Decision intelligence is defined as an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. The basic idea is that decisions are made based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains. So, why is it important to your organization?
Decision intelligence is the holy grail of data-driven decision making
Today’s companies struggle to keep up with – and make use of – massive amounts of data. This is where AI-enabled consumer intelligence comes in: it analyzes consumer-generated data to help companies understand real-time consumer behavior and preferences. Decision intelligence is the missing link that enables you to turn data into better, contextualized decisions at scale. By predicting potential outcomes, you’re able to to make well-informed business decisions.
First, collect and hybridize data to grasp your ecosystem
Decision intelligence requires that you’re already collecting, hybridizing, and analyzing data in one single place. AICI achieves this with three factors:
- Technical factor: The infrastructure needs to be able to ingest large amounts of data
- Technological factor: The tool needs to be able to ingest data from any source
- Methodological factor: The tool needs to apply the right algorithms and frameworks to augment and optimize the data
Let’s dive into this in a bit more detail:
- Technical. More consumer data is generated every single second. There is a constant and massive flow of data to manage, including:
- 500M tweets per day
- 50B photos uploaded per day on Instagram
- 4PB of data created by Facebook per day
- 3,5B searches per day on Google
- Technological. To truly understand consumers, you need more than just social media platform data. It requires combining multiple data sources and formats like:
- Social Data
- Behavioral Data
- Survey Data
- Internal Data (Sales data, HR data, etc)
- Traffic Data (in restaurants, store, events…)
- Etc.
- Methodological. Using consumer-generated data to address a real business question requires analytics expertise. It requires a combination of:
- Algorithms (Machine Learning methods, supervised or not, econometric, statistical, etc.)
- Frameworks to analyze data within the right context (business question, use case, domain, industry, etc.)
With these three components, you’re well on your way to understanding how consumers think, feel, and behave – and turning that information to better business decisions. But to really predict consumer behavior and outcomes, you need to go one step further.
Apply decision intelligence to predict what’s next
To explain how decision intelligence works, let’s start by defining a decision. According to the Cambridge dictionary, “A decision is a choice that you make about something after thinking about several possibilities.”
When making a decision, people consider their environment, the current situation, their previous experience(s), emotions, intuitions, etc. They can also be influenced by the opinions of external stakeholder or peers, and they of course layer on their own perception of the world.
Our brains try to combine the above factors to make a decision. But, we’re human! We’re not able to impartially and objectively assess a situation and weigh all of these inputs.
This is why decision intelligence is so powerful – and so necessary to making decisions at enterprise speed and scale. It’s a complete change of how people assess situations. Unlike humans, AI-powered systems are able to ingest, process, and analyze incredibly high volume of historical data, in real-time to make predictions. Then, they will suggest the best possible decision based on the data set and initially specified parameters.
To reiterate, there are two main differences between human and AI-powered decision-making:
- AI takes into account all the information available, while a human brain is intrinsically limited.
- Artificial intelligence is objective and not influenced by emotional factors. It eliminates bias with ML algorithms while still incorporating the value of human intuition, knowledge, and judgment.
In other words, data collection, machine learning, and AI are invaluable because they enable us to work with massive volumes of data. Now, ensuring the accuracy of your predictions requires enriching your analysis with decision-making methodologies and processes.
Here’s how it looks in practice:
- Build a data lake with all relevant data
- Apply relevant AI algorithms to analyze, segment, and understand your data
- Apply the right decision-making scenario
- Social science: Social science is the branch of science devoted to the study of societies and the relationships among individuals within those societies – this is key to making sure AI-generated decisions make sense in real life
- Decision trees: The goal of this algorithm is to create a model that predicts the value of a target variable. Decision trees use supervised machine learning to make predictions based on how a previous set of questions were answered.
- Predictive Analytics: Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning to analyze current and historical facts to make predictions about future or otherwise unknown events.
Decision intelligence is a highly endogenous process. To enable decision intelligence models to make the right decision, they need to be fed as much relevant data as possible. Like any AI model, the better the input, the better the output. This is why organizations struggle to improve decision-making if they don’t have access to all the relevant data sources. Ideally, organizations will partner with trusted vendors to bring internal and external data together to fuel better business decisions.
In action: decision intelligence to improve user experiences
Let’s end with an example of decision intelligence applied to a specific business need. One of our clients, a major French re-employment player, wanted to assess how their users perceive the overall quality of their service. They primarily provide training, coaching, and support to people looking for a job. To gather feedback from their users, they systematically run surveys each time they launch a new initiative or capability.
To help them get a better understanding of user feedback, we used the following process:
- Understand where and how users are inquiring about these initiatives
- Build the most typical journey flows to assist users
- Track the areas where users are still struggling to find information
As a first step, we gathered and analyzed the following sources of data:
- Website visits
- Comments on the website
- Surveys
- Training feedback
By analyzing these data sources – both conversational and behavioral, as well as requested and unrequested – we were able to build a 360° view of how users interact with this service and what they think about it. This enabled us to map the user journey of searching, discovering, and experiencing new initiatives from the re-employment player.
To help them improve their user experience, we relied on decision trees built by Ipsos over the past decade. We adapted these decision trees to their needs to give them concrete recommendations for UX improvements. One of the recommendations they implemented involved completely rethinking their website interface, creating an easier-to-follow discovery journey for users.
Want to learn more about how Synthesio’s AI-powered platform and decision intelligence capabilities can help you make better, more accurate decisions? Request a demo today!
