Automating Complex Data Workflows with Gradient’s Data Reasoning Platform
Oct 15, 2024
Gradient Team
Creating Value Through Data
In today’s enterprise landscape, data is the lifeblood that drives decision-making, operational efficiency, and strategic initiatives. Companies collect and manage vast amounts of data, but simply gathering information isn’t enough to unlock its full potential. To extract true value from data, businesses need to:
Leverage the full capacity of their data, especially the institutional knowledge that tends to go untouched due to its unstructured nature.
Run complex data workflows that not only extract data but also apply a higher order of operations to it that requires reasoning.
Historically these workloads are impossible to automate at the quality needed, forcing businesses to spend inefficient resources to run them manually. With AI it’s now possible to execute these workloads at scale using software, so businesses can unlock tremendous value and opportunities across their organization including:
Cost Cutting Measures: Regulatory compliance or manual processes that are critical to business operations and require substantial people to run.
Revenue Generating Opportunities: New data or automation features that improve product quality and customer experience.
Shifting From Basic to Higher-Order Operations
Contrary to basic operations which could be something as simple as calculating the sum or average of a dataset, higher-order operations may include:
Sentiment Analysis: Interpreting the tone or emotional context of data.
Forecasting: Ability to take existing or historic data to forecast future trends.
Insight Generation: Synthesize conclusions and insights from disparate pieces of information.
Entity Recognition: Identifying key entities (people, organizations, etc.) from unstructured data.
Inferring Relationships: Understanding how different data points relate to each other, such as cause-and-effect or correlations.
In essence, higher-order operations involve more sophisticated, knowledge-based actions that require a deeper understanding and interpretation of data to extract valuable insights. However, this is easier said than done.
Challenges with Higher-Order Tasks
The real challenge stems from the complexity of these workflows, where the input data is complex and each added layer of interpretation demands an additional level of reasoning. Unlike basic data processing, these workflows can be challenging to solve even with a staffed machine learning (ML) team on deck.
The reality is that it’s not just about cleaning or organizing data anymore. Modern enterprises are often tasked with sophisticated data operations, such as running sentiment analysis on customer feedback, assessing compliance risks from legal documents, or predicting maintenance needs in manufacturing. These tasks are multi-step processes that require advanced reasoning capabilities, and often require the user to derive new data from existing data sets - far from generic plug and play. Executing these tasks manually or with traditional ML / automation tools is not only difficult, but resource-intensive and prone to errors.
Let’s take a look at the two primary categorical challenges that teams typically face when having to develop a proper pipeline for these tasks.
Working with Complex Data
Data Quality and Integrity: Real-world data frequently contains missing values, inconsistencies, or errors, which can result in inaccurate analysis by the automated system.
Data Volume and Variety: These scenarios often involve large, heterogeneous datasets, including structured, unstructured, and semi-structured data. Wrangling such data at scale introduces a vast amount of additional complexities and steps.
Domain Expertise: Deep domain knowledge is often needed to accurately interpret patterns or relationships in the data. This requires a subject matter expert on staff that understands the domain or industry and has the technical expertise to navigate these nuances. Without context, there are high chances that you’ll misinterpret data and produce misleading insights.
Data Integration and Interoperability: Higher-order operations often require combining data from various sources, such as internal systems, third-party APIs, or external datasets. Ensuring interoperability and harmonization of these diverse data formats and structures is a challenge.
Performing Logical Reasoning Reliably
Causal Inference: Logical reasoning relies on understanding cause-and-effect, which is more complex than identifying correlations. Data alone may not reveal causal mechanisms, requiring engineers to use specialized techniques to infer and connect cause to effect. This is not only hard to do, but mistakes can reset the entire process.
Abstraction and Generalization: Logical reasoning requires abstracting from specific examples to formulate general principles. Machine learning models often overfit to the data it was trained on, making it hard to generalize to new situations. This requires engineers to design models that are capable of reasoning at a higher level of abstraction, to avoid overfitting while still capturing the essential underlying logic.
Multi-Step Reasoning: Many tasks require sequential steps, where each conclusion informs the next. Machine learning models are limited by their quantitative algorithms and are unable to generate intermediate reasoning.
Handling Exceptions and Edge Cases: Effective reasoning also requires the ability to account for exceptions, contradictions, and outliers, challenging simple models. Developing models capable of reasoning through these anomalies without failure, demands more intricate logical approaches than what standard pattern recognition and ML algorithms can offer.
These complex workflows are not only time-consuming, but it requires precision to ensure reliability and confidence in the outputs. More often than not, this requires dedicated resources with the right mix of expertise to help navigate the intersection between data and logic, which can be challenging even if you're fully staffed.
Data Reasoning Workflows: The Core of Intelligent Operations
As the demand for efficiency continues to rise, enterprises are and will be increasingly dependent on data reasoning workflows that require data to go through a higher order of operations to deliver value.
When we look at complex data workflows within enterprise that require a higher order of reasoning, some of the most common use cases can range from generating intelligence-driven insights to automating operational decisions or reconciling disparate sources of information, all of which rely on accessing as much data as possible and executing multiple levels of reasoning to generate meaningful output. For those who may be curious what this may look like in your own respective industry, here are a few examples that we’ve seen from some of our customers today.
Regulatory Compliance: Organizations must analyze contracts, legal texts, and compliance frameworks to ensure they meet local and global standards. This is a data reasoning workflow that involves understanding legal language and identifying potential compliance risks and vulnerabilities.
Quality Control and Monitoring: In industries like manufacturing, companies collect sensor data, as well as historical ticket data or recent work notes on the machinery. A data reasoning workflow might analyze all the sources of data together to detect anomalies, predict / triage equipment failures, and prevent costly downtime.
Customer Intelligence: Companies often collect customer data from various touchpoints. A data reasoning workflow here could leverage all structured and unstructured data to infer customer preferences and drive personalized marketing campaigns based on the findings.
These workflows play a critical role in driving value for businesses, but the complexity requires a tremendous amount of time, advanced tooling, and a staffed ML team to automate at scale. However for most businesses, these aren’t simple hurdles that can be overcome easily, which makes it extremely difficult for teams to scale and move quickly.
Introducing Gradient’s AI-Powered Data Reasoning Platform
To greatly simplify this process, Gradient has 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.
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.
