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Developing an AI Data Strategy: CDO “Louis DiModugno

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5 minutes, 47 seconds Read

Data is AI’s lifeblood, and in this episode, we speak with a prominent insurance CDO on the importance of data in developing an AI strategy.

Data Curiosity is a consultancy that focuses on data preparation, governance, lineage, master data management, and the integration of large language models (LLMs). Louis DiModugno is the managing partner of this firm. In his roles as chief data officer and analytics officer for two major international insurers, he established data strategies, infrastructure, and efficiency. On top of being a Six Sigma black belt, Louis is a former U.S. Air Force Colonel.

Take a look at this little snippet from our chat with Louis DiModugno and Lisa Wardlaw, hosts of Insurance Unplugged.

The Link Between Artificial Intelligence and Data Science

The field of data science, which includes AI, covers data in all its forms and uses. Within the vast realm of artificial intelligence, we can employ technologies that imitate human decision-making or knowledge base optimization through the utilization of processing and storage resources. Machine learning is one of the subfields of artificial intelligence that focuses on creating models and algorithms that computers can use to learn from data and generate predictions without human intervention.

Reevaluate Your Data Approach

If you want your data to be used accurately in future models, you need a plan on how to aggregate, integrate, and enhance it. Realizing that data is a multiplicative problem, rather than an additive one, is the actual obstacle there. It is not possible to have two data sources that are both 90% accurate just because you incorporate one that is 90% accurate. Only one of your data sources is closer to 80% accurate.

For any data company, improving accuracy must be their top priority. This is a result of good data governance, which entails knowing your data inside and out, including its origins, ways to make it more accurate over time, and ways to increase confidence in it moving ahead.

Building an Argument for Your AI Strategy Based on Data

Your AI approach should be designed with the final goal in mind from the start.

Knowing our end goal is the first step in gathering the necessary data for any artificial intelligence project. Determine if it presents a chance to improve efficiency, effectiveness, or decision-making.

Now feed what you’re attempting to solve for with the components of your data. In order to gain value fast for that specific AI use case, you need next concentrate on the tactical parts of improving the data pieces utilized for it. My faith in the results of that model can grow in tandem with the availability of more and better data.

So, what do you say when someone says, “I have to do AI” but the data team, including the chief data officer, states that your data strategy isn’t developed enough?

The relationship between inputs and outputs in data analysis is not necessarily linear. A mentality of constant experimenting is required of us. Put simply, it’s a game of trial and error. Finding out which data sets are useful and which ones aren’t is the goal. Is it possible to purchase data from an outside source that might enhance or add valuable information to the model?

Is it feasible to experiment quickly enough to 1) ensure I have a high-quality data collection and 2) am I well-equipped to use it in a model-building exercise? This is the problem, keeping the “end” in mind.

We Need Answers to These Questions Regarding When to Use AI

Your chief data officer (CDO) and the business side should be in sync once you’ve resolved the data questions.

Your team members who aren’t technically savvy need to know what AI can do:

How can we put these instruments to use?

I don’t understand the question you’re attempting to address.

Probably the trickiest of the two questions is the second one. It is divided into three parts by DiModugno:

Efficiency: how can I make better use of the resources at my disposal?

Efficiency: how can I find new product capabilities more quickly and more effectively while still satisfying my customers’ expectations?

How can I disseminate or orchestrate a model that improves people’s perceptions of the information so that they can better consume it? This will help me decide which decisions to help make better.

Typically, AI tools concentrate on these areas: how can I distribute or orchestrate a model that helps people see the information more favorably so that they can consume it better? This allows them to make better judgments.

Not only must I know “what am I asking?” but also, after receiving a response, how can I make sense of it? Businesses need to know, “What can I use this for?” Where and for what am I going to put this potent instrument to use?

Guidelines for the Insurance Executive Team

No matter where insurance businesses and AI are in their development, here are four things they can do.

Put data security and privacy first. There must be safeguards in place to prevent unauthorized access or manipulation of private data when it is integrated or ingested into an open source LLM.

More rules are on the way. In both the US and the EU, regulations are on the horizon. On the other hand, if everyone isn’t following the same guidelines, it becomes much more difficult. Ahead of time, businesses must make preparations for this.

Reliability of data is crucial. Facts and accuracy of input are the final arbiters. Finding the original data source will be a major difficulty when working with ChatGPT and huge language models. Was it a book published 30 years ago that provided the answer, or was it my confidential information? I think this is a good question, and it will only grow in significance as time goes on.

Integrate change management into your overall plan. AI is going to cause a shift in how a company operates. Organizations need to know how to integrate AI into their culture and be culturally ready for such changes. Your value offer will be affected by these technologies, and companies need to know that. In this regard, training, both for upper management and for regular employees, is essential.

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