Decision Intelligence and Strategies for Success
To deal with unprecedented levels of business complexity and uncertainty, organizations must make accurate and highly contextualized decisions with increasing speed.
This means IT leaders must create the capability to rapidly compose and recompose transparent decision flows, a practice described as decision intelligence.
Decision intelligence is not a technology but rather a strategy encompassing multiple technologies, of which artificial intelligence and machine learning (ML) are at the center.
Businesses need technology that can collect available customer and employee data in a centralized space, understand context and history, detect potential and existing roadblocks, and make recommendations in real-time to ensure the highest rate of success.
Having that centralized “brain” to process enormous amounts of data and make intelligent recommendations based on that data is the key to a successful decision intelligence strategy.
R “Ray” Wang, principal analyst, and founder of Constellation Research, says decision intelligence is what takes organizations from data to decisions. “The goal is to apply a framework to harness data, align that data to a process or journey, capture the insights, and then apply those findings to a decision model,” he says. “The components behind the approach rely on three technologies: analytics, automation, and AI.”
The use of advanced data analytics is a core component of decision intelligence strategies, giving organizations the ability to generate insights, while automation provides the ability to produce data collection and decisions, along with machine learning models.
Artificial intelligence is required to build convolutional neural networks (CNNs) — a class of artificial neural networks, which can be used to analyze visual imagery
“AI is used to build the CNNs, which in turn offer the ability to build a business graph,” Wang says. “The goal is to create an approach that brings in as much information and insight into the market, with CIOs, business leaders, and CFOs as key buyers.”
He points out that the majority of mission critical data will be from outside the four walls of an organization — everything from supplier data to social media feeds and other external information streams will all provide key signals for decisions.
From his perspective, if companies aren’t incorporating intelligent decisioning into their business strategy already, there’s a good chance they may have unhappy or disengaged customers to go along with that choice.
Omri Kohl, CEO and co-founder of Pyramid Analytics, points out that AI is a distinguishing characteristic of decision intelligence and what separates it from traditional business intelligence tools such as Tableau.
“Flashier, more colorful reports, summaries, dashboards, graphs, charts and maps are not what’s needed or next in analytics,” he says. “Those are table stakes. Eye candy.
