The Power of Decision Intelligence: 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.

Centralized Brain

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.

AI and Decision Intelligence

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.”

AI is what puts the “intelligence” into decision intelligence by lowering the skills barrier and automating the complex steps required for a data-driven approach.

An integrated platform that includes data preparation with multi-source data access capabilities, business analytics, and data science functionality for power users is essential.

“More people want and require access to data to make faster, more intelligent decisions. These include many people in any given organization who are in line-of-business, non-technical roles,” Kohl says. “Decision Intelligence empowers anyone to make faster, more intelligent decisions. And that can change everything.”

Increased Collaboration Between IT and Business

While chief information officers and chief data officers are the traditional stakeholders and purchase decision makers, Kohl notes that he’s seeing increased collaboration between IT and other business management areas when it comes to defining analytics requirements. “Increasingly, line-of-business executives are advocating for analytics platforms that enable data-driven decision making,” he says.

With an intelligent decisioning strategy, organizations can also use customer data — preferably in real time — to understand exactly where they are on their journeys — be it an offer for a more tailored new service, or outreach with help if they’re behind on a payment.

Don Schuerman, CTO of Pega, says this helps ensure that every interaction is helpful and empathetic, versus just a blind email sent without any context.

In the same way that a good intelligence integration strategy can benefit customers, the ability to analyze employee data and understand roadblocks in their workflows helps solve for these problems faster and create better processes, resulting in happier, more productive employees.

“As AI usage becomes more prominent, organizations need the ability to explain how they’re using data and making decisions, as well as to ensure their AI doesn’t begin inadvertently making biased recommendations,” Schuerman says.

Aligning with Corporate Values

By incorporating tools that conduct bias checks and enable transparency, the organization’s AI and decisioning models will remain aligned with corporate values.

Schuerman adds that decision intelligence can also be extremely beneficial to virtually all employees across an organization beyond those who interact with customers and prospects.

It can help pinpoint inefficiencies and make recommendations to improve employees’ experiences and increase the overall efficiency of an organization.

Having a good data analysis tool to understand patterns, context, and history in real time means better processes, better next-best-action recommendations and ultimately, better outcomes.

“Data analytics and decision intelligence go hand-in-hand,” Schuerman adds. “For your technology to make good decisions — either for your employees or customers — you need to have the right tools to properly analyze the data first.”

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