Decision Intelligence: What Is It & Why Is Alternative Data an Essential Piece of the Puzzle?

Organizational consultant and author Idowu Koyenikan writes, “Speed is not your enemy; hesitation is.” He’s right. The ability to make highly informed decisions in a timely manner can be the difference between the success or failure of your business. Particularly during times of uncertainty or market volatility, doubt can hold organizations back. And data overload can contribute to this doubt.

So how do businesses ensure they do not fall behind the competition or consumer values? Increasingly, they are looking to Decision Intelligence. This transformative approach harnesses AI-driven analytics to support decision-making across any enterprise, from financial services organizations to global manufacturers.

Staying ahead of the competition, protecting against risk, and driving innovation requires fast, confident decisions. And to get there, you need more than internal data to power DI; you need timely, relevant alternative data to add context within DI applications, enabling actionable insights and data-driven decisions.

Why do you need decision support?

Think about it. Investment managers navigating volatile markets, compliance officers ensuring adherence to ever-changing regulations, or product managers deciphering customers’ next need—they all need to fast-track decision-making.

Unfortunately, with data flooding in from all sides, manual analysis becomes a gargantuan task. It’s certainly not an approach that accelerates decisions. That leaves decision-makers falling back on past experiences and intuition—neither of which offer true insights.

What defines Decision Intelligence?

Writing in Toward Data Science, Google Chief Decision Scientist Cassie Kozyrkov describes DI as “a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them.”

Decision Intelligence marries AI, machine learning, and collaboration tools, breaking down internal data silos. But It's not just about accessing data but making it actionable. That’s where third-party data comes in. You can identify trends in consumer behaviors from internal data, for example. But by adding historical and current news datasets, you provide important context into the drivers behind those behaviors, enabling predictive insights and prescriptive advice—and ultimately, confident decisions.

Even better, DI continuously learns. For instance, as more investment decisions get made, the system refines future recommendations.

What are the building blocks of Decision Intelligence?

Data Collection and Analysis

DI thrives on robust data. Data analysis techniques in Decision Intelligence use both quantitative and qualitative data to assess the relationships between variables. Here’s how they differ:

• Quantitative data derives results that are mathematically and statistically measurable from sets of data that are generally massive.
• Qualitative data makes sense of messy or unstructured data by identifying patterns.

Naturally, investment decisions benefit from quantitative data, but numbers alone don’t tell the whole story. Ingesting qualitative data like historical and current news in DI applications unlocks valuable insights into events, trends, and other outside influencers. For example, instead of just observing a dip in a stock, a DI system might highlight a related negative news article, enabling investors to comprehend the root cause behind the dip and make a more informed choice.

The adage, “Garbage in, garbage out,” holds true when it comes to Decision Intelligence. DI’s strength relies on the quality of its data. As a result, ensuring you have the right data for Decision Intelligence starts with identifying a provider of timely, enriched external datasets aggregated from a wide variety of sources. The resulting analysis, recommendations, and decisions will offer relevant insights to inform decision makers.

Human Judgement and Expertise

Decision Intelligence integrates human decision-making into the process by providing interactive tools that allow decision making employees to fully leverage their expertise, interact with models, and evaluate the ramifications and payoffs of the various decision options. Human insight is integral to the process of data selection, collection, and Decision Intelligence practices.

Employees with an understanding of the essential aspects of their company and their role within it can guide the process of DI then evaluate its recommendations and outcomes within the context of its value to their company.

Technology and Automation

Advanced tools like machine learning and predictive modeling stand at the heart of DI. For instance, predictive modeling can help investment managers anticipate market movements. Likewise, automation in compliance decisions ensures swift, consistent actions aligned with regulatory norms.

How are organizations harnessing Decision Intelligence?

