Is Your Data Ready for AI?

Home » Resources » Is Your Data Ready for AI?

Artificial intelligence (AI) is like a genie in a bottle, granting wishes and making our lives easier. But like any genie, it needs the right tools to work its magic. That’s where data readiness comes in.

Is your data ready for AI?

Here are a few key questions to guide you in answering that question:

Is your data clean?

Data quality is a measure of how precise, comprehensive, coherent, and pertinent your data is for your AI objectives. Inadequate data quality can result in erroneous insights, prejudiced models, and undependable outcomes.

Labeling your data is also a crucial step. Data labeling involves adding annotations or tags to your data to make it comprehensible and usable for AI algorithms. Data labeling can help you train and evaluate your AI models, enhance your data features, and optimize your data performance.

Is your data integrated?

Data integration is the process of merging data from various sources and formats into a centralized repository or environment. Data integration can enable you to access and analyze more data, enhance your data with additional attributes, and eliminate data silos and inconsistencies. There are several ways to approach solving this challenge. Ultimately each organization will have to assess which enterprise data architecture best fits its needs – from the now classic data warehouse or lakehouse, to the recently trendy data mesh and data fabric.

Do you have enough data?

Maybe not. But there are several solutions.

Data augmentation is a technique that artificially increases the size of the training set by creating modified copies of an existing dataset. This can be achieved by making minor changes to the dataset or by using deep learning to generate new data points.

Unstructured data, which is often undocumented, can contain valuable information about a company’s products and processes. By transforming unstructured data into structured or semi-structured data and linking it with other structured data, companies can expand their datasets and gain new insights.

In addition to internal data, external data can also be a valuable resource for companies. By linking internal data with external data generated, gathered, and shared by reputable third party sources, companies can enrich their existing insights, fine-tune their operations, and unlock further growth. The right data signals can have a tangible impact on data analytics and machine learning model performance.

Where is your data stored?

Storing and making data discoverable and accessible is a crucial aspect of data management. Businesses are already reluctant to dispose of data, even though in many cases a significant portion of that data is never accessed or leveraged. The promise of AI will only further strain the capabilities of data storage solutions (and only increase our spend on these solutions). This is why it is critical to nail down a data storage strategy that enables users to easily access and work with both active and inactive data, while also controlling costs.

How about data privacy & governance?

Data privacy, security and governance are as critical as they’ve ever been. One of the promises of generative AI is to empower the end user to make lightning quick use of the data at their disposal. Connecting all employees with unfettered access to data across all depths of your organization however is not advisable (ie. trade secrets, social security numbers, etc..). Today, a rogue employee may have to go hunting for such information – however a generative AI assistant can do that hunting for them with ease. Before unleashing AI it is well worth a second look to ensure that your data is secure, encrypted where it should be, and that sensitive information is only accessible to the appropriate parties.


Creating a data strategy is crucial to prepare your organization and its data for AI. A data strategy defines how your organization will collect, store, manage, and utilize data to achieve its objectives. It encompasses a well-defined plan for data governance, quality, security, and privacy. The strategy should be aligned with the organization’s overall business strategy and be adaptable to changing business needs and technology.

This may sound like a lot to get your arms around, but luckily, I know a guy who can help.