Business intelligence is born from a robust Modern Data Stack.
At Accuratio, we view Artificial Intelligence not as a standalone technology, but as the pinnacle of a well-orchestrated data ecosystem. Our AI value proposition is built on a cutting-edge Modern Data Stack (MDS), which guarantees the quality, accessibility, and governance of data—the essential pillars for building and scaling AI solutions that generate real business value. AI is only as smart as the data that feeds it, and our MDS ensures that data is optimal.
Imagine AI that doesn’t just assist your employees, but also takes on entire tasks, optimizing workflows and freeing up your team to focus on strategic initiatives. The potential to redefine industries is already here, and your company can be at the forefront.
The Technological Process and Added Value of the MDS for AI:
Our MDS architecture is designed to flow seamlessly, transforming raw data into actionable insights and predictive or prescriptive models:
Our Strategic Data Source for AI:
At Accuratio, in partnership with BlueYeti, we don’t just develop AI solutions; we also create the robust data infrastructure that supports them. Our MDS architecture, powered by tools like Snowflake, Fivetran, Dataiku, dbt, data.world, myota, ALTR, and Sigma, is the engine that transforms data into strategic intelligence. This enables our clients to make faster, more precise decisions, automate processes, and unlock unprecedented value from their data.
Snowflake: The Unified Platform for Data and AI at Scale
- Technological Process: Snowflake is the core of our data infrastructure. It is an elastic cloud data platform that unifies data warehousing and data lake capabilities (the data lakehouse). It stores and processes petabytes of structured, semi-structured, and unstructured data, scaling compute and storage independently.
- Added Value to Data: It provides the scalability and flexibility needed for AI workloads. It allows data scientists to quickly access large volumes of data, perform feature engineering, and train models without worrying about the underlying infrastructure. It is the foundation for unifying data for analytics, machine learning, and applications, breaking down silos and enabling a 360° view for AI.
Fivetran: Automated and Reliable Data Ingestion for AI Models
- Technological Process: Fivetran automates the connection and replication of data from various operational sources (CRMs, ERPs, application databases, logs, etc.) into our central data platform. It automatically handles schema changes and ensures high-fidelity ingestion.
- Added Value to Data: It ensures that data for AI is fresh, complete, and consistent. It drastically reduces data preparation time (data plumbing) and minimizes manual errors, freeing up data teams to focus on building and improving AI models. This guarantees that the “fuel” for AI is of the highest quality right from the source.
Dataiku: AI Lifecycle Orchestration and Collaboration
- Technological Process: Dataiku is our Machine Learning Ops (MLOps) and AI development platform. It allows multidisciplinary teams to collaborate on data preparation, model building (using autoML or coding), validation, deployment, and monitoring of AI models in production.
- Added Value to Data: It democratizes access to AI. It turns transformed data into predictive and prescriptive models, facilitating rapid development and iteration. It ensures that AI models are built on a solid data foundation and allows the outputs of AI to be easily integrated into business processes. It accelerates the transition from experimentation to production, maximizing the return on investment in AI.
dbt (data build tool): Transformation and Modeling for AI
- Technological Process: dbt allows data engineers and analysts to transform the raw data loaded by Fivetran into clean, consistent data models ready for analysis and machine learning. It defines SQL transformations as code, enabling versioning, testing, and documentation.
- Added Value to Data: It creates a single “source of truth” for AI data. The resulting datasets are validated, tested, and documented, which drastically improves the reliability of inputs for any AI model and accelerates the feature engineering process. This ensures that models learn from structured and coherent data.
data.world: Governance and Discovery for AI Reliability
- Technological Process: data.world serves as our data catalog and data governance platform. It allows us to document datasets, discover data sources, understand data lineage, and ensure regulatory compliance.
- Added Value to Data: This is fundamental for building trust in AI. It ensures that data scientists use the correct data and understand its context and limitations. It provides traceability for the data feeding AI models, which is crucial for explainability and auditing. It also mitigates biases by offering visibility into the origin and transformations of the data.
myota (Data Quality and Compliance): The Guardian of AI Integrity
- Technological Process: myota (assuming its role in quality and governance) implements rules to validate, cleanse, and monitor data quality throughout the MDS. It detects anomalies and errors before they can impact AI models.
- Added Value to Data: It provides continuous quality control, ensuring that data is accurate, complete, and consistent. AI critically depends on high-quality data; myota minimizes “garbage in, garbage out,” thereby increasing the reliability and performance of the models.
altr (Security and Access Control): Protecting Sensitive AI Data
- Technological Process: altr manages and enforces security and access control policies at the data level, ensuring that only authorized users and systems can access sensitive information, even when it’s used to train AI models.
- Added Value to Data: It protects the privacy and confidentiality of sensitive data used in AI. This is crucial for compliance with regulations like GDPR or CCPA, allowing organizations to harness the power of AI without compromising security or incurring legal risks.
Sigma: Visualizing and Acting on AI Results
- Technological Process: Sigma, a cloud-native BI platform, allows business users to explore, analyze, and visualize the outputs and predictions of AI models directly on the data within Snowflake.
- Added Value to Data: It closes the AI loop. It translates the complexity of the models into accessible and actionable business insights. It enables users to monitor model performance, understand their predictions, and make informed decisions, thus maximizing the impact of AI investments.