Ayasdi Core vs. Vertafore RiskMatch

Well suited: 1. If data set is not yet well organized. 2. Hypothesis is not yet established. 3. Need to visually explore to find patterns of data (often when analysts have no good understanding of data) 4. When [you need] to analyze events with a timeframe (specifically a sequence of events as a transaction) Less appropriate 1. If a data set is very large, such as Hadoop data, it becomes hard to manage data pipeline and process to feed the data into Ayasdi. To be feed into Ayasdi, data should be aggregated or organized to some level.

Incentivized

For analyzing insurance policies for the clients that have specific requirements is really suitable since it allows you to match all this information and make the overall thinking process more easier when it comes to make a decision or to provide information to the customer.

Incentivized

• Ayasdi Core provides an easy way to get some insight on data. Typically analytics may require having a model or hypothesis before starting to look into the data, but Ayasdi lets you just feed the data first then start seeing what the data looks like.
• Ayasdi Core's topological network visualization is quite unique. It allows you to explore patterns and potential relations between multiple data elements. A user can also dynamically navigate data with different aspects on the web.
• The Web version of Ayasdi is easy to use, stable, and fast. It hasn't crashed even when we feed it a lot of data sets, although it took time.

Incentivized

• sorts book of business
• integrates with agency management system
• allows us to view revenue, etc without need to run reports in agency management system

Incentivized

• Use of Python SDK is required to feed data into Ayasdi, but it lacks training materials or sample codes for a novice to get started.
• Although Web UI of Ayasdi is looking good, often it freezes when the user runs an analysis. It doesn't crash but the web page needs to be refreshed to see the progress of analysis.
• Algorithms provided by Ayasdi, such as metrics types, lens types need to be explained (what they are and what their strengths and weaknesses are). We had to Google or do research on our own to understand what they are.

Incentivized

• Risk Profile Categorization
• Can lead to a lot of spam email

Incentivized

Because I have the feeling that some improvements should be done to the software itself but the overall experience is quite good for the paid price and the information and analysis you can received from the system

Incentivized

Vertafore Support is always ready and willing to provide support when we ask for it.

Incentivized

We had a working group that has been using R studio for the general purpose of statistical analysis in our organization. Although it is a great tool that provides enriched function sets, it is time-consuming for our clinical analysts to learn the tool to see the first result. R is somewhat of a developer-oriented/friendly tool. Ayasdi is friendly to a domain analyst or end users. Plus, support and consulting from Ayasdi were excellent so that we could get knowledge from them immediately whenever we needed.

Incentivized

Vertafore had a cleaner platform all around. The user interface was far superior and the functionality seemed to be leaps and bounds ahead of the competition. Everyone was pleased with the sales team and the responsiveness. We felt the tool integrated very well with our existing internal tools and software.

Incentivized

• Good decision-making tool
• Accuracy

Incentivized