Use Cases

Innovation Amplified: Artificial Intelligence and the Future of Data

Join the Cutting Edge of Data-Driven Decision-Making with Powerful AI Solutions Built to Meet Your Organizational Goals

The AI revolution has arrived, and artificial intelligence is quickly reshaping the ways we perceive and leverage data. Like everything else about AI, the applications of emerging  tools and technologies seem limitless, necessitating a paradigm shift in our data systems and practices to make way for the next big wave of innovation. 

Whether it’s forecasting future trends with predictive analytics, streamlining data processing and improving data quality with AI-driven automation, or unlocking unstructured data with AI’s impressive natural language processing capabilities, the integration of AI in data practices holds the key to unlocking efficiencies, uncovering insights, and propelling organizations into a data-driven future.

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improvement in data metric discovery

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improvement in report rationalization

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improvement in business analysis and ETL discovery

Data Cleaning and Preprocessing

  • Anomaly detection Identify errors, gaps, or inconsistencies in text data.

  • Text normalization Convert different forms of text into a standard format, such as changing abbreviations or synonyms to a standard term.

  • Semantic mapping Translate free-text terms to standard codes or vocabularies.

Data Cataloging

  • Automatic tagging Use NLP to automatically tag or label datasets based on their content.

  • Metadata extraction Extract metadata from unstructured data sources automatically.

  • Dataset summarization Generate brief summaries or overviews of large datasets.

Querying and Access

  • Natural language querying allows users to search databases using natural language queries instead of SQL or other formal querying languages.

  • Data exploration Assist in uncovering patterns, correlations, and insights from vast datasets through semantic understanding.

 

ETL (Extract, Transform, Load) Processes

  • Text extraction Extract structured information from unstructured or semi-structured sources, such as scraping text data from web pages or extracting entities from documents.

  • Transformation logic explanation Convert complex transformation logic into human-understandable descriptions or visualizations.

Documentation and Metadata Generation

  • Auto-documentation Automatically generate documentation for databases, data lakes, or pipelines.

  • Sentiment analysis On feedback about data products or tools, to understand user sentiment and improve systems accordingly.

Schema Matching and Integration

  • Entity matching Identify and link the same entities across different datasets.

  • Semantic schema matching Link database schemas based on semantics rather than just structural or syntactic similarity.

Data Governance and Compliance

  • Sensitive data detection Identify sensitive information in datasets, such as Personal Identifiable Information (PII), to ensure compliance.

  • Policy enforcement Check datasets and data operations against natural language policies to ensure adherence.

Knowledge Graphs and Ontology Creation

  • Entity and relationship extraction Populate knowledge graphs by extracting entities and their relationships from text.

  • Ontology augmentation Expand or refine existing ontologies based on new textual information.

Automated Data Lineage Discovery

  • Trace data origins Understand where data comes from and its journey through various systems.

  • Explain data lineage Convert complex data lineage graphs into human-understandable narratives.

How it works

Strategic Planning

Evaluate your current technology, align on your goals, and develop a custom, step-by-step roadmap to strategically implement AI solutions.

Custom AI Solutions

Build bespoke AI models and solutions tailored to your business requirements and industry-specific challenges.

Seamless Integration

Weave cutting edge AI solutions into your existing systems, ensuring that they work in harmony with current technologies and don't disrupt current workflows.

Thought Leadership

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