Data Management Glossary
Artificial Intelligence (AI)
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to perform tasks that would typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI involves the development of computer systems capable of performing these tasks.
AI subfields
AI subfields employ different techniques and algorithms to enable machines to learn from data, recognize patterns, make predictions, and solve complex problems. Examples include:
- Machine learning: a prominent branch of AI, focuses on enabling machines to learn from and adapt to data without explicit programming. It involves the development of algorithms that allow computers to analyze and interpret large volumes of data, identify patterns, and make informed decisions or predictions.
- Natural language processing (NLP): Deals with enabling machines to understand, interpret, and generate human language. NLP plays a crucial role in applications such as speech recognition, language translation, chatbots, and text analysis.
- Computer vision: Involves enabling machines to interpret and understand visual information from images or videos. It enables systems to perceive and analyze visual data, such as object recognition, image classification, and autonomous driving.
- Robotics, expert systems and more.
AI has a wide range of applications across various industries, including finance, healthcare, transportation, manufacturing and entertainment. It has the potential to revolutionize industries, improve efficiency, automate processes, and solve complex problems.
AI is still an evolving field, and while it has made significant advancements, it is not yet capable of replicating the full spectrum of human intelligence. Researchers and developers continue to explore and push the boundaries of AI, striving to create more advanced and sophisticated systems. There is an ongoing discussion about the important role of regulation and governance, especially as they relate to generative AI. The leaders of OpenAI have proposed an international regulatory body.
At the end of 2022, Komprise CEO Kumar Goswami wrote about the importance of unstructured data and unstructured data management to AI and machine learning. He wrote:
Enterprises need to be ready for this wave of change and it starts by getting unstructured data prepped, as this data is the critical ingredient for AI/ML. This entails new data management strategies which create automated ways to index, segment, curate, tag and move unstructured data continuously to feed AI and ML tools. Unforeseen changes to society, fueled by AI, are coming soon and you don’t want to be caught flat-footed.
In 2023 he wrote an article entitled: The AI/ML Revolution: Data Management Must Evolve.
Why do most AI projects struggle with enterprise data?
Most AI projects fail to scale because enterprise data is not ready for use. It is scattered across file shares, cloud storage, and applications, often without context or consistency.
AI models perform well in controlled environments, but in production they depend on data that is:
- hard to locate
- poorly labeled
- inconsistent across sources
The gap between available data and usable data is one of the biggest barriers to successful AI adoption.
What role does context play in AI performance?
Context is what makes AI useful.
Unstructured data, such as documents, images, logs, and conversations, contains the business and operational context that AI systems rely on to generate accurate outputs. Without metadata and enrichment, that context is lost.
Improving AI performance is less about adding more data and more about ensuring data has:
- meaning
- relevance
- traceability
This is why metadata and data preparation are increasingly central to AI strategies.
Why is managing unstructured data different from traditional data management for AI?
Traditional data management focuses on structured datasets with defined schemas. AI, however, depends heavily on unstructured data, which is far more complex.
Unstructured data:
- does not follow consistent formats
- grows rapidly across distributed environments
- requires interpretation before it can be used
This makes it difficult to apply traditional ETL or data warehouse approaches. AI requires a more flexible, metadata-driven model that can operate across diverse data types and storage systems.See unstructured data metadata management.
How are enterprises evolving their data strategies for AI?
Organizations are shifting from storing data to activating it for AI and analytics.
This includes:
- analyzing data globally before moving or copying it
- prioritizing high-value datasets over bulk ingestion
- embedding governance and security into data workflows
- automating how data is prepared and delivered
The focus is moving toward building a continuous data pipeline for AI, rather than one-time data preparation projects.
How does Komprise support AI without requiring data consolidation?
Komprise enables organizations to work with unstructured data where it lives, without requiring large-scale migrations or centralized data lakes.
Using a global metadata layer (Global Metadatabase) and automated workflows (Smart Data Workflows), Komprise allows teams to:
- analyze data across silos
- selectively prepare and deliver the right datasets for AI
- enrich data with context at scale (see KAPPA data services)
- control costs and reduce unnecessary data movement
This approach helps organizations accelerate AI initiatives while maintaining flexibility across hybrid environments.