While major players keep betting on the producer vs explorer model to access data, a revolution is on its way fuelled by three main factors: startups, machine learning and natural language. Here is how entrepreneurs and technology are changing how we do business intelligence and shifting the paradigm of an old industry.
1 – Startups
With a wider range of business users demanding user friendly ways to perform analytics and Gartner featuring opinions like this one on the Magic Quadrant for BI and Analytics:
“Over the next few years, BI vendors are expected to start playing a quick game of catch-up with the virtual personal assistant market. Initially, BI vendors will enable basic voice commands for their standard interfaces, followed by natural language processing of spoken or text input into SQL queries. Ultimately, “personal analytic assistants” will emerge that understand user context, offer two-way dialogue, and (ideally) maintain a conversational thread.”
one would expect that BI vendors would be chasing new ways to interact with data, but startups are the ones leading the way. The goal is to make data analytics, even the more complex, accessible for any user, including the non-techie ones. These startups are on a mission to empower business users with data and they’ve already defined how they are going to do it.
2 – Machine Learning
As Gartner mentions above, startups are focusing on two types of interfaces, one is more Google-like, with people entering queries in a search box and the other looks more like a conversational bot.
Both of them are based on Machine Learning and Natural Language. The principle is simple: users interact with the BI tool via Natural Language and the system uses Machine Learning to learn how to run analyses on the data. These are essential to make real the promise of Artificial Intelligence – computers learning and producing autonomous work.
It starts with data, the fuel of the engine, it’s how the system can learn to perform a task and what will be transformed to get insights.
For that to happen we use Machine Learning. It feeds from data to learn how to perform a specific task. In this case Machine Learning is helping the platform understanding how it should query the data to give users the result they want. It also helps automating analyses and tasks like creating charts.
3 – Natural Language
On top of all of this is Natural Language Processing. The natural way for us to get answers is asking, so why can’t we do the same with our data? As said before, startups are following two different approaches: one is conversational bots, where in a chat-like environment you can ask “Bot X show me new versus regular visitors to my website”; the other one is a Google-like interface where you can enter queries in a search box. The query can be the same we just mentioned above minus the “Bot X show me…”.
The future should be a mix of the two. While bots are more close to an analytics assistant, they’re also more limited when it comes to present results, as it gives less freedom to change visualisations, solve ambiguity, etc.
Whether we are chatting with a both or entering queries in a search box, in both cases we are using plain language to communicate to the system what do we want to know. This democratizes User Interface, making possible to any type of user to interact with data.
Plus, as we are using Machine Learning, the more we interact with the system, the more it will learn from us and improve results.
All of this will produce insights from where users can act upon, making strategic, data-driven decisions.
Interacting with data using plain language is something that more and more companies are looking for as a way to solve BI tools adoption issues and improve the return on the investment they made on complex and expensive platforms.
The only question is how long it will take these natural language and AI interfaces to become the norm.