Data modelling it's process and examples
- 1. Data Modeling • The process of creating a visual representation of either part of a system or the entire system to communicate connections between structures and data points using elements, texts, and symbols. • Data is changing the way the world functions. It can be a study about disease cures, a company’s revenue strategy, efficient building construction, or those targeted ads on your social media page; it is all due to data. • This data refers to information that is machine-readable as opposed to human-readable. For example, customer data is meaningless to a product team if they do not point to specific product purchases. Similarly, a marketing team will have no use of that same data if the IDs didn’t relate to specific price points during buying. • This is where Data Modeling comes in. It is the process that assigns relational rules to data. A Data Model un-complicates data into useful information that organizations can then use for decision-making and strategy. According to LinkedIn, it is the fastest-growing profession in the present job market.
- 2. Data Modeling • Data is changing the way the world functions. It can be a study about disease cures, a company’s revenue strategy, efficient building construction, or those targeted ads on your social media page; it is all due to data. • This data refers to information that is machine-readable as opposed to human-readable. For example, customer data is meaningless to a product team if they do not point to specific product purchases. Similarly, a marketing team will have no use of that same data if the IDs didn’t relate to specific price points during buying. • This is where Data Modeling comes in. It is the process that assigns relational rules to data. A Data Model un-complicates data into useful information that organizations can then use for decision-making and strategy. According to LinkedIn, it is the fastest-growing profession in the present job market.
- 3. What is a Data Model? • Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. • In order for data to allow this measuring, it has to be organized through data description, data semantics, and consistency constraints of data. • A Data Model is this abstract model that allows the further building of conceptual models and to set relationships between data items. • An organization may have a huge data repository; however, if there is no standard to ensure the basic accuracy and interpretability of that data, then it is of no use. • A proper data model certifies actionable downstream results, knowledge of best practices regarding the data, and the best tools to access it.
- 4. What is Data Modeling? • Data Modeling in software engineering is the process of simplifying the diagram or data model of a software system by applying certain formal techniques. • It involves expressing data and information through text and symbols. The data model provides the blueprint for building a new database or reengineering legacy applications. • In the light of the above, it is the first critical step in defining the structure of available data. • Data Modeling is the process of creating data models by which data associations and constraints are described and eventually coded to reuse. • It conceptually represents data with diagrams, symbols, or text to visualize the interrelation. • Data Modeling thus helps to increase consistency in naming, rules, semantics, and security. • This, in turn, improves data analytics. The emphasis is on the need for availability and organization of data, independent of the manner of its application.
- 5. Data Modeling Process • The first step in the data modeling process is identifying the use cases and logical data models. • Then create a preliminary cost estimation. Identify the data access patterns and technical requirements. • Create DynamoDB data model and queries. Validate the model and review the cost estimation.
- 6. Data Modeling Examples • The best way to picture a data model is to think about a building plan of an architect. • An architectural building plan assists in putting up all subsequent conceptual models, and so does a data model. • These data modeling examples will clarify how data models and the process of data modeling highlights essential data and the way to arrange it.
- 7. 1. ER (Entity-Relationship) Model • This model is based on the notion of real-world entities and relationships among them. • It creates an entity set, relationship set, general attributes, and constraints. • Here, an entity is a real-world object; for instance, an employee is an entity in an employee database. • An attribute is a property with value, and entity sets share attributes of identical value. Finally, there is the relationship between entities.
- 8. 2. Hierarchical Model • This data model arranges the data in the form of a tree with one root, to which other data is connected. • The hierarchy begins with the root and extends like a tree. • This model effectively explains several real-time relationships with a single one-to-many relationship between two different kinds of data. • For example, one supermarket can have different departments and many aisles. • Thus, the ‘root’ node supermarket will have two ‘child’ nodes of (1) Pantry, (2) Packaged Food.
