Top Benefits of Analytics-driven Decision Intelligence

It would be difficult—probably impossible, in fact—to find an enterprise-scale organization in any industry that isn't constantly looking for new ways to maximize the value of its data. This priority is what drives businesses to chase the latest data trends so closely. Numerous industry experts believe that the burgeoning discipline of decision intelligence could rank among the most influential and important of those trends in the years to come.

Decision intelligence has the potential to drive enterprise strategy development and decision-making efforts in a way that is nothing short of revolutionary. In this guide, we'll explore the fundamentals of the methodology, examine its benefits and viable use cases, and look at some of the technologies that can allow enterprises to adopt it.

Decision intelligence uses a combination of cutting-edge technologies and traditional human intelligence to optimize decision-making at scale. It's fair to call it a logical evolution of traditional business intelligence (BI).

Key components of decision intelligence include quantitative analysis through applied data science and the qualitative approaches of social and managerial science. Another core pillar of the discipline is the idea that decisions are dependent on a thorough understanding of how actions lead to outcomes.

Massive amounts of data must be processed during decision intelligence operations. This requires leading-edge data analytics systems and tools, including machine learning (ML) algorithms and cloud-based analytics platforms. But the models used in decision intelligence are just as dependent on human input as ML. Traditional BI methods and tools simply aren't capable of analysis at such a complex level.

Decision intelligence further differentiates itself from other analytical approaches by starting with a decision and the business problem it's meant to solve, and then seeking out data connected to the decision and problem. In traditional methods, the data possessed at the outset determines the queries that are made and the tools used to process them.

Clear advantages can be realized through the implementation of decision intelligence.

Because the practice of decision intelligence involves complex artificial intelligence (AI) and ML technologies, it allows for faster decision-making even at a massive scale. This is especially important for enterprises, where decisions that aren't made at an efficient pace can cripple progress and significantly impede the bottom line. The consideration of such large volumes of data means that a wider range of outcomes—such as might result from every permutation of potential business decisions—can be examined without causing notable delays.

Decision intelligence also allows those who implement it to bridge the gap between quantitative and qualitative analysis—because it doesn't rely solely on either of them. There is a large gulf between decision-making in a vacuum and the often complex nature of the real-world circumstances in which decisions are made, actions occur, and outcomes follow. Sophisticated ML algorithms allow enterprises to collect and process the vast swaths of data that factor into decisions they must make, but the decision model is crafted so that analysts consider evidence based on human knowledge, intuition, and judgment alongside the automation-driven insights that ML produces.

The combination of intuitive considerations and analyses mined from numerous data sources through ML techniques can help to minimize and perhaps even eliminate detrimental biases. Humans are prone to a variety of conscious and unconscious biases—and even the most advanced AI can have bias programmed into it. But in decision intelligence, the two fundamental elements of the process effectively provide quality assurance for each other.

Last but not least, decision intelligence can be effective for situations in which several different logical or mathematical techniques must be factored into a decision. For example, the choice to push back, accelerate, or maintain the date of a product launch involves considerations ranging from Bayesian statistical analysis to projecting customer reactions using the techniques of behavioral economics. The ability to conduct these analyses simultaneously using a decision intelligence framework is a significant step forward from more traditional methods of decision modeling, in which quantitative and qualitative sciences had to be separate.

As is the case with other analysis and data analytics trends that are fairly new, there aren't a wide range of industries or enterprises that have actively incorporated decision intelligence into their business operations yet. Gartner recently projected that this will change in the near future, stating that by 2023, over 33% of large organizations will have their data teams using the cutting-edge technique.

For now, these are some of the sectors where decision intelligence is being used to varying degrees.

Given what's at stake for any bank or financial institution when it decides to take on a borrower, it makes sense that these organizations would embrace an advanced decision modeling method to mitigate risk. Decision intelligence allows companies to efficiently examine loan applicants' personal financial information—credit score, income, investment statements, debt-to-income ratio, and so on—while simultaneously looking at factors that require human judgment to properly evaluate, like employment and residence history. 

Supply chains and the various issues affecting them were brought to the forefront of enterprise leaders' minds when COVID-19 disrupted supply chains across the globe. Shipping companies can use decision intelligence models to analyze factors ranging from route efficiency to fuel usage and driver experience. This can ultimately aid them in devising more effective transportation strategies that can create significant advantages in a highly competitive vertical.

Compelling marketing campaigns have arguably never been more important for enterprises than they are right now. By using decision intelligence, organizations can analyze a broad spectrum of customer and market data to make pivotal choices for their marketing strategies. These include budgeting for channel spend according to priority—e.g., television and social media over direct mail—and projecting which audience segments will be most receptive to specific campaigns.

Inefficiencies and service interruptions for water, natural gas, or electricity providers cause major difficulties for customers and—particularly in the case of the latter two utilities—can jeopardize contracts with state or city governments. Minimizing these occurrences requires a strategy that's based on large-scale analysis of relevant data and also informed by the experience of human experts—which is exactly what decision intelligence can offer. In a recent blog post, AI and data science expert Lorien Pratt highlighted a water technology firm that used decision intelligence to reduce water use in Fountain Valley, California by 23% and help Park City, Utah save hundreds of thousands a year on water costs.

Because decision intelligence is a discipline composed of multiple scientific and business practices rather than a specific technology, it's not something companies can instantly adopt. There is no single off-the-shelf decision intelligence tool you can use to implement it within your organization in one fell swoop—at least not yet. Also, due to the sheer scale associated with decision intelligence, it can be complicated to adopt at first if it's not thoroughly planned and tested in advance, but the right technologies and best practices can guide the way.

This starts with an effective and agile cloud deployment. The massive volume of data required for decision intelligence can most readily be accommodated by the elasticity and virtually unlimited resources of the cloud. And with a hybrid cloud approach, you have a seamless connection to any on-premises data infrastructure your enterprise needs or wants to maintain.

Other key tools for decision intelligence may already be in your enterprise's tech arsenal: decision modeling software, business rules management solutions, and an ML stack. A cloud-based, leading-edge data analytics platform like Teradata Vantage will also be essential. The solution integrates data from across the organization so it can be properly leveraged in decision intelligence models. Vantage also makes it easier to examine data—while focusing on the human factor of complex decisions—with dynamic visualization features.

To learn more about Vantage, take a look at the 2021 Gartner Magic Quadrant report for cloud database management systems, in which Teradata was named an industry leader.