Ever since the dawn of technologies like the hand-held calculator, we have been empowered to make quicker decisions with digital data at scale. Technology has evolved drastically since then, of course: today, an overwhelming amount of data permeates everything we do and say.
Most recognise the transformational power of data in business, but even as ‘big data’ got bigger and bigger (and bigger), many of the opportunities in it have remained relatively untapped. Too little data; too much data; or the enormous amount of laborious processes and manpower necessary to actually empower people to use it – there have been roadblocks. What was missing were a couple of key ingredients: automation and artificial intelligence.
But now automation and AI are powering the next evolution in using data – decision intelligence. While once there was business intelligence – a static sort of interpretation, with limited generative capabilities – decision intelligence makes data-driven decisions available and useful to everyone, in all business units and at all levels.
And not a moment too soon, because despite the awareness of data’s transformative power, an enormous gulf exists between what decision-makers want and what they have.
According to a recent IDC study, 83% of CEOs say they wanted their organisations to be more data driven, and 87% of CXOs say that becoming an intelligent enterprise is a top priority. But in practice, only 30% of decision makers say that actions in their organisations are driven by data analysis. Only 33% are comfortable questioning the KPIs and metrics used in organisations, in other words, interrogating the quality of the data beneath the superficial layer of numbers or statistics.
However, a separate IDC study found that a third of C-suite decision makers globally are now enthusiastic about the potential for decision intelligence, which is already helping businesses leap ahead from the limitations found in traditional business intelligence platforms, and no longer relegated solely to data scientists or statisticians.
“If you’re basically querying against the database and preparing visualisations based on what’s in it, that’s not different from what we’ve been doing with BI (business intelligence) for years,” says Chandana Gopal, research director for future intelligence at IDC. “With decision intelligence, what’s different is really enabling organisations to analyse data to generate insights – to make those insights available to everybody, and make it pervasive within the organisation.”
For companies like Mr Kipling manufacturer Premier Foods, decision intelligence powered by Pyramid Analytics is helping it connect data from all of its sources. For example, SAP Business Warehouse with Amazon RedShift is helping to provide visibility and insights across all business functions. Already, the platform is allowing the company to automate insights into the recyclability of the products it generates, shifting it away from laborious manual processes and helping the business meet sustainability goals.
London’s Regent Street staple Liberty, meanwhile, is using decision analytics to inform its omnichannel retail strategy – rolling out self-service, in-depth analytics across finance, buying, merchandising, web and shop floor, available on any device, anywhere – creating massive time savings and allowing the business to better measure and prepare for peak shopping hours. It’s becoming evident that businesses equipped with this kind of actionable data are likely to inch ahead of competition due to the sheer speed and power of automated insights; whether businesses are really data driven or not will ultimately be a differentiator in today’s hyper-competitive landscape.
If change starts from the bottom up and there’s no support from management, people will continue to make decisions based on their gut instinct
But the end results could be even more profound, says Omri Kohl, CEO of Pyramid Analytics, provider of a decision intelligence platform used by data scientists and non-technical line of business users alike, with organisations one day transforming into “autonomous companies” where burdensome manual decision-making is completely taken over by AI.
This would mark a shift to an outcomes-based kind of analytics, which Kohl compares to TV. It’s rare for most viewers to obsess over the technical ins and outs of how a show can be broadcast, streamed, processed and viewed in real time: “The same should happen with analytics – eventually, all I care about is the outcome. What can I do better, and how can I perform smarter, better and faster in my organisation? That’s what we’re trying to deliver in decision intelligence, providing you with the outcome.”
But how do you get to that point? Many businesses have, of course, invested in analytics technologies for decades, adds IDC’s Gopal. To reach the next step, a holistic, joined-up approach is required.
“You have all the technologies you need, but do you have the ability to access all the insights they need to make decisions on a day-to-day level?” she asks. “Does your organisation have the skills, the culture, the understanding, the trust? All of these elements must come into place to be able to leverage technology to really guide decisions and actions after those decisions.”
The CEO has a critical role to play in bringing about these changes. To create businesses with data as their beating heart, CEOs will need to lead by example.
“If change starts from the bottom up and there’s no support from management, people will continue to make decisions based on their gut instinct,” comments Kohl. “This works to a certain degree – but not when the decisions are super complex.”
Even small changes at the leadership level can cascade into wider organisational shifts. When CEOs begin debating the accuracy and quality of data they are making decisions with, adds Kohl, suddenly the conversation is no longer quantitative and forces the business to align on being data driven.
At the same time, leadership must work to dissolve the data silos that can naturally form within organisations.
Just as most businesses have a single CRM system, says Kohl, it makes little sense to have hundreds of often ad-hoc analytics initiatives all running independently. “In those cases, you can’t really see a single version of truth across the organisation,” he says. “It’s extremely costly to support so many localised initiatives from different groups, functions and even different people.”
With that single source of truth established, businesses can be better served by one analytics platform with capabilities that cut across departments and, crucially, one that’s simple enough to use that every employee is encouraged and able to actually engage with it. This is where internal initiatives around data literacy matter: “It’s one thing to make a decision to become data driven,” says Kohl. “It’s a completely different story now to support everybody to start implementing that capture.
“Analytics isn’t one size fits all – my requirements are different to colleagues in finance, to the controller, to the CFO, to the accountant, to the bookkeeper. Each one of us has different requirements, different needs, and this is where automation and AI can influence the way people actually use analytics without preliminary knowledge.”
Armed with the data, tooling, understanding and resolve to make it happen, this is the task of the data-driven CEO – leveraging automation and AI to empower every user, no matter their function.
To learn more about applying augmented analytics, decision intelligence and AI in your enterprise, visit pyramidanalytics.com/decision-intelligence-platform
Promoted by Pyramid Analytics
Ever since the dawn of technologies like the hand-held calculator, we have been empowered to make quicker decisions with digital data at scale. Technology has evolved drastically since then, of course: today, an overwhelming amount of data permeates everything we do and say.
Most recognise the transformational power of data in business, but even as ‘big data’ got bigger and bigger (and bigger), many of the opportunities in it have remained relatively untapped. Too little data; too much data; or the enormous amount of laborious processes and manpower necessary to actually empower people to use it – there have been roadblocks. What was missing were a couple of key ingredients: automation and artificial intelligence.
But now automation and AI are powering the next evolution in using data – decision intelligence. While once there was business intelligence – a static sort of interpretation, with limited generative capabilities – decision intelligence makes data-driven decisions available and useful to everyone, in all business units and at all levels.
