How to prepare your company’s data for AI

What is data readiness?

Data readiness means your company’s data is organized and accessible enough to support your business goals. To assess your company’s readiness, consider questions like:

• What data do you have?
• Where is it stored?
• Is it complete and consistent?
• Can your systems access and use it?
• Do employees follow consistent processes when they enter data?

Many organizations struggle because their data has accumulated over time across multiple software systems, spreadsheets and databases. Some of the information may be incomplete, duplicated or stored in incompatible formats.

“Just because you have a lot of data doesn’t mean it’s the right data,” says Ma. “Sometimes one missing element can dramatically reduce how useful the data is for a particular AI application.”

For example, a logistics company may track the locations of vehicles or shipments, but if data about time is missing or inconsistent, the information won’t be that useful for route optimization or predictive analytics.

The four pillars of AI-ready data

Although every company is different, preparing data for AI typically involves work in four core areas.

1. Data inventory

The first step is understanding what data exists across your organization.

Depending on your company’s size, you may have information spread across different software, spreadsheets and cloud systems. Some data may also come from manual employee input or automated system logs.

Before you try to implement AI, assess:

• where the relevant data resides
• how it moves through the organization
• how different datasets relate to one another
• which information is relevant to the business problem you’re trying to solve

“The starting point is always the business objective,” says Ma. “You need to understand what problem you’re trying to solve first.”

2. Data quality

Everyone has heard the expression “garbage in, garbage out”—it predates AI, but it’s still relevant. Missing, inconsistent or poorly structured information can reduce accuracy and reliability.

A common problem is inconsistent data entry. For example, employees may enter information in different formats or use free-form text fields rather than dropdown lists, making data hard to standardize or analyze later.

“If there are no governance guidelines or standard operating procedures, you can usually anticipate that the data will need clean-up work,” says Ma.

AI tools can help identify patterns, inconsistencies and data-quality problems automatically. This means the process of improving data quality and adopting AI can often happen in parallel, rather than sequentially.

3. Data access and integration

AI systems often need to pull information from multiple sources. That becomes difficult when the systems cannot communicate with one another.

In addition, many organizations still rely on older software that were not designed for modern AI applications or data-sharing. The data may belong to the company, but still be difficult to extract or use.

The accessibility problem isn't always caused by outside vendors, says Ma. “Sometimes companies unintentionally ‘lock’ their own data: It’s in there, but the formats are legacy, or don't match other datasets. Or extracting the data in a straightforward, practical manner is difficult without changing systems.”

In that case, you may need to do one or more of the following:

• Connect your systems using integrations or APIs (application programming interfaces), which allow different software systems to exchange data.
• Centralize the data in a shared environment, such as a data warehouse or data lake.
• Modernize portions of your technology stack (the software, systems and infrastructure you use to run your company’s operations).

You don’t necessarily have to rebuild everything from scratch. It may be enough to start by improving access to the most important data you need for a specific AI use case.

4. Data governance

Data governance is an often overlooked aspect of AI readiness.

Data governance includes:

• defining who owns the data
• establishing standards for how information is entered and maintained
• creating rules around privacy, security, and potential compliance
• ensuring employees follow consistent processes when they work with data

According to Ma, establishing an effective governance and organizational culture is often more challenging than cleaning up the data.

“It may take some hard work, but you can usually clean up the data,” he says. “The harder part is changing the company culture—putting governance and standard operating procedures in place and getting people to follow them going forward.”

Some organizations are starting to treat data more like software. The idea is that data require ownership, version control, documentation and ongoing maintenance, and should not be considered an afterthought.

Why some companies struggle

Many businesses have years of fragmented or inconsistent data that is difficult to use effectively.

For example, you might ask employees for insights or reports only to discover that answering simple business questions takes days because the required data is scattered across multiple systems or requires extensive manual clean-up.

“Until there’s a pain point that affects the business, many companies don’t prioritize data quality,” says Ma.

This tends to become more problematic as companies scale AI initiatives beyond small pilot projects. For example, AI systems may struggle if:

• data formats vary widely across departments
• employees enter information inconsistently
• critical fields are missing
• systems cannot exchange information easily
• processes for collecting data are poorly defined 

In some cases, companies may need to redesign workflows or software interfaces to improve how they capture data at the source. For example, using dropdown menus or other standardized inputs to improve consistency.

When to bring in outside help

Some companies can clean up and prepare their own data. Others may need outside expertise—it depends on employees’ skills and comfort levels with the work. Signs that you may need external help include:

• waiting days or weeks for business insights
• AI or analytics projects stalling repeatedly
• teams struggling to integrate systems
• lack of internal expertise in data engineering or governance
• insufficient capacity to manage large-scale clean-up and modernization efforts 

An external partner can help you avoid costly detours if you’re trying to build more advanced AI systems.

“There are many potential pitfalls and obstacles,” says Ma. “If you don't have the culture or the know-how, or you’re trying to move quickly, that's when you should seek an expert.”

Ultimately, preparing for AI is not just a technical exercise. It can require changes to systems, workflows, governance and organizational culture. The good news is that you don’t need to solve every data problem at once.

If you start with clear objectives, realistic expectations and strong data practices, you will be well-positioned to benefit from AI over the long term.

Next step

Adopt AI, digital tools or smart equipment and get preferential-rate consulting and financing with our Lead with Innovation and Focus on Technology (LIFT) program.

Want to learn more about getting your business ready for AI? Download our free guide: Get Your Business AI-Ready.