Since the release of ChatGPT in late 2022, businesses the world over are scrambling to test this powerful generative AI technology, and there’s every reason to believe the trend will continue into the future.
But as magical as these tools may seem when they effortlessly spit out working code or give users a refresher on photosynthesis, the truth is, they’re akin to very complex machinery. One of the most important parts of getting the (metaphorical) gears and levers to run smoothly is high-quality, well-managed data, supported by robust data governance.
Here, we’ll go over the essential steps to build an effective AI strategy, paying particular attention to master data management (MDM). By the end, you’ll have a firmer grasp of the role that data plays in AI, why data quality matters as much as quantity, and how MDM figures into the complete picture.
What Makes AI-Ready Data So Important?
“With the right data, enterprises can better understand their customers’ needs, fine-tune their product recommendations, predict future trends and offer a far superior user experience,” says Prash Chandramohan, Sr. Director, Product Marketing. MDM & 360 Applications. “But getting this right requires a lot of good data, so companies have to do the work of making sure their data is clean and accessible.”
As Chandramohan explains, to be “AI-ready,” data needs to meet these minimum requirements:
Data Quality: Because AI models are only as good as the data they are fed, all data needs to be checked for accuracy and completeness.
Data Governance: Organizations of any size are probably dealing with staggering amounts of data, so it’s crucial to put policies and data governance frameworks in place to track data provenance (i.e., where it comes from or how it’s been changed). AI governance ensures organizations can trust the data and the output of the models that use it.
Data Integration: These days, it’s pretty common to have data flowing in from multiple different sources, which makes it all the more important to be able to automate data integration seamlessly across platforms and systems.
The challenge many organizations face in achieving AI readiness is that their data is often plagued by data quality issues, making it error-prone, inconsistent and siloed. Implementing advanced data validation techniques can help ensure that the data is accurate and ready for AI applications.
Master data management was designed to mitigate these issues. In the next section, we’ll define this important term and discuss how MDM and generative AI will shape AI strategy for proactive, forward-looking companies.
The Role of Master Data Management in AI Success
Master data management involves drawing data from diverse sources to gather information about entities important to your business — people, locations, products, etc. — and integrating them into a single “master” record that acts as a complete, consistent view of all your pertinent data.
Working with data at any scale comes with a litany of challenges like inconsistent formatting, fragmentation, and sources that don’t agree, but a high-quality MDM solution means you can put these headaches behind you. Data profiling is a crucial step in this process, ensuring that the data is accurate, consistent, and ready for integration.
For example, Holiday Inn Club Vacations implemented MDM to manage more than 350,000 member profiles. By consolidating seven different data systems into one, they reduced data-entry errors while building brand loyalty with more customer-specific personal touches. Similarly, Discount Tire turned to MDM to manage its 70 million customer records and cut duplication by 50%.
As Chandramohan says, “People often don’t realize how messy their approach to data management has become until they look back on it with the clarity offered by MDM. The amazing successes organizations have had in successfully implementing MDM not only make their data more actionable, but they ultimately bring an effective AI strategy in reach.”
Master Data Management in AI Strategy
Now that we’ve defined what master data management is and why it matters, let’s walk through the process of building an AI strategy that leverages advanced data management practices.
Steps to Build an Effective AI Strategy
1. Perform a data readiness assessment
The best place to begin is with an audit of your data quality management, paying special attention to whether it’s accurate and complete. You’ll also need to figure out what it will take to integrate your data sources, which means taking stock of how many different data sources you have and how each operates.
Whatever gaps are revealed in this process will need to be addressed, and you’ll also want to improve data accuracy with cleansing (e.g., correcting obvious misspellings) and enrichment (adding additional data, if you can).
2. Implement master data management
Next, it’s time to begin setting up your MDM solution. Identifying data owners is crucial in this process, as they are responsible for ensuring data quality and accessibility across the organization. This means consolidating data from however many data sources you’re using, standardizing the format you use for your data across the organization, and establishing data management policies that are clear and consistent.
3. Build the infrastructure for scalability
One of the great advantages of AI is how scalable it is; once you get a model working, you can query it as much as your budget and bandwidth allow. Effective data models are essential for ensuring that the AI infrastructure can scale efficiently and handle increasing data volumes.
Making full use of this means building AI infrastructure that supports growing AI demands, and the most common way of achieving this is to leverage cloud or hybrid systems for flexibility and scalability.
4. Automate data workflows
With the infrastructure in place, you’ll want to start looking for opportunities to automate data integration and handling to ensure efficiency and reduce manual errors.
This is also a good way to differentiate among MDM offerings. An AI-powered tool like CLAIRE, for example, will allow you to automate and optimize many data management tasks, such as classifying individual data points or finding data for a particular task, ultimately freeing you up to focus on other things. And, since it’s a low-code offering, CLAIRE empowers the less technically inclined to contribute to deploying an AI model, further increasing efficiency.
5. Monitor for continuous improvement
As a final step, establishing a framework for monitoring and improving any AI models that have been deployed is critical. Start by defining KPIs to measure the impact of AI on your most important business objectives so that it’s possible to assess the effectiveness of your strategy. By monitoring these KPIs, organizations can gain valuable insights into the effectiveness of their AI strategy and make necessary adjustments.
In addition, you’ll probably have to regularly update and refine both your AI models, along with the data they’re being fed. There are a couple of reasons this is worth underscoring. First, AI models are indiscriminate; their output can vary from one session to the next and over time. This variability means that monitoring is required to ensure its outputs are consistently moving you toward your broader goals. Second, your data will need to be updated (when you add customers, for example), and there will periodically be new AI models released that you might want to use instead.
Build Your AI on the Right Foundation
AI is an amazingly powerful tool that will certainly transform many industries, but it’s not magical. It requires clean, consistent, accurate, and well-governed data to work most effectively, which is why many enterprises have turned to master data management as a cornerstone of their efforts to improve their AI strategy.
There’s no two ways about it: organizations must invest in data management to unlock the full potential of AI. If you’re looking for an all-in-one, AI-powered solution that unifies and governs an organization's critical data across your enterprise, Informatica’s IDMC platform is a solution that will help ensure you’re ready to fully utilize this powerful new technology. Moreover, by integrating these data management capabilities into an AI-driven, low-code, multidomain platform, Informatica enables more efficient implementation, lower costs, and a measurable, positive impact on your bottom line.
To explore how AI-powered master data management can transform your organization's data strategy, get your copy of Informatica’s latest whitepaper Elevate your Data Strategy with AI-powered MDM.
