Analytics and Business Intelligence (BI) have long been considered fundamental to business success. Today, powerful technologies, including Artificial Intelligence (AI) and Machine Learning (ML), make it possible to gain deeper insights into all areas of business activity to increase efficiency, reduce waste and gain a better understanding of customers.
Then, why isn’t every company doing this? Or, more importantly – why aren’t they doing so successfully?
To really benefit from analytics – especially the most advanced and powerful analytics technology involving AI – there is a need to develop a top-to-bottom culture of data literacy throughout the organization and this is, in my experience, where many businesses still fail. are happening. This is highlighted by a particular statistic that came up during my recent webinar conversation with Amir Orad, CEO of Cissens.
Orad told me that according to his observations, 80 percent of employees in the average organization simply aren’t taking advantage of the analytics that are, in theory, available to them. It is true that leadership teams and some functions, such as marketing and finance departments, have spent recent decades catching up with reporting and dashboard applications. However, this is often not the case with frontline employees and the many professionals whose job it is to manage the day-to-day operations and service delivery of organizations and enterprises.
Orad tells me, “This market has matured a lot … and BI teams and analysts are now having really valuable tools at their disposal … the challenge is rank and file.
“The people who run real organizations haven’t taken advantage of the power of ML and AI because it’s so different from their day-to-day lives.
“We’ve solved the problem of the first mile – C-suite, marketing, sales. We haven’t solved the problem of the last mile, which is widespread adoption, and that’s where we believe that not only There is a huge opportunity for adoption… but also to really move the needle on the impact of BI and AI across multiple organizations.”
When looking at the role of analytics in the modern enterprise, it often becomes clear that it is the reporting and dashboarding approach that is behind many of the bottlenecks, which in turn lead to overall deployment and “top-to-bottom” analytics.
Here’s the problem – analytics and data science teams are often forced to spend time creating tools, applications and dashboards for themselves that will only be accessed by 20 percent of the workforce, for whom analytics is an accepted part of their role. For example, marketing, finance and sales teams and business leadership units. These users are accustomed to their silent datasets that, although they know they can draw insights from it, are not available to the entire workforce in a way that allows “new thinking” to emerge. This prevents new, potentially even more valuable use cases from being able to “bubble up” to become part of a corporate data strategy.
This is an obstacle to the “democratization of data” that we know is important to address if organizations are going to unlock the true value that data can bring to their organization. Simply put – the data and the insights it contains are too valuable to be kept locked up in the “ivory towers” of data scientists, the C-suite, and some of the rarefied environments where it is already used.
“People don’t want to use BI,” says Orad. People want to run better businesses and serve their customers better.
“They don’t want to have dashboards – they’re a way to make better decisions and deliver better results – the goal isn’t more dashboards and more AI, it’s how we get insights into the hands of the right people at the right time.”
Failing to address organizational data strategy challenges from this angle is a surefire way to end up in the “data-rich, insight-poor” situation that is holding many organizations back today.
“The best way to make an impact is to embed the insights you need in the right place at the right time — not in a separate screen where you have to log in and see a nice chart and dashboard, et cetera,” Orad says.
So what does it look like in practice? Well, in ideal terms, this means providing insights, in real time, directly to operational systems as they are being used. In other words, by moving away from the data science dashboarding model we are rethinking and rethinking the way analytics – or rather insights – are delivered directly to the people who need them at the right time.
For example, imagine that you are creating Youtube videos with the aim of building an audience and establishing your authority within your niche – a direct marketing strategy employed by thousands of businesses around the world every day.
Theoretically, using AI, it would be possible to harness the power of natural language processing (NLP) and image recognition, along with the in-depth audience analytics available today, to receive feedback in real time about who is in your content. is interested, whether you’re speaking too fast or too slow, are your images and graphics going to work when it comes to engaging the people you want your message to reach – and Any other tactical or strategic objective that you may have.
In health care, a doctor monitoring a camera during an operation or observation procedure can receive real-time feedback on what they are seeing inside a patient’s body and receive suggestions about possible diagnoses or next steps of procedures. Can do.
In an industrial or manufacturing environment, engineering workers on the ground can gain real-time insight into which pieces of machinery break down or require maintenance, which means they can determine preventive measures and prevent potentially costly downtime. can be completely avoided.
It can also work in an educational setting, Orad suggests, with a teacher receiving real-time feedback about which students in their class are fully engaged in their learning and which are more likely to fail assessments or drop out. are in danger.
The instances in which Orad told me of occasions where he has seen these principles put into practice was one very different – a charity organization that linked a crisis line to a phone number on San Francisco’s Golden Gate Bridge. operates. Signs at various locations on the bridge prompt users to call the distress line if negative thoughts occur on the bridge. The organization running the phone line uses machine learning-powered predictions to monitor calls in real time and help point operators to advice and information that is most relevant to their specific situation. “It’s augmenting human beings with options or suggestions to serve them better … and literally save lives,” Orad tells me.
“Giving me a report once a month on what could have been done better, or asking in person on the phone, ‘Wait at the bridge, let me log into the dashboard and get some insight’, doesn’t make any sense.”
It’s true that it’s easier than ever to pull insights from data, and thanks to the proliferation of cloud services and analytics platforms, just about any organization can leverage technology to make better predictions and decisions. As technology continues to evolve, however, it is quickly becoming clear that putting real-time insights into the hands of those who are best positioned to use them is the critical “last mile” Stands between businesses and the ability to achieve real growth and value from data.
you can click here To see my full webinar with Sissens CEO Amir Orad.
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