Creating an ontology for Analytics is an uncommon practice. Few companies have a space in their data where the concepts that permeate their business are explicitly defined and categorized for all employees.

But before that, let's define:

What is an ontology?

An ontology is a formal specification that provides a representation of shareable and reusable knowledge. Examples of ontologies include:

• Taxonomies
• Vocabularies
• Thesaurus
• Topic maps
• Logical models

ontology specification includes descriptions of concepts and properties in a domain , relationships between concepts, constraints on how relationships can be used, and individuals as members of concepts.

Ontologies for Business Analysis course , an article that is always incomplete and constantly being updated.

Onthology: a formal, explicit specification of a shared conceptualization. – Studer et al., 1997

In my translation:

Ontology is a formal and shared conceptualization of an explicit specification. – Studer et al., 1997

Analyzing this sentence in relation to the theme, we can say that an ontology is formal because it can be read by computers, and it is explicitly specified due to its precise representation derived from a consensus about a particular subject, that is, a shared conceptualization.

The Structure of an Ontology

Instances : individual occurrences (instances)

Taxonomy : classification technique

Inheritance : a classification where the elements of a class inherit the characteristics of their "parents".

Universe (or Domain) of Discourse

The domain of discourse, also called the universe of discourse or domain of quantification, is an analytical tool used in deductive logic, especially in predicate logic.

It indicates the relevant set of entities to which the quantifiers refer.

The term "universe of discourse" generally refers to the complete set of terms used in a specific discourse, that is, the family of semantic or linguistic terms that are specific to a certain area of ​​interest.

In Model Theory, the term "universe of discourse" refers to the set of entities on which a model is based. A database is a model of some aspect of an organization's reality. It is conventional to call this reality the "universe of discourse" or "domain of discourse."

What are ontologies from a business perspective?

Ontologies are " information models ," and they can help us overcome several of the challenges of domain models with loose interpretations—making ontologies a great addition to the business analyst's toolkit.

Ontologies allow us to explicitly define the structure of concepts , their relationships , as well as the axioms* that govern their interpretation within a universe of discourse.

There is a connection between ontologies and mathematical logic, as well as formal representation – used in defining models that can be encoded and recognized by computers.

Representation languages ​​are fundamental to modeling ontologies. These languages ​​can be graphical and/or formal in nature.

Ontologies can be modeled as lightweight or heavyweight representations, depending on the level of formality required. Visual and less formal models fall on the lightweight spectrum, while models that are encoded using expressive knowledge ​​are considered heavyweight.

Ontologies have witnessed a growing interest over the last decade.

But there is still some way to go before the technology business analytics is one of the areas where the benefits should be visible.

linguistic system or theory , a formula that is presumed to be correct, although not susceptible to demonstration.

What are the benefits of developing and using an ontology?

From a business perspective, especially in a market where technical terms are widely used, ontologies are very useful because they allow for the generation of a common understanding of specific sets of information.

This factor alone makes the creation of ontologies for business analysis fundamental, as it eliminates assumptions and makes explicit the definitions and relationships between the entities of your business, supporting all areas involved.

A shared understanding of explicit information and premises about a semantic domain is valuable in itself.

Ontologies , by supporting data for analysis and applying domain knowledge data , facilitate the interoperability of data model-driven applications.

They therefore reduce development time and cost and improve data quality through the creation of metadata (data about data) and a kind of relational "family tree".

A double case study of how the use of anthologies generated more traffic for two websites!

Automating data insights with Ontology

In the process of managing a high-performance business, it's necessary to automate processes. Analysis and decision-making processes are rarely automated, largely due to the difficulty in standardizing data entry, interpretation, and insight generation.

Creating an ontology for your business allows you to create semantic repositories, which use these ontologies as schemas to make sense of the data.

This makes automated reasoning about the data possible and easy to implement, since the most essential relationships between concepts are incorporated into the ontology.

Ontologies, Structured Data, and SEO

When we create an ontology for business analysis, we connect data to each other by structuring their connections . Having information about your products , services, and field of activity makes the systems that depend on this information use the connected data you created, using you as a source.

Among these services, we have Google !

Strategies on how Content Modeling, in conjunction with data structuring, will be the future step for SEO.

But what is Content Modeling and how does it help SEO?

Content Modeling helps define how your content will be organized and divided so that it can be managed and used across all the environments in which you operate.

Dividing all the content your organization produces into common elements and distributing access to the creators makes access to and use of this content free from platforms, programs, and access protocols.

There is a training process for content creators, who must be aware that everything they create will be deconstructed and "moved" by a technology system, to be applied across various platforms, by the Content Model manager.

How does Content Modeling work?

Content engineering fills any gaps that may exist between strategy and content development.

Working with content strategy and engineering transforms your content, which is currently static and dependent on platforms and programs, into an independent, intelligent, structured, and optimized format. Everything you need to be loved by search engines!

Structured content is the SEO of the future!

Structuring content with the help of Content Modeling allows your company to adapt to changes in your market, the creation of new products or services, design changes, or new platforms and social networks, for example.

Most large companies already use some form of content strategy and operations, even if they don't use that name. But small and micro-enterprises may feel they don't need to worry about it or may not have the resources to use content management through Content Modeling.

But that's not true. Decentralized content modeling saves resources for companies that have money to invest. Small and micro-enterprises can also invest to gain control of their content and avoid rework, saving money and time in creating their promotional and marketing .

And on top of that, by using structured data with content modeling, your SEO project will elevate your company far beyond the competition!

And that's why creating an ontology for business analytics will help build the next level of your SEO.