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Overview#

In this set of tutorials, we will walk you through the data modeling lifecycle and how NEAT can assist in the different tasks involved in building and maintaining data models. But first, let's understand what data modeling is and why it is important.

Ultimately, the goal of data modeling is to use data to aid decision-making. To achieve this, a company needs to coordinate between multiple different people and departments. A set of data models can be used as the common language to share information and provide context for data such as timeseries and documents. The data models can then be used as the basis for multiple different applications/solutions/reports that are used to make decisions. Simply put: Data modeling is cooperation.

In this set of tutorials, we will focus on a fictions company, Acme Corporation, that works in the Power & Utilities industry. You don't need to know anything about the Power & Utilities industry to follow along. The tutorials will provide you with all the information you need to understand the domain and the data model that we will build. The tutorials will focus on all parts of the data modeling lifecycle process, from gathering information, taking advantage of existing standards, building an enterprise model, build models for specific use cases, extending existing data models. You can follow the tutorial in any order. In addition, you can use User Personas as a lookup to get the structure of the Acme Corporation and the user personas that will be used in the tutorials. Note the tutorials covers a large spectrum of tasks and roles, which includes many concepts. It is not intended that a single person should be able to do all the tasks, but rather that different people with different roles can work together to achieve the goal of building a data model.

  • Knowledge Acquisition: In this tutorial, you will learn how to gather information about the business requirements and the data sources that will be used to build the data model from the domain experts, crafting an enterprise data model that represents the business requirements and the data sources. The enterprise data model is fine-tuned by the solution architects to ensure that it technically aligns with the organization's data infrastructure, such as Cognite Data Fusion.

  • Analytic Solution: In this tutorial, you will learn how to build a solution model for a forecasting use case. The solution model is using a subset of the enterprise model and, in addition, adds new concepts that are needed for the forecasting use case.

  • Extending Enterprise Model: In this tutorial, you will learn how to extend the enterprise model while keeping the existing model intact. In this tutorial, we will add new concepts discovered during the implementation of the forecasting use case.

  • Extending Solution Model: In this tutorial, you will learn how to extend the solution model while keeping the existing model intact. In this tutorial, we will add new concepts discovered during the implementation of the forecasting use case.

  • Business Solution Model: In this tutorial, you will learn how to use the Enterprise Model to build a Solution Model for a business case. This data model will only use a subset of the Enterprise Model and not add any new concepts. It is intended for a business user that only needs to select the part of the Enterprise Model that is relevant for their business case.