The future of analytics and business intelligence?

Analytics and Business Intelligence (BI) have long been understood as fundamental to business success. Today, powerful technologies, including artificial intelligence (AI) and machine learning (ML), have made it possible to gain a deep understanding of all aspects of business activity to drive efficiency, reduce waste and gain a better understanding of customers.

So why doesn’t every company do this? Or, more importantly – why aren’t they doing it successfully?

Truly benefiting from analytics – especially the most advanced and powerful analytics techniques that involve AI – requires developing a culture of data literacy from the top down within an organization, and in my experience, this is where many businesses are still falling short. This was highlighted by one particular statistic that emerged during a recent webinar discussion with CEO of Sisense, Amir Orad.

Orad told me that according to his opinion, 80 percent of the employees in the average organization are not using the analysis that is theoretically available to them. It’s true that management teams and some functions, such as marketing and finance departments, have spent the last few decades using reporting and dashboard applications. But front-line workers and many professionals manage the day-to-day operations and service delivery of organizations and enterprises.

Orad tells me, “This market has grown a lot… and BI teams and analysts are now getting very useful tools at their fingertips… The challenge is the rank and file.

“People who run the right organizations are not harnessing the power of ML and AI because it’s so disconnected from their day-to-day operations.

“We’ve solved the first mile problem – the c-suite, marketing, sales. We haven’t solved the last mile problem, which is broad adoption, and that’s where we believe there’s a big opportunity, not just to get adoption…”

As analytics pay attention to the role it plays in the modern enterprise, it becomes clear that behind many of the bottlenecks is the reporting and dashboarding approach itself, which in turn, serves as a barrier to overall deployment and rollout. Down” analysis.

Here’s the problem – analytics and data science teams are often forced to spend their time creating tools, applications, and dashboards that only 20 percent of the workforce can access because analytics is an accepted part of their role. Marketing, finance and sales teams, and business leadership departments, for example. Although these users know they will gain insights, they are generally accustomed to their latent data bases, which are not available in all workforces, in such a way that “new thinking” can emerge. This prevents new, potentially more important use cases from being “pulled” to become part of the corporate information strategy.

This is an obstacle to what we know as the “democratization of information” if companies are to unlock the true value that data can bring to their organizations. Simply put – data and the insights it contains are too valuable to be kept in the “ivory towers” of data scientists, the c-suite and the few rarified areas where they are already used.

“People don’t want to use BI,” says Orad. People want to do better business and provide better service to customers.

“They don’t want to do dashboards – they’re just a way to make better decisions and get better results – the goal isn’t more dashboards and more AI, it’s how we get insights into the hands of the right people. Time.”

Failure to address organizational information strategy challenges from this angle is a sure way to fall into the “data-rich, intelligence-poor” scenario that is holding many organizations back today.

“The best way to make an impact is to put the insights you need in the right place – you have to go into a separate screen and see a nice chart and dashboard, etc.,” says Orad.

So what does this look like in practice? Well, strictly speaking, what it means is delivering insights in real-time, directly to operational systems as they are used. In other words, ditching the data science dashboarding models we’re used to and rethinking the way analytics — or rather, insights — can be delivered directly to those who need them at the right time.

For example, imagine you’re making YouTube videos to build an audience and establish your authority in your niche – a direct marketing method used by thousands of businesses around the world every day.

In theory, using AI, it would be possible to use natural language processing (NLP) and image recognition, along with today’s deep audience analytics, to receive real-time feedback on who is looking for your content. Fast or slow, your images and graphics are going to work when it comes to engaging the people you want your message to reach – and any other tactical or strategic objective you may have.

A doctor monitoring a camera during a healthcare, surgical or follow-up procedure can receive real-time feedback about what they see inside the patient’s body and recommendations for available tests or next steps.

In an industrial or manufacturing environment, engineering staff on the ground can gain real-time insights into which machinery is breaking down or in need of repair, meaning they can schedule preventative measures and avoid costly downtime altogether.

It can also work in an educational setting, says Orad, where a teacher receives real-time feedback on which students in their class are fully engaged in their lessons and at risk of failing or dropping out of assessments.

Of the examples Orad gave me of examples of these principles being put into practice, one stood out—a charity operating a crisis line linked to a phone number on San Francisco’s Golden Gate Bridge. Signs at various points on the bridge prompt users to call the crisis line if they experience negative thoughts on the bridge. The company that operates the hotline uses machine learning-based predictions to track calls in real-time and direct operators to the advice and information most relevant to their situation. “It’s adding options or suggestions to better serve people … and literally save lives,” Orad told me.

“It doesn’t make sense to give me a report once a month about what could have been done better, or to ask the person on the phone, ‘Wait on the bridge, go to the dashboard and get some insights.'”

It’s true that extracting insights from data is easier than ever, and thanks to the proliferation of cloud services and analytics platforms, any organization can use technology to make better predictions and decisions. As technology continues to evolve, however, it is quickly becoming clear that putting real-time insights in the hands of the people best placed to use them is the critical “last mile” that stands between businesses and the ability to achieve real growth. value from data.

you can Click here To watch my full webinar with Amir Orad, CEO of Sysense.

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