cloud systems that collect and store data in a range of places, including both on-premise servers and the cloud. These disparate locations aren’t always easily accessible to data architects, which can make it impossible to clean and organize the data in the way you need.

To achieve success with AI, you must take the necessary steps to create a unified data foundation that is designed specifically for AI programs. With the right tools and strategic approach, you can streamline the data pipeline so that every AI solution is fueled with high quality data that pulls automatically from the full ecosystem.

Get your data in working order

When working with AI, you must make your data clean, organized and accessible. To get meaningful results, you need to work with quality ingredients. The challenge is that, as artificial intelligence is a newer tool, most organizations don’t process their data with AI in mind. Even data that is used for other business purposes may not meet stringent AI standards, which is why organizations need to review their data handling practices accordingly. Take the time to audit your data and figure out what you can strengthen or change.

Look out for these three common ways that companies mismanage their data. One key area is governance. Adhering to governance policies ensures that organizations only use data in appropriate and secure ways. If a company is new to AI, then internal governance protocol will likely not cover the various new use cases. This can cause issues down the line: If you fall out of compliance, you’re vulnerable to financial penalties and even legal action. Updating your policies to cover AI will not only protect your bottom line, but also your customers and your brand reputation.

Then there are issues with organization and accessibility. If you work across multiple clouds or in a hybrid architecture, then your data will likely be scattered between these different locations. How can data architects find and access the material they need in this environment? Your data may also be stored in different formats that are hard to standardize, keeping workers from obtaining a clear view and organizing the data efficiently. This disorganization can exclude information from the data pipeline, leading to inaccurate or impeded AI solutions. A company can have all the material it needs for advanced AI problem-solving, but it will be useless if it’s not handled properly.

Unify your data with a data fabric

The good news is that with the right vendor support, you can get your data up to code and start fueling AI initiatives that deliver results.

Step one is to design new data governance policies that vet your data for AI applications. Consider all possible use cases to ensure legal compliance, but also consider what you don’t need. Make sure that you’re only storing and organizing data that can be used for its intended purpose. Not only will this free up valuable data storage, but it will ensure that you aren’t caught accidentally holding onto sensitive data. By beginning with this step, you ensure that all future strategy is designed with compliance in mind.

Next, enlist the right support. Data throughout your hybrid environment needs to be assembled into a unified data fabric, which can be overwhelming to do without the right partner ecosystem. Partnering with a global vendor to introduce simplicity and visibility into your data management is often the most effective approach. You can leverage their expertise and technical frameworks without draining internal developer resources. Look for a single access data platform that can support you today and tomorrow, scaling with you as your data grows.

Finally, work with your solutions partner to establish a singular data fabric so you can locate, manage, and use data with ease. By consolidating all your data points into a central, flexible platform, you can feed the data pipeline with every relevant data point – and nothing else. This will set you up for AI success, wherever you choose to implement it in your operations.

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