gradient
v15 logo
Embark on your AI
data quality journey
Join the v15 launch webinar
March 5, 2024

How AI Models and Data Products Benefit from Data Quality and Lineage Tools

How AI Models and Data Products Benefit from Data Quality and Lineage Tools Cover Image

By Catherine Yoshida
Catherine is a data governance specialist and data architect at Teranet. She is thrilled to share her professional insights on data governance, data management, and what it means to be a leader in data.


As a data governance specialist and data architect at Teranet, I have witnessed data's importance and its benefits for an organization. In today's tumultuous business climate, AI is a perfect example of companies with mature and adept data programs flourishing while others are left playing catch up.

When I started at Teranet, we still needed to set up a data governance program, create data ownership structures, and implement the necessary data management tools. I'm proud to say that we've come a long way since then, and it has had a lasting impact on our other capabilities related to data (i.e., building and implementing AI models).

We have several AI projects in the works and currently running at our organization, from land registry to real estate. I can safely say we have our data quality, lineage, and governance models to thank for their successful outcomes. I want to share some of that information with you today so you can go on to implement your own AI models successfully.

The "Garbage In, Garbage Out" Dilemma

As everyone familiar with data quality is probably aware, data must uphold certain standards to perform effectively. Data can be incorrectly formatted, unverified, or subject to data drift, rendering it inaccurate over time. Not only can this mess with your analytics, but it can also harm most AI models.

Ultimately, AI models are only as good as the data they are built on. You need precise and reliable input data to deliver precise and reliable results. Hence the term "garbage in, garbage out." Therefore, companies focusing on improving their data quality and the health of their data systems have a better chance of inputting the best data and getting the best results.

How to Enhance AI with Data Management

Now that we understand the importance of high-quality data, you will need to take measures to improve your data and make it reach those standards. At Teranet, we selected our data management software about seven months after I joined. We wanted a tool that could address the main challenges of our data users: data discovery, classification, and understanding the data handling policies.

Once we chose Ataccama, our company began experiencing some immediate benefits. We saw a massive decrease in the time spent searching for data. Our overall data quality grew significantly, and we could even streamline important processes like creating data products.

An adept tool like Ataccama can make the difference between a data-mature organization and still working toward becoming one. Organizations must go through the various phases of data maturity, progressing from infancy to development and eventually to a final stage where your data is working for you, and you're not working for it (or spending all your time preparing it). Once we reached this stage of maturity, I saw a significant jump in the success of our AI and all data-related projects.

Importance of Data Lineage

To be more specific about capabilities that set your data up for success, I have to highlight data lineage. Understanding where data comes from where it goes, and providing a comprehensive view of that journey is key to ensuring the best data makes it into your models.

Emphasizing data lineage allowed us to keep bad data and data drift out of our models, but it also helped with vital tasks like compliance. Together with having clear tags and defined parameters for sensitive data, data lineage can help us keep track of sensitive data to ensure it's used properly and in the right hands. It's essential for supporting any data governance, quality, or management initiative.

Why Business Users Should Care

By nature, business users may wonder where they fit into this equation. They will be one of the primary beneficiaries of gen AI and its value. Having high-quality data and adopting advanced data management software isn't just a gift for your IT department. Even if you're not dealing with the data on such a technical level, here are some advantages it can present.

  • Data literacy and education: An intuitive data management tool can help business users interact with data on an easy-to-understand level, improving overall data literacy.
  • Policy Management and Compliance: Having company data policies clearly outlined and integrated into the tool can educate them on how to properly handle data, understand its classification, and know what's sensitive and what isn't.
  • Support business initiatives: Data management tools can ease the onset of important business initiatives such as mergers and acquisitions, data integration, and streamlining decision-making.
  • Data products: Everyone loves data products because they are pre-packaged solutions that are easily consumed. Data management tools can help create data products faster, shortening the time to market and facilitating efficient research by data teams.
  • New Gen AI capabilities: With the onset of Gen AI, new capabilities are coming to data management platforms, such as natural language DQ rules creation and automated assistants. This will make these platforms even more accessible to non-technical users and help drive the adoption of the data management platform company-wide.

Roadmap to Data Maturity

I've already mentioned the importance of data maturity, but let me outline the steps we took as an organization that got us there.

  1. Assess and understand. Assess the current state of data and capabilities at the organization.
  2. Invest in data management tools. Invest in tools like Ataccama to give you the ability to discover, understand, and improve your data.
  3. Implement policies and compliance. Establish and enforce data policies that adhere to your business and industry's standards. Create data governance practices that define clear ownership of data, roles and responsibilities, and processes for data stewardship.
  4. Data literacy and training. Offer training programs to educate EVERYONE in the organization about company policies and procedures regarding data.
  5. Integrate with business processes. Integrate data management processes with broader business initiatives, ensuring data considerations are embedded in decision-making processes.
  6. Continue Improving. Treat data management as a continuous process. New features and technology are constantly emerging. It's your job as a data user to keep your company up to date.

Conclusion

My journey as a data governance specialist and data architect at Teranet has highlighted the pivotal role of data in organizational success. Whether with AI models or data lineage, understanding the importance of data quality (and having the right tools to address it) will result in the best possible outcomes.

I've already mentioned how much Ataccama has helped our company reach our data goals. Want to find out more about how they can help you? Check out their platform page.

Related articles

Arrow Right
Arrow Left
Blog
The Importance of Data Quality for AI

The Importance of Data Quality for AI

Learn why data quality is key to successful AI implementation.

Read more
Blog
The Rise of GPT

The Rise of GPT

Learn about what GPT and generative AI are and how they became so popular.

Read more
Ebook
Why Our Company Needs Data Management

Why Our Company Needs Data Management

Learn how to advocate data management tools at your company with a series of…

Read more