4 Steps to AI-Ready Data

Those in the know have long been aware of the potential of AI technologies. However, it wasn't until the public unveiling of generative AI at the close of 2022, namely the launch of ChatGPT, that the business world realized the technology's transformative power. A recent survey found that 55% of respondents have already adopted AI technologies.

Today, AI will give your organization a competitive edge, but only if you can secure the first-mover advantage. For that, you need data, the fuel that drives the complex algorithms that support AI-driven decision-making.

However, if that data isn't highly governed and curated, it won't be suitable for AI modeling, and you'll quickly lose your head start. This blog will explain the four steps to make your data AI-ready.

Related Post : Data Governance Tools: Capabilities To Look For

What is AI readiness?

The concept of AI readiness is wide-reaching. It covers company culture, budget, infrastructure, and resources, but ultimately it comes down to one question: Is your company prepared to leverage AI technologies?

Regarding data, AI readiness is about ensuring your data is organized to make it easy for data scientists to utilize it for AI modeling. Most organizations don’t have data scientists on the company payroll. Instead, data or IT leaders will hire dedicated data scientists to work on specific AI projects.

As well as the direct expense of bringing in data scientists, the longer it takes them to interpret and organize the data, the more it will cost. Beyond this, longer projects encourage the likelihood of your competitors overtaking your AI efforts.  

1. Creating a data catalog

In most companies, data isn't centralized. It's found in various repositories, such as data warehouses, and spread across a complex ecosystem, spanning multiple departments, users, and locations.

However, when developing AI models, data scientists must be able to access the right data fast to expedite the build process.

Just as a chef with an organized pantry can create a stand-out dish quickly, knowing exactly what ingredients they have at their disposal, so can data scientists create AI innovations when they have an organized data set. When data is dispersed, data scientists need help finding and understanding the data they have at their disposal.

It's more than simply locating data assets. It's about understanding these assets in context. When this information isn’t provided, there is no other option but to manually research the data, which in practice means a lot of back and forth with business teams to determine the context.

The only way to avoid this expensive delay is to organize the data correctly and provide context. And the best way to do this is with a data catalog. Using a data governance tool, like OvalEdge, with a built-in data catalog, you can automatically crawl all of your metadata and provide a centralized repository for data scientists to quickly identify the data they need to access to complete their work.

Yet, crawling is just the first step. Next, you need to curate the data to provide context. A chef's pantry would make little sense if no ingredients were labeled, nor would a data catalog.

Related Post: How to Build a Data Catalog 

2. Classifying and curating your data

The curation process is the most important step in ensuring your data is AI-ready. This is about organizing your data to make it easy to find and utilize. There are several outcomes from classifying and curating data. They include the following:

• Providing contextual business information
• Enabling users to identify and prioritize data

An AI-enabled and process-driven tool like OvalEdge enables you to complete the process much faster. The alternative, manual curation, is incredibly time-consuming when considering that there could be millions of different data attributes and hundreds of users in your ecosystem.

By carefully curating and classifying data in your organization, you will provide information about the business data owner, what the data is used for, when it's been used, and the type of business information it contains. This level of business detail goes a long way to streamlining the AI modeling process because, as well as explaining exactly what data is, it explains where the data is, and what it means to the business.

That’s why enabling business users to curate the data via business processes is so important. When you rely solely on AI-driven tools and technical user-driven curation, you will only retrieve technical information, making it difficult for data scientists to understand the business context.  

3. Ensuring compliance

Operating within regulatory frameworks on a regional basis is tricky. Trying to do so on a global scale can seem nearly impossible when you don’t have the correct methodologies or tools in place to support you. Yet, fail to remain compliant, and the penalties can be severe. For example, if you develop an AI model for use in the US and attempt to deploy the same model in Europe, you may find your company owing millions of dollars in fines for flouting data privacy laws specific to the region.

Data curation doesn't just help data scientists find and utilize the correct data assets quickly; it enables them to identify sensitive, confidential, and PII information across sectors such as healthcare and banking. Maintaining regulatory compliance is a critical outcome of data governance, and when constructing AI models, it is vital.

AI applications rely upon and leverage huge amounts of data. To create personalized customer experiences, much of this data will be customer-centric. However, data scientists must be able to determine that the data they use upholds compliance regulations, of which there are many.

Regarding AI applications, particularly those destined for broader markets, the potential for violating privacy compliance regulations by disseminating PII or other sensitive data is enormous. That's why careful curation is so important in AI readiness.

Related Whitepaper: How to Ensure Data Privacy Compliance with OvalEdge

4. Data quality improvement

Data quality improvement is a cornerstone of data governance. And when developing AI models, using high-quality data is essential. However, it isn't the most important step to AI readiness, at least not in the short term.

Just as a qualified chef can quickly determine the quality of ingredients at first glance, so too can data scientists when it comes to judging data quality. Providing AI models with high-quality data sets is important. However, this comes down to selecting quality data assets and undertaking cleaning exercises to improve lower-quality data in the short term.

The first steps must include cataloging and curation so data scientists can understand the data they are working with. The most important factor is time to market. Ongoing data quality is a long-term benefit.

The trouble is data quality improvement is a long-term objective. It requires changes to business processes, policies, and culture. If that were a prerequisite for AI innovation, there's no way a project would get off the ground in time to gain a competitive advantage.  

In an ideal world, a chef would have a pantry stocked with the finest ingredients ready to use to create the best dishes on demand. However, it takes time to source the right ingredients from the best producers.

Instead, they may set out to whip up a tasty salad but find that many of the tomatoes are bruised or damaged. The job then would be to cut and shape them so they looked presentable on the plate. It takes time to do this; the result could be better, but fail, and your customer will go elsewhere. Over time, that chef will find the best tomato producer in the area and arrange delivery of flawless fruits.

The same thing goes for building AI models. Over time, the data used to inform these models will be expertly curated and of high quality in every instance, but it is a challenging job. In the short term, data scientists must rely on training to spot the best quality data available to complete the initial development process.

Conclusion

Commercial large language models (LLMs), like OpenAI, are a commodity fuelled by generic data. While originally, these models will have been trained on exceptionally high-quality data, over time, this quality has degraded as the models have relied on user-generated internet data for training.

That's why they must be enhanced with proprietary data, even using a commercially available LLM as a starting point. The main objective is to develop something that improves the customer experience. Many companies are uniquely positioned to do so with AI because of their proprietary data. That is a competitive advantage. However, if you fail to act on it, a rival company soon will.