Decision Intelligence and the Future Beyond BI
Decision intelligence has emerged as one of the most strategic technological imperatives.
As every company strives to become data-driven, decision intelligence has emerged as one of the most strategic technology imperatives today. And for good reason. Today there’s more data than ever before, and decision-making is becoming more challenging. Decision intelligence democratizes AI-enabled analytics and enables anyone within an organization to make better decisions faster and more consistently. In this article, Omri Kohl, co-founder and CEO of Pyramid Analytics, explores the emerging practice of decision intelligence, what’s driving its demand, and what the future holds.
The idea that “data is the new oil” is a mantra that companies have rallied behind for decades, and yet data alone creates little to no value if it’s not analyzed and acted upon. Business intelligence (BI) and analytics tools – many of which were first introduced more than 20 years ago (one is even 30 years old) – promised a future where business users could easily access and transform huge volumes of enterprise data to make timely and reliable decisions.
But the reality is that these tools remain highly technical. They are often built on older data models/structures and legacy architecture and require data scientists to conceptualize and transform the data into dashboards, reports and usable insight by others.
Ironically, many lack augmented analytics capabilities and AI, which are critical to achieving two critical outcomes: 1. Moving beyond BI dashboards and basic descriptive analytics; and 2. Putting the power of actionable information – which is what decision intelligence provides – into the hands of everyone in an organization who would benefit from data-driven insights. And isn’t that pretty much everyone?
As businesses of all sizes strive to become data-driven, decision intelligence has emerged as one of the most strategic technology imperatives today. Decision intelligence aims to democratize analytics and enable anyone within an organization to make better decisions faster and more consistently.
Let’s explore what we mean by decision intelligence (DI), what’s driving its demand, and what the future holds.
Defining Decision Intelligence
The term “decision intelligence” was co-invented by Mark Zangari, CEO of Quantellia and Lorien Pratt, Ph.D., Chief Scientist at Quantellia and became more popular thanks in part to Dr. Pratt’s 2019 book, How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Despite technological advances, organizations have traditionally had difficulty applying data science to solve the most critical problems facing businesses today. Why? Simply put: a gap has existed between data scientists and everyday users — employees, stakeholders, and organizational leaders that get the work done.
The gap is now closing. Thanks to advances in AI, ML, and augmented analytics capabilities, decision intelligence is a new emerging discipline that helps organizations move beyond the limitations of traditional business intelligence.
Gartner defines decision intelligence as “a practical approach to improving organizational decision making,” which “models each decision as a set of processes, using intelligence and analytics to inform, learn from, and refine decisions.” Accordingly, new decision intelligence software platforms are coming to market that help everyday users make, more intelligent decisions, even if they don’t have a technical background in analytics or data science.
What’s Driving the Shift from BI to DI?
Today there is more data than ever before. With an incredible 2.5 quintillion bytes of data being created every day, 90% of the world’s data has been created in the last two years alone. A staggering figure, it is expected that the data volume will double every two years. That makes decision-making challenging. According to a recent Gartner survey, 65% of respondents agree that decision-making has become more complex.
In an effort to solve complexities, many organizations turn to traditional BI and analytics tools. But “more” is not “better.” According to Forrester, 25% of organizations use ten or more BI platforms, 61% of organizations use four or more, and 86% of organizations use two or more. This divergence creates data silos that are either inconsistent or incompatible with other business units and therefore, inconsistencies and inaccuracies within the organization generally follow. And, when people lose confidence in the data, analytics adoption plummets.
In turn, organizations suffer from three common issues:
- Inefficiencies and lack of delayed time to insight because data is difficult to access or IT needs to move it first
- Fewer people using the tools slow adoption because they tools are too technical for non-technical people to use, and result in poor experiences
- High costs and complexity that result from having multiple tools for data prep, business analytics, and data science, resulting in a disjointed pipeline and inability to meet the growing demand for more sophisticated analytics like predictive and prescriptive analytics
The underlying problem with traditional analytics is that it is inaccessible to the users responsible for making business decisions. Traditionally, the skills and knowledge needed to build, manipulate, and report the relevant data often have been more aligned with tech and development personnel—not business leaders and strategists. Decision intelligence changes that, in Gartner’s words, by facilitating “a wide range of decision making” through advanced AI and “adapted system applications.”
The Future of Decision Intelligence
Decision intelligence is crucial in moving from actions to outcomes. I recently had the honor of joining Dr. Lorien Pratt at the Israel Hi-Tech Conference 2022 to talk about how the growing field of decision intelligence is taking AI, data, and analytics to the next level. Dr. Pratt put it best: “The shift to decision intelligence is a once-in-a-generation moment, democratizing access to AI and data analytics – technology once reserved exclusively for the tech giants of our age, and enabling decision makers and data scientists to come together to ensure technology can be put to work in solving problems.”
Solving problems and capitalizing on opportunities rely on decisions from everyday business operations like finding the best shipping materials to top-level strategies like identifying upcoming risks and opportunities in the market.
Agility and speed are business requirements in the current economic environment. People in all parts of the organization – from the C-Suite to the front line and everyone in between, need to decide faster, respond faster, capitalize on opportunities faster, and take corrective action faster. Augmented analytics have evolved beyond traditional BI to serve the needs of anyone within an organization within a governed, self-service environment tailored to their role.
See More: Why the C-suite Should Invest in the Competitive Advantage of Quality Data
With the right platform, business users and non-technical users gain access to trusted, repeatable insights in real-time. Data scientists can take advantage of machine learning algorithms and scripting to understand difficult business problems. Specialists can prepare and model their own data to create illuminating analytic content. Importantly, non-technical users can benefit from visualizations and guided analytical presentations that empower better decision-making.
I believe we’re on the cusp of a massive transformation in business analytics, and the next decade will redefine how companies scale to meet the analytics needs of their non-technical people. The organizations that will survive and thrive will have much higher adoption of data-driven decisions because they use decision intelligence with augmented analytics capabilities to enable any person and serves any analytics need from the simple to the sophisticated.
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