- 9. 3. Network Model • This database model enables many-to-many relationships among the connected nodes. • The data is arranged in a graph-like structure, and here ‘child’ nodes can have multiple ‘parent’ nodes. • The parent nodes are known as owners, and the child nodes are called members.
- 10. 4. Relational Model • This popular data model example arranges the data into tables. • The tables have columns and rows, each cataloging an attribute present in the entity. • It makes relationships between data points easy to identify. • For example, e-commerce websites can process purchases and track inventory using the relational model.
- 11. 5. Object-Oriented Database Model • This data model defines a database as an object collection, or recyclable software components, with related methods and features. • For instance, architectural and engineering real-time systems used in 3D modeling use this data modeling process.
- 12. 6. Object-Relational Model • This model is a combination of an object-oriented database model and a relational database model. • Therefore, it blends the advanced functionalities of the object- oriented model with the ease of the relational data model. • The data modeling process helps organizations to become more data- driven. • This starts with cleaning and modeling data.
- 13. Types of Data Modeling • There are three main types of data models that organizations use. • These are produced during the course of planning a project in analytics. • They range from abstract to discrete specifications, involve contributions from a distinct subset of stakeholders, and serve different purposes.
- 14. 1. Conceptual Model • It is a visual representation of database concepts and the relationships between them identifying the high-level user view of data. • Rather than the details of the database itself, it focuses on establishing entities, characteristics of an entity, and relationships between them.
- 15. 2. Logical Model • This model further defines the structure of the data entities and their relationships. • Usually, a logical data model is used for a specific project since the purpose is to develop a technical map of rules and data structures.
- 16. 3. Physical Model • This is a schema or framework defining how data is physically stored in a database. It is used for database-specific modeling where the columns include exact types and attributes. A physical model designs the internal schema. The purpose is the actual implementation of the database. • The logical vs. physical data model is characterized by the fact that the logical model describes the data to a great extent, but it does not take part in implementing the database, which a physical model does. • In other words, the logical data model is the basis for developing the physical model, which gives an abstraction of the database and helps to generate the schema. • The conceptual data modeling examples can be found in employee management systems, simple order management, hotel reservation, etc. • These examples show that this particular data model is used to communicate and define the business requirements of the database and to present concepts. It is not meant to be technical but simple.
- 17. Data Modelling Techniques • There are three basic data modeling techniques. • First, there is the Entity-Relationship Diagram or ERD technique for modeling and the design of relational or traditional databases. • Second, the UML or Unified Modeling Language Class Diagrams is a standardized family of notations for modeling and design of information systems. • Finally, the third is Data Dictionary modeling technique where tabular definition or representation of data assets is done.
- 18. Data Modeling Tools • We have seen that data modeling is the process of applying certain techniques and methodologies to the data in order to convert it to a useful form. • This is done through Data Modeling tools which assists in creating a database structure from diagrammatic drawings. • It makes connecting data easier and forms a perfect data structure according to requirement.
- 19. Importance of Data Modeling • It is clear by now that data modeling is necessary foundational work. It allows data to be easily stored in a database and positively impacts data analytics. • It is critical for data management, data governance, and data intelligence. • It means better documentation of data sources, higher quality and clearer scope of data use with faster performance and few errors. • From the regulatory compliance view, data modeling ensures that an organization adheres to governmental laws and applicable industry regulations. • It empowers employees to make data-driven decisions and strategies. • It builds on business intelligence as it allows the identification of new opportunities by expanding data capability.
- 20. Uses of Data Modelling and Analytics Better Quality of Applications • The first and foremost benefit of data modeling and analytics is the ability to generate higher-quality applications that are stable and less error-prone, reducing application crashes and in turn reducing maintenance efforts. • Users usually create applications without the use of a data modeling process which results in the following consequences: • The raw data and user information is directly stuffed into the variables. • These variables are manipulated throughout the course of codes and create newer values based on the initial variables. • This process continues and finally, it becomes impossible to revert back. • The size of an organization doesn’t matter when the code is written without a proper structure. • Without structure, the code becomes a mess that cannot be solved. This also reduces the options for updates and modification to the existing codes since it is highly tangled and difficult to understand.
