Analytics and business intelligence (BI) have long been recognized as fundamental to business success. Today, powerful technologies, including artificial intelligence (AI) and machine learning (ML), make it possible to gain insight into all areas of business activity to increase efficiency, reduce waste, and better understand customers.
So why doesn’t every company do this? Or, more importantly – why didn’t they succeed?
Truly benefiting from analytics—especially the most advanced and powerful analytics techniques involving artificial intelligence—requires fostering a top-down data literacy culture across the organization, which in my experience is something many businesses still where it fails. This was highlighted by a particular statistic that emerged during my recent webinar conversation with Sisense CEO Amir Orad.
Orad told me that, based on his observations, 80 percent of employees in the average organization simply don’t utilize analytics that are theoretically available to them. Indeed, leadership teams and certain functions, such as marketing and finance, have spent nearly decades mastering reporting and dashboard applications. However, this is often not the case for frontline workers and the many professionals responsible for managing the day-to-day operations and service delivery of organizations and businesses.
Orad told me, “The market has matured a lot…BI teams and analysts now have access to very valuable tools…The challenge is the rank-and-file.
“The people who run the actual organization are not harnessing the power of ML and AI because it’s so separate from their day-to-day work.
“We’ve solved the first mile — executives, marketing, sales. We haven’t solved the last mile, which is broader adoption, and that’s where we think there’s a huge opportunity to not only get adoption… …and can really drive how BI and AI can impact organizations in many ways.”
When looking at the role analytics plays in the modern enterprise, it is often clear that the reporting and dashboard approach itself is behind many of the bottlenecks that in turn become the overall deployment and rollout of “top-to-bottom” analytics.
Here’s the problem – analytics and data science teams often find themselves forced to spend time creating tools, apps, and dashboards that are only accessible by 20% of employees, for whom, Analysis is a recognized part of their role. For example, marketing, finance and sales teams, and business leadership. These users are accustomed to their siloed datasets, and while they know they can gain insights from them, not in a way that “new thinking” emerges across the workforce. This prevents new, potentially even more valuable use cases from being able to “bubble up” as part of a company’s data strategy.
This is an obstacle to the “democratization of data,” and we know it’s critical to addressing this if organizations are to unlock the true value that data can bring to their organizations. In short – data and the insights it contains are too valuable to be locked away in the “ivory tower” of data scientists, executive management, and the few rare environments that are already in use.
“People don’t want to use BI. People want to run a better business and serve their customers better,” Orad said.
“They don’t want to use dashboards – they’re just a way to make better decisions and better results – the goal is not more dashboards and more AI, but how do we deliver insights to the In the right hands.”
Failure to address organizational data strategy challenges from this perspective is a surefire way to end up in the “data-rich, insight-poor” situation that hinders so many organizations today.
“The best way to make an impact is to embed the insight you need in the right place at the right time – not in a separate screen where you have to log in and see nice charts and dashboards etc,” Orad said.
So what does this look like in practice? Well, ideally, that means providing insight directly to the operating system being used in real-time. In other words, do away with the data science dashboard model we’ve become accustomed to and rethink the way analytics – or rather insights – are delivered directly to the people who need them at the right time.
For example, let’s say you create a Youtube video with the goal of building an audience and establishing your authority in your niche – a direct marketing tactic used by thousands of businesses around the world every day.
In theory, using artificial intelligence, it is possible to harness the power of natural language processing (NLP) and image recognition, as well as the deep audience analytics available today, to receive real-time feedback on who will be interested in your content, no matter what you say Too fast or too slow, whether your images and graphics will work in attracting the people you want your message to reach – and any other tactical or strategic goals you may have.
In healthcare, doctors monitoring cameras during surgery or observations can receive real-time feedback on what they see inside a patient, as well as advice on possible diagnoses or next steps for surgery.
In an industrial or manufacturing environment, field engineers have real-time visibility into which machines are likely to fail or require maintenance, meaning they can schedule preventive actions and potentially avoid costly downtime altogether.
It could even work in educational settings, Orad suggests, allowing teachers to receive real-time feedback on which students in their class are fully engaged in learning and which are at risk of failing assessments or dropping out.
In the examples Ollard gave me, he saw these principles in action, and one very different one stood out—a charitable organization that ran a crisis hotline connected to a phone number on the Golden Gate Bridge in San Francisco. Signs at various locations on the bridge prompt users to call the crisis hotline if they have negative thoughts on the bridge. Organizations running phone lines then use machine learning-driven predictions to monitor calls in real time and help operators direct callers to the advice and information most relevant to their specific situation. “It enhances humanity by offering options or suggestions for better services…and really saves lives,” Olard told me.
“It doesn’t make sense to give me a report every month telling me what could be better, or to ask that person on the phone, ‘Wait on the bridge, let me log into the dashboard and get some insights’.”
Indeed, it’s easier than ever to derive insights from data, and thanks to the proliferation of cloud services and analytics platforms, almost any organization can leverage technology to make better predictions and decisions. However, as technology continued to evolve, it was quickly realized that getting real-time insights into the hands of the people best suited to use them was the critical “last mile” between a business and its ability to achieve real growth, and insights from data value.
you can Click here Watch my full webinar with Sisense CEO Amir Orad.
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