The concept of “Ontology”

An ontology focuses on the relations between concepts, data and entities.

It does this by representing the characteristics of a domain (i.e. a strategic business activity of a company) and how they are related, by defining a set of concepts, categories and relationships that describe the subject. It allows to relate, understand and “bring together” content based on the information in it. For example, an adequate enterprise ontology can help retrieving “hidden” information, or correlations, typically lost in large data lakes.

More information is available in this Wikipedia article: “Ontology (information science)“.

The concept of “Enterprise Semantics” and “Semantic Layer”

A semantic exploration methodology is used to allow the correlation of concepts and to extend data discoverability within a company. What is a semantic layer? In essence:

• The approach on how to categorize, classify and structure the knowledge that is important to the company
• An overview of the business processes of the company

A good introduction to the topic is available in this article “What is a Semantic Architecture and How do I Build One?“, which starts with this introduction:

Can you access the bulk of your organization’s data through simple search or navigation using common business terms? If so, your organization may be one of the few that is reaping the benefits of a semantic data layer. A semantic layer provides the enterprise with the flexibility to capture, store, and represent simple business terms and context as a layer sitting above complex data. This is why most of our clients typically give this architectural layer an internal nickname, referring to it as “The Brain,”  “The Hub,” “The Network,” “Our Universe,” and so forth. 

As such, before delving deep into the architecture, it is important to align on and understand what we mean by a semantic layer and its foundational ability to solve business and traditional data management challenges. In this article, I will share EK’s experience designing and building semantic data layers for the enterprise, the key considerations and potential challenges to look out for, and also outline effective practices to optimize, scale, and gain the utmost business value a semantic model provides to an organization.

and provides this example how a semantic layer provides business value to an organization:

Organizations have been successfully utilizing data lakes and data warehouses in order to unify enterprise data in a shared space. A semantic data layer delivers the best value for enterprises that are looking to support the growing consumers of big data, business users, by adding the “meaning” or “business knowledge” behind their data as an additional layer of abstraction or as a bridge between complex data assets and front-end applications such as enterprise search, business analytics and BI dashboards, chatbots, natural language process etc. For instance, if you ask a non-semantic chatbot, “what is our profit?” and it recites the definition of “profit” from the dictionary, it does not have a semantic understanding or context of your business language and what you mean by “our profit.” A chatbot built on a semantic layer would instead respond with something like a list of revenue generated per year and the respective percentage of your organization’s profit margins.

With a semantic layer as part of an organization’s Enterprise Architecture (EA), the enterprise will be able to realize the following key business benefits:

• Bringing Business Users Closer to Data:
  Business users and leadership are closer to data and can independently derive meaningful information and facts to gain insights from large data sources without the technical skills required to query, cleanup, and transform large data.
• Data Processing: 
  Greater flexibility to quickly modify and improve data flows in a way that is aligned to business needs and the ability to support future business questions and needs that are currently unknown (by traversing your knowledge graph in real time). 
• Data Governance: 
  Unification and interoperability of data across the enterprise minimizes the risk and cost associated with migration or duplication efforts to analyze the relationships between various data sources. 
• Machine Learning (ML) and Artificial Intelligence (AI):  
  Serves as the source of truth for providing definition of the business data to machines and enabling the foundation for deep learning and analytics to help the business answer or predict business challenges.

What’s the difference to “Enterprise Knowledge Graph” / “Knowledge Management”?

An enterprise knowledge graph is a representation of an organization’s knowledge domain and artifacts that is understood by both humans and machines. It is a collection of references to your organization’s knowledge assets, content, and data that leverages a data model to describe the people, places, and things and how they are related.

semontvis includes the concept of the “Entreprise Knowledge Graph”, by applying the above mentioned concept of ontology.

More information is available in this article: “What is an Enterprise Knowledge Graph and Why Do I Want One?“.

The concept of “System of systems“

Any organisation is a system of systems and has multiple systems at its core. The “System of systems” theory guarantees that all the elements are working and interacting with each other = the interconnectivity between these elements is in place.

More information is available in this Wikipedia article: “System of systems“.

Why is access to your confidential data not needed?

First, we transform your data to the standardized data model of semontvis. Thereafter we apply the rules and relations. Their results are used, which never contain confidential data.

What does “tailored“ imply?

The business processes and data sources in scope need to be understood in detail. They are the fundament for the installation of semontvis.  The main actors involved for a lean and successful implementation are:

• Business analyst/s
• IT architect / IT system engineer
• Developers
• Project manager

What is “data mining”?

As elaborated in this Wikipedia article “Data mining“, data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the “knowledge discovery in databases /KDD“ process.

The term “data mining“ is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.