Data Reasoning 101: Understanding the Various Levels of Complexity
Nov 25, 2024
Gradient Team
Quick Primer: What is Data Reasoning and Why Is It Hard?
Data reasoning is the practice of analyzing, interpreting, and applying logic to data, in order to draw insights that can help deliver informed decision-making. It goes beyond basic data collection and analysis by focusing on understanding the why behind the what. At Gradient, we like to describe this as moving from basic operational tasks to higher order operational tasks. Unlike basic operational tasks—which might handle simple calculations or aggregations—data reasoning takes it a step further, using advanced operations to interpret data and help you integrate your institutional knowledge back into your workflows. This can involve uncovering relationships between data points, forecasting future trends, analyzing sentiment, or integrating unstructured data from various sources to produce actionable insights.
In essence, data reasoning requires more than just access to data; it requires the ability to process, interpret, and derive meaning from data in ways that align with specific business goals. And by data, we mean all types—both structured and unstructured. This process is especially valuable in cases where data alone cannot yield straightforward answers but requires deeper analysis and contextual understanding to guide decisions.
However the challenge with data reasoning is that it’s inherently complex. Real-world data is not ideal. As we mentioned earlier, it can be noisy, incomplete, unstructured or inconsistent - posing significant challenges to teams that have to work with this data. On top of that, distinguishing between correlation and causation requires deep expertise and careful methodology. Which means that you’ll need a unique blend of analytical prowess, domain knowledge, and critical thinking if this is something you want to pursue. Having the right combination of talent and tools to just get a start on this can be daunting for most businesses. However if you stick around, we’ll introduce a platform that will not only simplify this process for you, but require far less resources than traditional methods.
Why Is Data Reasoning Important for Enterprise Businesses?
Despite how complex data reasoning may be, it isn’t just a luxury for enterprise businesses—it’s a strategic necessity. As organizations face increasing competition and rapid technological advancements, relying solely on executing tasks that use basic order of operations can limit growth and innovation. The ability to reason through data equips enterprises with the power to not just report what happened, but to anticipate what could happen, identify root causes, and prescribe optimal strategies.
It’s safe to say that the future of automation hinges on data reasoning. Automation tools are becoming smarter, but for them to move beyond simple, repetitive tasks, they need to incorporate advanced reasoning capabilities. Enterprises that can harness data reasoning will benefit from predictive and prescriptive automation, leading to smarter workflows, better customer experiences, and more agile operations.
Sound like something that could help your organization? If you’re still unsure, ask yourself these 5 simple questions to find out.
Data Reasoning: The Various Levels of Complexity
Understanding the various levels of complexity when it comes to data reasoning can help enterprise businesses understand the core concepts in order to develop a comprehensive strategy for data analysis and decision-making. With each increasing level of complexity, additional logic is required to help coordinate and produce the desired output. However what’s important to note is that an enterprise use case may span across one or more levels, depending on the desired output. This exponentially increases the difficult, even with a fully staffed data science and machine learning team. Here’s a quick breakdown from the simplest form of data reasoning to the most complex.
1. Descriptive Reasoning
Descriptive reasoning, also known as descriptive statistics, is the simplest form of data analysis and probably what you see most often in your day to day. It analyzes past data to understand and summarize what happened, using techniques such as aggregation, data visualization, etc.. This type of reasoning is foundational for business intelligence, helping organizations track performance and communicate findings effectively.
Example: An example of descriptive reasoning would be analyzing quarterly sales data to report that a company's revenue increased by 15% compared to the previous quarter. This insight provides a clear summary of past performance, helping the company understand recent growth trends and informing stakeholders about business progress.
2. Diagnostic Reasoning (Associative/ Correlation Analysis)
Diagnostic reasoning is used to uncover the underlying reason(s) behind certain outcomes by analyzing data to identify associative relationships or correlations. It goes beyond describing what happened to determine what other factors could have impacted it. This type of reasoning helps organizations pinpoint the underlying causes rather than the symptoms, enabling better decision-making and targeted problem-solving. You might recognize this type of reasoning if you’ve done any of the following analysis: correlation, trend or root cause.
Example: An example would be if a company were to analyze customer feedback and demographic data, in order to determine why a product’s sales dropped in a specific region. By examining trends, customer sentiment, and market conditions, a business can identify underlying issues, such as negative reviews related to product quality or a mismatch between product features and regional preferences, and take corrective action to improve future performance.
3. Predictive Reasoning
Predictive reasoning involves using historical data to forecast future events or trends. By analyzing past behaviors and patterns, predictive reasoning helps organizations anticipate what is likely to happen, enabling them to make proactive decisions. This empowers businesses to stay ahead by anticipating potential challenges or opportunities, enabling proactive rather than reactive strategies. Most commonly you will see this type of reasoning if you’re doing a regression analysis, time series forecasting or any type of machine learning.
Example: An example would be analyzing historical customer engagement and purchase data to forecast future customer churn. By identifying patterns that indicate when customers are likely to stop using a product or service, a company can proactively implement retention strategies, such as targeted offers or personalized follow-up communications, to reduce churn and maintain customer loyalty.
4. Causal Reasoning
Causal reasoning focuses on understanding cause-and-effect relationships within data, aiming to determine whether one variable directly influences another. Unlike correlation, which only shows an association, causal reasoning helps identify true causation. This enables businesses to make confident strategic decisions based on evidence, ensuring that actions taken lead to intended results. This type of reasoning is particularly important in strategic pivots or new initiatives. For most teams, they’ll see this type of reasoning when conducting A/B testing or when they use causal inference methods.