- 21. Uses of Data Modelling and Analytics Better Requirement Analysis for Application development • Data Modeling helps is creating and gathering the tangible information that enterprises could rely on. • The data model deals with the collection of data and the requirements for creating the applications. Having a proper requirement documented and formatted reduces the misinterpretations and reduces the efforts to analyze the requirements. • Data Modeling and analytics allow for proper focus on the compartmentalized efforts of each team toward the application. • It also employs the use of jargon in the model, which is forwarded into the development phase of the application. Data Modeling and analytics help in creating a more competitive and sophisticated product that meets the customer requirements a lot better. • This also means that the results of analytics performed on the requirements data are a lot better interpreted.
- 22. Uses of Data Modelling and Analytics Better Risk Management for Application • Performing Data Modeling and analytics on an existing ideology about a product helps in understanding and mitigating any foreseen risk associated with it in the development phase. • This helps in structuring and planning newer methodologies that would reduce the risk of applications being released in the market among the target audiences. • This Data modeling and analytics also help in calculating the complexity of the procedural methodology to be applied in the development phase of the product. • This enables the developers to take a simpler but effective path reducing the cost, efforts, and risk (of failure and incompatibility) while developing the product.
- 23. Uses of Data Modelling and Analytics • Reduced Time for Application development • Data modeling plays an important part in the development process of a new application. This directly impacts the cost and time associated with the application. If a proper data model is made before the process of development starts, it reduces the time required for the requirement gathering, planning on the go, and errors caused due to it. • Creating a data model helps in changes further down the lifecycle. For instance, when there is a requirement to add new tables to the program you can directly add them to the data model and update the existing program without major confusion and structural imbalances. • If there is no data model, the team would need to update the database as well as code which is time-consuming since there is no structure and the consequences of each change would need to be managed. And in case there are multiple changes spread across the code base, it is a very difficult task to maintain consistency and robustness in code.
- 24. Uses of Data Modelling and Analytics • Early detection of errors and data incompatibility issues • Usually, in a program where there is no proper data modeling and analytics, the errors in data are not found until the program is executing. When the user uses the application and an error message pops up regarding the bad data, this means that the data was bad from the start, and since no data modeling and analytics were performed on it, it was impossible to detect these errors in the testing phase of the applications. The earlier detection helps in solving it before it brings a negative impact on the application and its users. • Data Modeling and Analytics give an accurate view of the user interactions with the application and business data, even the minute details like which specific parts are accessed and how it is used by them. This allows employing corrections to the critical parts that are found by the information provided by data modeling and analytics. The data model audits also enable you to find the optimizations that will benefit the users the most.
- 25. Uses of Data Modelling and Analytics • Better performance of the application • Data modeling and analytics not only save money but also makes the applications run faster and more efficiently. Data modeling impacts the performance of applications by charting a plan that determines the usage of data by the application. This enables the developers to know the kind of data and the storage locations of the data as well. This enables them to write efficient codes to retrieve the data quickly. • Using unstructured and unorganized data from tables causes the developers to write more SQL queries to just figure out the location of data. Through data modeling and analytics, the data is structured into tables which enables the finding of the desired information. This makes applications run faster without slowing down for large amounts of data processing.
- 26. Uses of Data Modelling and Analytics • Documentation for future maintenance and support • Data Model allows you to find the relationships between different entities and processes. Data modeling and analytics are used to define all these entity relationships at a single location for easier access resulting in easier maintenance of the processes. • Data modeling and analytics also help in documenting the application’s design and business requirements. Being a single source it becomes easier to understand by all the teams removing any changes that occur due to the transmission of information. Also, all the changes and implementations can be monitored efficiently, Data modeling and analytics require expertise but the benefits are higher.