Chapter 2:
The Nuts and Bolts:
A Deeper Dive into Data, AI, and… Humans
The concept of AI is simple at its core. It's the idea that a machine can take actions and exhibit human-like analysis.
The benefits of using AI are also straightforward. By automating complex analysis and processing massive amounts of data, AI provides a solid foundation for unlimited growth.
Artificial Intelligence processes and analyzes data, which gives it the ability to streamline all types of workflows. AI functions enhance decision-making and achieve greater efficiency and productivity. In turn, people can focus less on rote, administrative tasks and more on strategic initiatives. This enables professionals to spend more time leveraging their unique skill sets to conquer more significant business transformational goals.
AI is innovation that enhances human ingenuity.
While the benefits of AI are undeniable, a lot goes into the successful execution of these technologies. AI technologies simplify many jobs by removing manual, repetitive tasks from a human’s to-do list. But first, there are complexities to address when making them work practically for businesses.
How Does AI Work?
In the beginning, AI machines were limited to following an instructive computer program, step by step.
While primitive AI was coded to follow explicit rules and carry out narrow tasks, modern AI can reason and create increasingly like a human—but faster.
Built on a foundation of advanced mathematics and deep learning architectures, today's AI can leverage incredibly vast data sets. This allows AI software to recognize and learn increasingly complex patterns.
While Artificial Intelligence and data science are two different concepts and practices, they are closely intertwined.
When building AI technology, programmers use algorithms to process input data and generate output—results, conclusions, or actions. For modern AI, these algorithms comprise a flexible foundation that allows the machine to learn from data and appropriately adapt to different workflows.
AI algorithms are the step-by-step instructions that guide machines how to make decisions based on data. They make it possible for machines to process data in real time, crawling through endless sources, without humans taking a special step to introduce the information to them. They “combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data.”[7]
Avoiding “Garbage In, Garbage Out”
The old saying, “garbage in, garbage out” (GIGO), is especially relevant to data science and AI.
AI solutions are only as good as the data they receive. To be effective, they first need to take in high-quality data. The data acts as the model's teacher, while the algorithm instructs the machine how to leverage the data. Algorithms come together to form the comprehensive learning framework for any AI solution.
A machine is only as "intelligent" as the data is good. It needs guidance from comprehensive sets of rules and logic, based on accurate, up-to-date data, in order to transform those learned patterns into reliable, human-level performance and actionable results.
Since data has become so dynamic, programmers and data scientists can rarely “set it and forget it” when it comes to algorithmic development and programming for AI-based machines.
Good AI requires both good data and finesse.
[6] https://www.ibm.com/topics/data-science
[7] https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/
AI users can overcome data quality issues by integrating the human with technology.
Today, responsible AI use requires data scientists to perform quality checks—regular audits to identify duplicates, outliers, missing information, corrupted files, and even typos.
While human intervention is required more frequently in certain cases, consistent human oversight is always critical to data integrity. It takes a combination of good programming and regular data checks to ensure that AI tools run on data sets that are complete, accurate, relevant, and high quality. This is the only way to ensure that AI will consistently produce the highest quality output—or intelligence.
As we will discuss in greater detail later in this ebook, AI isn’t magic. Companies must balance human performance and machine output to capitalize on the opportunities that AI offers.
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Companies must balance human performance and machine output to capitalize on the opportunities AI offers.
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How to Ensure You Are Working With the Best Data Sets
Success with any layer of AI hinges both on data quality and access.
AI-driven Insurtech tools need to be able to reach comprehensive sets of property data to produce precise, accurate property insights.
A common challenge to this ability lies with data silos: isolated data collections that are inaccessible to other stakeholders or business units. These silos, which are singular, locked down storage locations—digital or otherwise (as we will discuss further in Chapter 6)—prevent your AI from getting the complete picture it needs to be effective.
When data exists only in one locked location, its value is trapped, starving your AI of what it needs to generate truly comprehensive and reliable insights.
It can be a never-ending challenge to consistently ensure that your organization works with integrated (or, un-siloed), good-quality data. Difficulties compound with the number of data sources available to property insurers has grown exponentially in recent years.
Data management can be downright difficult—but not impossible.
The process begins with a deep assessment of your data— first identifying where it all lives and then conducting an honest review of its quality.
When you are sure that your AI technology is working with the best data sets, you can then set your system up to glean maximum AI benefits. Here's how to do it:
1. Adjust Your Data Governance Model to Maximize the Power of AI
A data governance model is the system of rules and organizational structure around all the data an organization manages. Leaders and data experts establish this framework to make sure a company's important data is accurate, safe, and used correctly by everyone, at all times. It's a collaborative framework that sets the standard for "an effective and efficient use of information."[1]
In other words, companies set up data governance models to rule how data is stored in technology, so that it is kept and used securely and ethically.
For optimal AI performance, your organization must revise its data governance model to guarantee data quality standards right when the data is captured, entered, or collected. This revision should stipulate that all AI input data sets are consistently accurate, complete, timely, relevant, and consistent in formatting. It is also best if the model clearly determines who will provide human oversight in data management.
Aligning your data governance with company goals ensures that data is always treated as a strategic company asset, guaranteeing its value is maximized and risks are managed just like any other critical business resource.
2. Conduct Regular Data Audits
Organizations with a dedicated team of professionals to oversee data quality and usage will reap the greatest results from AI. These data audit groups perform regular data audits to ensure that data is always handled securely and within the bounds of established data governance models.
These routine audits are critical to your company's ability to identify data silos, access, and collection issues that could be undermining AI performance.[2]
Data auditing teams are key to embedding the necessary human oversight in a successful AI program.
A specialized team monitoring data quality and practices can spot errors, as well as areas for improvement—both in the data itself and in how AI is interacting with it. These professionals will provide essential insights for executing AI enhancements and upgrades.
Note: it is necessary to support and equip these data management professionals with the right resources for conducting effective audits.
3. Nurture an Integrated Digital Ecosystem
Enhance the value of AI by establishing a digital architecture of solutions that can speak with one another and seamlessly share data between platforms. The more integrated these systems are, the less room there will be for unwanted data silos.
For insurance companies, it is ideal for data from an underwriting technology to automatically populate within claims technology. This drastically reduces the possibilities for inconsistencies and miscommunications to develop over an insurance lifecycle. This is just one example of how an integrated digital ecosystem lays the essential framework for accuracy and important collaboration between all stakeholders in a property insurance workflow.
With an integrated digital ecosystem, there is a single, centralized source of truth. As a result, AI technologies can pull data from one reliable location. This makes data absorption and processing more efficient, thereby optimizing performance and ensuring businesses can make decisions based on the most reliable, up-to-date, consistent intelligence.
4. Work Exclusively with Trusted Data Sources
Because AI technologies are often designed to aggregate data from many sources, it’s essential to work with vendors who can guarantee that their core property data is always up-to-date, accurate, and comprehensive.
It's also important to work with partners that consistently get the necessary permissions to use and distribute data from all their sources. This clear-cut permission is always key to avoiding any downstream compliance issues.
For example, Cotality CoreAI™ solutions leverage data from the industry's most comprehensive and accurate property data source. Cotality has its own extensive, evolving property database, and it partners with other verified sources. Cotality's due diligence with data verification ensures that its solutions don’t pull different data sets from various sources and then produce insights based on inconsistent or contradictory collections of data. Additionally, Cotality gets explicit permission from every client before using any data in its proprietary database.
To fuel transparency, there are also no black boxes in Cotality's technology.
[8] https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-a-data-governance/
[9] https://www.capterra.com/resources/how-to-conduct-a-data-audit/
