In this podcast we talk with cody david, solutions architect with syniti, which is part of capgemini, about the importance of ensuring Data Quality for Artificial Intelligence (AI) Workloads,
Being approle to trust ai is core to its use, he says. And here we need to be sure that its outs are reliable. That's only going to be the case if ai is trained on a dataset that's not full of duplicates and incomplete data.
Meanwhile, AI, Claims David, Can Be Used To help with data qualitySuch as by finding issues in datasets that can lead to erroneous outcomes.
The big takeaay is that Organizations need a “data-firist” attitude so that ai can do its work and Produce Reliable Results That can be trusted, and he outlines the quick wins that can be gained.
Antony Adshead: What are the key challenges in data quality in the enterprise for ai use cases?
Cody David: One of the biggest challenges in data quality for ai that i see is trust.
Many people View an AI System as a Single Black Box. When it produces an incorrect person or action, they call it an ai mistake and they lose confidence. Sometimes permanently, they might lose that confidence.
The real issue, however, often lies in poor data quality. This is compounded by the lacked of undersrstanding of how the AI Solutions Truly Work.
Consider a Sales Organization. They have a Crm And it has duplicate customer records. And an AI Solution Ranks Your Top Customers Incorrectly because it's not rolling up all of the transactions to one account.
So, the sales team blames the Ai tool, Never realising that the root cause is actually poor or inconsistent data. This is an example of what we call data quality for ai; Ensuring that data is accurate and ready for there-driven processes.
On the flip side, there's also AI for data quality, where an ai solution can actually help detect and merge that there duplicate records that we just out in that example. I think one more challenge is that data quality has history Organasations often Jump into ai without this data-first mentality and before ensuring they have that solid data foundation.
So, you have these legacy systems, these legacy ERP Systems With Thousands of Tables and Decades of Compounding Data Issues.
That all adds to this complexity. And that's why it's crucial to address data quality issues proactively rather than trying to retrofit solutions after that ai initiatives fail. We've got to put that data up-fraont and center of these ai initiatives and then establish that stable solution that that's going to support that Trustworthy Ai Outputs.
What are the key steps that an organization can take to ensure data quality for Ai?
David: I think a systematic approach always begins with data governance.
And that's really the politicals for how data is collected, stored, cleansed, shared, and finding out who is the true owner of particular business processes or datasets. It's Crucial to Figure Out Who's Responsible for that Standards.
I think that next, you want to prioritise. Rather Than Trying to Fix Everything at Once, focus on that area that delivery the biggest business impact. That's a very key phrase there: what's the biggest business impact of what you're trying to fix as far as data quality? And Figure out the ones that feed your AI solutions.
This is where you're going to see those quick wins. Now, there are going to be budget concerns that often arise when you start talking about these data quality, data governance programs. And ironaciously, it's more excursive to work with bad data over the long run.
I think a practical solution is to start small. Pick a critical business process with measurable financial impacts. Use that as a pilot to demonstrate those real savings in roi.
And Once you show that data Quality improvements lead to tangible benefits, like cost Reductions or higher working capital, you will have a stronger case with the management for a wider Data Govern Investment. You should also embed that that data quality practices in data workflow. For example, Integrate Validation Rules Into Your Data Management SO Errors Can Be Caught Immeditely, Preventing that Data from Impacting that that the Solutions.
If you can't put in validations like that upon your data creation, you've got to put the system and processes into place to catch there immediatively through automated reporting.
Lastly, I would say always focus on that Continual improvement. Measure Data Quality Metrics and Use them to Drive Itecive Refinements By Weaving That Data Governance Into Your Organization, Proving Its Value Through Through Targeted Pilots, And then you create that that you are create that that is Foundation for the Trustworthy Ai Initiatives.
Finally, I wondered if you could give an example of one or two quick wins that Enterprises can get in terms of data quality and improving data qualities for Ai?
David: There are a few different examples of we try to get Quick Wins for Data Quality, Especially when Trying to Get Vry Quick Rois and HIGH-HIGH-HIGH-HIGHAH-HIGHAH ROIS and HIGHAH-HIGH
If you take an erp system, we have what we call materials. That are ons that are parts to equipment in a manufacturing process. And when you have those materials, you usually keep a safety stock or an amount of that items that would allow you to repair that machines.
If a plant goes down, you're going to potentially lose millions of dollars a day. And if you have duplicate materials, as an example, you're actually storing more than you need. And that's actually working capital that, if you to correct that data quality, you free up that work capital.
And then, of Course, You Can Use that Working Capital for other parts of your initiatives.
Another one would be maybe vendor discounts. If you have vendors that are duplicated in a system, and they are entitled to rebates based upon the Amount of money they're spending, they're not going to realise that thats that those those. That count be an area where you could have cost saving as well.