Data ontology is a branch of knowledge representation that deals with the organization of data and information. It aims to provide a common, standardized vocabulary and conceptual framework for describing and sharing data across different domains and applications. As a result, when properly applied, it can provide a powerful decision-making framework and empower organizations and more importantly individuals in the organizations to better understand the outcomes of their decision. It allows people to understand why certain decisions are made, who made them, and what were datasets used to make those decisions. It is not omniscience, but at minimum clairvoyance.
And if you are a Palantir (PLTR) shareholder, you probably heard the term ontology many times. The word “ontology” comes from the branch of philosophy known as metaphysics, which deals with the nature of existence and reality. In philosophy, ontology is the study of the nature of being, becoming, existence, or reality, as well as the categories, properties, and relations of entities. So what does that mean in relation to data?
The Core Tenet of Data Ontology
At its core, data ontology is concerned with the meaning and interpretation of data, as well as the relationships between different data entities. It allows for the formalization and representation of complex data concepts and their relationships, enabling a shared understanding and interpretation of the data by different stakeholders.
Answering questions about data can be a challenge and there are many questions worth knowing answers to such as: Where does this come from? Where does it go? Who submitted this? Who accessed it? Who can override it? Who can share it? and most importantly What does it mean?
In order to answer a specific question, all data within the system – including raw data, processed data, operational data, and outputs from computational models – is relevant. It’s essential to understand that data does not inherently have meaning; rather, it is given meaning by the users of the data ecosystem. While this may seem like a philosophical concept, it is actually a crucial factor in any effective data system.
Ontology is a way of organizing the world into categories. In Foundry, Ontology is a digital version of an organization, a rich semantic layer that sits on top of the digital assets (datasets and models) integrated into Foundry. The Foundry Ontology creates a complete picture of an organization’s world by connecting datasets and models to object types, properties, link types, and action types.
An ontology is a system for mapping data to meaningful semantic concepts. An effective ontology exists outside of the data itself, establishing a framework that enables data integration, application building, user collaboration, and many other functions. This effective ontology recognizes that data is neutral and does not have inherent meaning. While data may inform how the ontology is structured, the ontology itself should function independently of the data in the ecosystem. To better understand the role of ontology, it is helpful to consider what ontology does.
An ontology provides a map that connects data and meaning by defining what is meaningful. These meaningful things are the nouns, verbs, and adjectives of an organization. Each of these object classes would then need a definition in the ontology, along with other concepts that are connected together in a web of relationships. Each object class definition could have certain properties that describe it. It is the job of data scientists to create class definitions within the ontology so that they can be used to create objects that can be put into action.
Governance And Management
Data ontology is an important tool for data management, as it helps to improve the quality, interoperability, and accessibility of data. It allows for the standardization of data definitions and schemas, enabling different data sources to be integrated and shared more easily. This is particularly important in today’s increasingly interconnected and data-driven world, where organizations rely on the efficient exchange and analysis of large amounts of data to make informed decisions.
Data ontology also plays a crucial role in data governance, as it provides a common framework for defining and enforcing data policies and standards. This helps to ensure that data is properly managed, protected, and used in a responsible and ethical manner.
In summary, data ontology is a key enabler of data management, governance, and interoperability. It provides a common vocabulary and conceptual framework for describing and sharing data, helping to improve the quality and usefulness of the data, and enabling organizations to make more informed decisions.
Scale of Data Ontology
The Ontology is a shared source of truth that helps people make and record decisions in a large organization. It allows business users to easily find and understand data across the business, and to see how their decisions fit into the bigger picture. The Ontology is used not just to read data, but also to write data and record decisions.
Using standard data lakes can lead to a lot of complexity, as the number of datasets, dashboards, and applications grows. Over time, it becomes harder to understand what data is available, and new projects often end up “reinventing the wheel” instead of using or building on existing data.
In contrast, the Ontology provides a well-defined system for organizing data in a way that makes sense for the organization. With an Ontology, organizations can make the most of their data as it grows, and make it easier to manage. This helps them make digital transformations at scale, while controlling complexity.
The Ontology makes it easier and more efficient to build an operational platform by focusing on a single, reusable data asset that can be used for all analytical work and application development.
Instead of needing a separate data integration and data layer for each new project or use case, data integration is only needed for new data. Applications and use cases can be built on the existing Ontology, so that people working on them can focus on the business problem and user workflow, instead of on dealing with data.
Decisions And AI Capabilities
Ontology acts as an organization’s “digital twin”, helping to capture decisions being made and improve decision-making over time. It allows users to edit and add to the data that supports Ontology.
Capturing decisions in Ontology helps organizations learn from and improve their decision-making. It also allows the value of the data to grow over time, as insights from each one user can strengthen the organization and help others make better decisions.
Ontology makes it easier for data science and AI/ML teams to work with business and operational teams on a shared platform. Models (and their features) can be connected directly to the business processes that drive the organization. This allows models to be governed, released, and integrated into core applications and systems, without needing additional adapters or code, and to be used in-platform or externally. As decisions are made and actions are taken, operational and process data is written back into the Ontology, creating a feedback loop.
Foundry helps organizations quickly achieve their goals. Ontology and other tools make it easy to get started and deliver AI/ML-powered outcomes, whether through new applications or by enhancing existing systems. Subsequent projects can use interconnected datasets and model assets throughout the enterprise, reducing the time it takes to get value from new projects.
Why Does Ontology Matter?
Organizations are always complex, some complexity is necessary and useful, while another complexity is not. The key is to know the difference between the two types of complexity.
Optimum complexity is the right amount of complexity for your organization. It is what you need to achieve your goals, no more and no less. Unnecessary complexity is any complexity that goes beyond what you need. It is extra complexity that doesn’t help you reach your goals.
Many technology solutions try to make things too simple, or even ignore complexity altogether. This is unwise because organizations need software that helps them stay at the right level of complexity.
Most companies have invested a lot in technology over the past few decades, and these investments provide different levels of value. However, almost no organization has a platform that allows these investments to work together at scale.
As a result, point solutions create a jigsaw architecture that makes it hard for the organization to understand and manage the right level of complexity. This makes it hard for them to move towards it or even maintain it.
To operate at the right level of complexity, organizations need Ontology in order to:
- Achieve a common system and use their resources more efficiently;
- Untangle by getting rid of technology that is not adding value;
- Use the system to drive business outcomes;
- Improve over time by writing back information into the common system, so that it and the actions it supports can develop and improve;
- Lay a strong but flexible foundation that allows for stability and flexibility at the same time.
Through the mentioned properties Ontology is able to transform any enterprise.