Ayasdi Core vs. Prism, by Prism Skylabs (discontinued)

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

I really like to use Prism, compared to other software, for data analysis. It allows a quick "plug and play" of the data and is very tolerant of many data formats. I typically resort to other software only when I need a more specific statistical analysis, or if I need a graphically cleaner visualization.

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

• Data manipulations
• Data visualization
• Ease of use

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

• Need a stronger interface with R
• Advanced analysis is difficult to use
• Graphically clumsy

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

Excel would be the next best thing to use. It is free for the academic lab to use though the quality of the figure is lacking and not standardized. Excel also lacks easy to use built-in formulas for data manipulation and can be tedious to replicate the same data Prism can do with a single click.

Incentivized

• Excellent figures for publications

Incentivized