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3 Ways AI and IoT Bring Data Analytics Insights to Life

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3 Ways AI and IoT Bring Data Analytics Insights to Life

We believe that natural language processing (NLP), which allows us to talk to machines as if they were human, is the future (and the present) of business intelligence.

· Big Data Zone ·
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Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.

If you’ve been paying any attention to the news lately you’ve probably heard the words “artificial intelligence,” “machine learning,” and “bots” thrown around quite a bit. From wearables to customer service bots and beyond, it’s safe to say what was once thought of as a vision of the distant future is starting to show itself today: especially in the BI world.

We believe that natural language processing (NLP) — the combination of machine learning, AI, and linguistics that allows us to talk to machines as if they were human — is the future (and the present) of business intelligence. So, for the past two years via Sisense Everywhere, we’ve been offering our customers innovative ways to interact with their complex data beyond the screen.

But what can AI and IoT offer BI users? I’m happy you asked.

1. Answer Effectively

In business, being able to answer your customers’ questions quickly and with proper data is a must. And while using dashboards to drill down and answer questions not only provides you with accurate numbers and effective responses to customers, NLP-enabled devices (like a chatbot or Amazon Echo) have the ability to get you the instant answers you need in a more natural and intuitive way.

Take Premium Retail Services (PRS), for example. They provide third-party services to suppliers across the U.S. and Canada, looking specifically at how often and when they have to go inside stores, data on where they need to go next, and how often they have to service different products for their clients.

PRS uses the Sisense Bot and Echo integration to have data at their fingertips.

“The biggest difference is that with a typical dashboard you have to really filter and there’s a lot more work involved in trying to get to the data you want,” Aaron Hayes, Senior Software Architect says, “with using a voice controlled system it’s a lot faster to get to the data you need to be able to answer a question from your client or the business.”

2. Communicate Simply

Ah, the age old questions: How will I get my business users to adopt yet another platform? How can I make sure that my investment is worth it?

Among some of the keys to adoption success, like spreading the word through periodic communications, publicizing success stories internally, and centralized training days, have you thought about bringing dashboards into your business users favorite communication channels?

Casumo, an online gaming platform, did just that and are reaping the benefits. By integrating a chatbot into their favorite business communication platform, Slack, Casumo was able to bring the insights to a platform that they were already comfortable with. This reduced friction, making data more accessible and easy to digest and share with others in the business.

“Our vision was to take analytics even closer to where the business user feels comfortable and would prefer to use it,” Casumo Data Scientist, Emanuele Nardo says.

This means that end users don’t have to log in to an external tool to get information, but instead can query the chatbot in natural language in a platform they already know how to use and receive a response that is easy to understand — bringing reliance on data scientists and IT to an all time low.

3. React Quickly

Imagine this: You’re driving down a packed highway when, out of nowhere, you hear sirens and see red flashing lights in your rearview mirror. Instinctively, you know you need to pull over to the side of the road to let the emergency vehicle pass.

What if you could have a visual cue (minus the emergency sirens) whenever your customer support team is missing their service level agreement (SLA)? Or whenever your target revenue for the month dips below a predefined number? With integrated devices like a light bulb that changes color according to your own predefined conditions, data is no longer something that sits on another tab on your screen.

“Bulb is the KPI that you don’t need to load up on one of your screens, it’s not just another browser window,” Brent Allen, Director of Infrastructure and WebOps at Skullcandy, says. “It’s this physical piece that’s simply part of your life. It’s a simple product with a powerful way of telling you whether things are going well.”

Where to Start?

When you’re thinking of integrating IoT and AI devices into your data plan, it’s important to remember the basics. To being with, you first need an underlying system that can handle complex data. You should look for a BI product that covers the full scope of analytics in one agile software, all the way from preparing data for analysis to creating dashboards with a variety of customizable visualizations.

And let’s not forget about self-service. You should also find a tool that allows you to build complex data models easily and gives end users the freedom to explore, filter, and drill down into data independently to answer their ad-hoc questions.

The days of being constrained by having data only on your screen are gone. Once you’ve set the foundation, the ways you can consume your data can bring you benefits you thought were reserved for the year 2055.

Hortonworks Sandbox for HDP and HDF is your chance to get started on learning, developing, testing and trying out new features. Each download comes preconfigured with interactive tutorials, sample data and developments from the Apache community.

Topics:
big data ,artificial intelligence ,internet of things ,business intelligence ,data analytics

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