As with many AI-enabled technologies, you need to build confidence in Decision Intelligence to broaden adoption and normalize it into your processes. Fortunately, DI can be aligned to your organization’s specific needs, enabling you to step up use of DI as your comfort level and confidence increase. Here are a few examples of how an organization might step up over time:

• Decision Augmentation: Users are assisted by DI technology to make decisions using DI data analysis, data exploration, and alerts. In this scenario, the DI acts as a guide to potential decisions.
• Decision Recommendation: DI is used to both analyze the data and make predictions that provide a recommended decision. The DI predictions and recommendations enable more confident decisions. For instance, a risk manager could leverage DI insights to identify red flags and act on those cues.
• Decision-making: DI is empowered to make decisions based on past positive performance. In real-time trading scenarios, for example, DI can auto-execute trades based on preset parameters, optimizing investment returns.

Distinguishing between Business Intelligence, Decision Intelligence and Artificial Intelligence

Though intertwined, key distinctions exist between Business Intelligence, Decision Intelligence, and Artificial Intelligence.

• Business Intelligence involves the use of data analysis tools and techniques to make sense of raw data, providing insights that can help organizations make informed business decisions.
• Decision Intelligence is specifically molded for decision-making in business contexts. This discipline encompasses the tools, processes, and frameworks used to make decisions by modelling, analyzing, and optimizing complex systems and scenarios. For instance, while BI might recognize patterns in customer behaviour, DI recommends launching a new financial service targeting that behaviour.
• Artificial Intelligence embodies the simulation of human intelligence in machines. From Natural Language Processing to Machine Learning, AI underpins both BI and DI, enabling the automation of data analysis and the prediction of outcomes, making the decision-making process more efficient and data-driven.

The imperative of Decision Intelligence for future success

Quality decisions can be the lifeblood of financial success. Consider this: a slight misjudgment in investment strategy or non-compliance can cost millions. According to Forbes, the average S&P 500 company wastes approximately $250 million per year due to ineffective decision making. Decisions made well, and a company’s ability to turn their insights into action, are vital to the financial success of a business.

DI saves employees time that would have been spent on tasks like manually sifting through data, thus providing faster commendations to executives, and allowing them to focus on other areas of importance. Additionally, these results are often free from the human error that can occur when analyzing large amounts of data—saving you time and money.

Furthermore, DI’s continuous learning loop helps the quality of the decisions to get better, providing practical and concrete recommendations based on parameters set by the user (who does not need to be a technology or data specialist!).

Considerations before adopting Decision Intelligence

Introducing new technologies comes with a caveat. Preparing to address these factors helps ensure success.

• Data availability: If your datasets are too small or limited in scope, your outcome will be limited, as well. You should consider working with a service that provides you with ample high-quality data to make sure your research is robust and fruitful. That’s where Nexis^® Data as a Service can help. From historical and current news to legal and regulatory information, LexisNexis aggregates data from reputable sources so you can deliver the right data into your DI
  applications.
• Data quality: If your past data does not meet standards of consistency, timeliness, accuracy, and completeness, you will need access to a set of data that does. DI only produces outcomes at the levels of what it has learned and had reinforced through its continuous learning loop—which is based on the quality of the initial data.
• Change management: No one wants to feel like they are going to be out of a job because of a machine. Knowing that you might face resistance from employees empowers you to meet the challenge head on to break down resistance and encourage adoption. Transparency about the process and how it benefits employees will help usher in the change.
• Human bias: AI applications, including DI, are taught using past data to make future decisions. Biased data and algorithms can amplify discrimination in DI analysis by reproducing the patterns of bias. Rigorous oversight can mitigate such pitfalls. Establishing a diverse team can help you properly manage the data so you can avoid perpetuating embedded bias through your DI.

Staying current with Decision Intelligence trends

Effective decision-making is the cornerstone for business growth and success. Yet, its efficacy hinges on the quality of underlying data. Cutting-edge technology, without quality data, leads to misguided decisions. As Asif Syed, Vice President of Data Strategy at Hartford Steam Boiler shared with MIT, “In most cases, you can’t build high-quality predictive models with just internal data.”

Nexis^® Data as a Service empowers performance, catalyzing innovation, risk mitigation, and fostering competitive advantage. With relevant external datasets complementing the data you generate in-house, you’re poised for DI success, enabling decisions that are swift, insightful, and robustly defensible.

Ready to dive deeper into the world of decision-making? Check out our eBook, "The Future of Decision Making.”

Discover how Nexis^® Data as a Service can redefine your financial trajectory.