Example: An example could be running an A/B test to determine whether a new customer service approach leads to higher customer satisfaction. By dividing customers into two groups—one experiencing the new service model and the other continuing with the current approach—the company can analyze the results to see if the new model directly causes an improvement in satisfaction. This allows the business to confidently implement the new approach if the data shows a positive causal effect.
5. Prescriptive Reasoning
Prescriptive reasoning focuses on recommending specific actions to achieve optimal outcomes, often while considering various constraints and potential scenarios. It goes beyond simply predicting future trends by suggesting how to respond effectively to those forecasts. This empowers businesses to stay ahead by anticipating potential challenges or opportunities, enabling proactive rather than reactive strategies. This ultimately helps organizations make informed decisions and maximize their results, often seen in optimization work or running any type of simulation.
Example: An example of prescriptive reasoning would be using data analysis to develop targeted marketing strategies aimed at maximizing customer retention. For instance, after identifying which customer segments are most likely to churn, a company could create tailored promotions or personalized communication plans to engage those customers and keep them loyal. This approach helps optimize marketing resources and improve retention rates by recommending the most effective actions to take.
6. Reflective Reasoning
Reflective reasoning focuses on self-improvement and long term planning. Where prescriptive reasoning may suggest specific actions to achieve a specific objective, reflective reasoning encourages a higher-level perspective, questioning if the strategy itself is sustainable, adaptable, or still relevant in light of changing conditions or new insights. This type of reasoning encourages businesses to rethink and adapt their approaches, fostering continuous improvement and resilience in dynamic environments.
Example: An example of reflective reasoning would be a company assessing its reliance on a single supplier after noticing potential risks, such as supply chain disruptions or escalating costs. By reflecting on the company's sourcing strategy, decision-makers may consider diversifying suppliers or exploring alternative materials to ensure stability and competitiveness. This strategic evaluation helps the business adapt proactively and better align with its long-term goals of resilience and growth.
The Easiest Way to Master Data Reasoning: Gradient’s AI-Powered Data Reasoning Platform
If you’re unsure if data reasoning is what your organization is looking you, take a look at our recent article that we put together to help you get an answer by answering 5 simple questions. For those who are interested but are unsure on how to get started - look no further.
The team at Gradient developed the first AI-powered Data Reasoning Platform that’s designed to automate and transform how enterprises handle their most complex data workflows. Powered by a suite of proprietary large language models (LLMs) and AI tools, Gradient eliminates the need for manual data preparation, intermediate processing steps, or a dedicated ML team to maximize the ROI from your data. Unlike traditional data processing tools, Gradient’s Data Reasoning Platform doesn’t require teams to create complex workflows from scratch and manually tune every aspect of the pipeline.
Schemaless Experience: The Gradient Platform provides a flexible approach to data by removing traditional constraints and the need for structured input data. Enterprise organizations can now leverage data in different shapes, formats, and variations without the need to prepare and standardize the data beforehand.
Deeper Insights, Less Overhead: Automating complex data workflows with higher order operations has never been easier. Gradient’s Data Reasoning Platform removes the need for dedicated ML teams, by leveraging AI to take in raw or unstructured data to intelligently infer relationships, derive new data, and handle knowledge-based operations with ease.
Continuous Learning and Accuracy: Gradient’s Platform implements a continuous learning process to improve accuracy that involves real-time human feedback through the Gradient Control System (GCS). Using GCS, enterprise businesses have the ability to provide direct feedback to help tune and align the AI system to expected outputs.
Reliability You Can Trust: Precision and reliability are fundamental for automation, especially when you’re dealing with complex data workflows. The Gradient Monitoring System (GMS) identifies anomalies that may occur to ensure workflows are consistent or corrected if needed.
Designed to Scale: Typically the more disparate data you have, the bigger the team you’ll need to process, interpret, and identify key insights that are needed to execute high level tasks. Gradient enables you to process 10x the data at 10x the speed without the need for a dedicated team or additional resourcing.
Even with limited, unstructured or incomplete datasets, the Gradient Data Reasoning Platform can intelligently infer relationships, generate derived data, and handle knowledge-based operations - making this a completely unique experience. This means that teams can automate even the most intricate workflows at the highest level of accuracy and speed - freeing up valuable time and overhead.
Under the Hood: What Makes it Possible
The magic of the Gradient Data Reasoning Platform is its high accuracy, quick time to value, and easy integration into existing enterprise systems.
Data Extraction Agent: Our Extraction Agent intelligently ingests and parses any type of data into Gradient without hassle, including raw and unstructured data. Whether you’re working with PDFs or PNGs we’ve got you covered.
Data Forge: This is the heart of the Gradient Platform. AI automatically reasons about your data - re-shaping, modifying, combining, and reconciling your structured and unstructured data via higher order operations to achieve your objective. Our Data Forge leverages advanced agentic AI techniques to guide the models through multi-hop reasoning reliably and accurately - contextualizing the institutional data and grounding it to elicit the best output.
Integration Agent: When your data is ready, Gradient will ensure that your data can be easily integrated back into your downstream applications via a simple API.
With Gradient, businesses can focus on the outcomes—whether it’s driving customer insights, ensuring regulatory compliance, or optimizing production lines—without getting bogged down in the operational intricacies of data workflows. By automating complex data workflows, organizations can achieve faster, more accurate results at scale - reducing costs and enhancing operational efficiency. In a world where data complexity continues to grow, the ability to harness that data through automation is not just a competitive advantage—it’s a necessity.
