Let’s say you are an executive at a big company or the CEO of a mid-size startup. You’ve requested information about the quarterly sales performance of your company and your team has put together a report that renders the latest sales metrics using beautiful charts. So here you are, reviewing a detailed report with gorgeous visualizations and you are still confused about what it all means. Then you start a conversation with your team to better understand the data and brainstorm possible strategies.

Does this scenario sounds familiar? It should because it is certainly common in the corporate world. Data analytics are typically accompanied of additional information in the form of human conversations. To put it in simple terms, natural language plays a role improving analytics.

The advancements in deep learning techniques such as natural language processing(NLP) and natural language understanding(NLU) have created new opportunities to enhance analytics as we know it. We might soon be entering the era of conversational analytics.

Data Semantics via Conversations

Data serves little purpose as an isolated element. Just when it is interpreted data acquired a semantic meaning associated with a specific context. Voice and conversational interactions can create narratives to enrich business data sets. Additionally, natural language narratives can expand the semantic interpretation of data sets beyond static visualizations. For instance, a sales forecast visualization might show a positive version of the company’s performance but the management team could interpret it using a different narrative of the data as they understand the forecast still falls below their top competitors.

Interpretation

User: Alexa, what does the bottom right quadrant of this chart represents?

Imagine having that type of conversation with your favorite digital assistant and reviewing explanations about specific data analytics. Interpretation is one of the areas on which voice conversations can really improve how we consume analytics. Conversational analytics will not only provide right visualizations but it will complement the graphics and data with voice narratives that will help to interpret them.

Multi-Medium Analytics

Suppose that you recently reviewed the latest quarterly sales forecast of your company and that you are currently in your car driving to a meeting with investors. Imagine if you could ask the analytics digital assistant in your car’s console: “Alexa, please help me review our latest sales forecast”. At that point, the digital assistant will start providing a succinct voice summary of the forecast data and clarifying some of your questions. Pretty cool, huh?

Conversational analytics open the door to models that allow the consumption of analytical data across different channels ranging from rich web dashboards to voice-only devices.

From Voice to Analytics

In a typical business day, we have dozens of conversations and meetings on which we discuss hundreds of data points. Those data points and ideas aren’t typically captured anywhere except in our own memories. Imagine, having an analytics assistant that is constantly documenting the data points in our conversations and creating real time analytics based on them. That would be something, wouldn’t it?

Written by

CEO of IntoTheBlock, Chief Scientist at Invector Labs, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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