June 25, 2018 | Written by: Bill Lobig
Categorized: AI for the Enterprise
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The promise of AI is that it will deliver digital transformation and improve productivity and efficiency across businesses. For many of our customers, IBM Watson has already helped deliver on this promise – by enriching customer interactions, accelerating research and discovery, empowering employees, and mitigating risk.
The next step for businesses is to make AI ubiquitous by operationalizing their workflows across the full AI lifecycle. IBM is committed to delivering these fundamental, end-to-end AI capabilities and giving enterprises everything they need.
For example, consider the critical step of understanding and preparing data for productive and speedy use in analytical tools, machine learning and deep learning. Your teams first need access to all your data –no matter where it lives. But there are often multiple barriers to fully harnessing the value of your data, including:
- Exponentially growing volumes of data
- Complex structures and multiple file formats
- Multi-layered governance rules locking down access to files across organizations
IBM has addressed these challenges with IBM Watson Knowledge Catalog, which was recently recognized by Forrester as a Leader in: “The Forrester Wave™: Machine Learning Data Catalogs, Q2 2018.”
Machine learning data catalogs (MLDCs) are the stepping stone for an intelligent business – they help scale out data understanding and speed up data use. IBM’s MLDC offering, Watson Knowledge Catalog, is an intelligent cataloging service that knowledge workers, including data scientists, can use to index all the available data sets in their business, on premises or cloud. This includes open and third-party data, as well as dashboards, data science and ML models, connections, notebooks and more, to activate them for analytics, machine learning and deep learning with IBM Watson Studio.
“The Forrester Wave™: Machine Learning Data Catalogs, Q2 2018,” which evaluated the major machine learning data catalogs, mentions that IBM Watson Knowledge Catalog: “disguises its ML and cataloging power behind a simple role-based workspace. Revisiting its traditional data management and governance approach to enablement, IBM designed its MLDC from the ground up around role intent and behavior, with ML at the core and the ability to tap into Watson APIs.” 
Driving business value with Watson Knowledge Catalog: A use case
Think about how this could work with processing insurance claims, which is an expensive, time consuming, and risk-intensive process. The more time it takes to process a claim and make required adjustments, the higher the risk of lawsuits, which are a costly outcome. Challenges around claims processing become especially intense during natural calamities, when insurers need to process a sudden spike in claims, even to the point of transporting adjusters to the impacted location.
With a data-driven approach, the information gathering process can be significantly expedited with immediate access to relevant information at the first notice of loss, and the use of data, analytics and AI to help identify potential claim fraud using deep learning and detailed data analysis.
Using information that’s available in the insurance company’s enterprise Knowledge Catalog, the provider can easily develop a data-driven claims process that:
- Creates a dashboard for a claims agent to interact with the information pushed up to the insurance company from the customer’s mobile app
- Reduces the median time for a claim to be processed
- Minimizes the risk of fraud
- Automates as much of the claims and adjustment process as possible, while triaging more complex claims for adjusters to process
Watson Knowledge Catalog: Fueling AI workflows
View our tutorial to learn more about the intelligent and collaborative capabilities of Watson Knowledge Catalog that empower the insurance industry’s business analysts, data scientists and data professionals. You can also find more inspiration and learning on the IBM Watson blog.
Get a free copy of The Forrester Wave™: Machine Learning Data Catalogs, Q2 2018 Report