The competitive edge of data science is preserved primarily by two things: the sheer volume of the data and the time it takes to understand the nuances. Data repositories in growing organizations are often siloed and the different silos don't always speak to one another efficiently. Plus the complexity of the data takes teams of data scientists years to understand. Picking up on subtle nuances related to specific businesses or industries is challenging. These 2 things prevent companies that rely on data science from ultimately losing their competitive edge.
I spent many years fooling around with HR data. If there is any area where data is probably more siloed and dirty than healthcare, it’s probably HR.
There are a lot of external data sources that companies to pull in like performance data. It’s often not all housed in one place. Now data scientists have to think about data integration. It sends chills down my spine because it’s very hard, complicated and costly to do those things. This complexity is what in part motivated me to found RapportBoost. I don’t want to deal with endless data integration. Conversely, at RapportBoost our focus is really simple, conversational data and some measurement of the outcome of that conversation.
And the conversational data consists of only 3 fields: What was typed? Who sent it? When was it sent? The challenge there is not data integration--it’s parsing a lot of unstructured data that has a lot of sarcasm, emoticons, and misspellings that we have to deal with. We don’t want to do a ton of integrations to clean up the data. Luckily our data is in good shape when it comes in. Our challenge is can we parse something that is not lending itself to structured data analysis.
Dr. Michael Housman, Chief Data Scientist and Co-Founder of RapportBoost.AI, discusses the challenges and methods of preserving the competitive data edge while creating effective solutions at the JMP Securities Conference panel on AI.
Learn more about the chat sales solutions from the team at RapportBoost.AI.