While reading about data science, it is very likely that you will come across phrases like “data is the new oil”, or “data is the new gold”. Just to give you a rough estimate, currently, 5 billion searches are carried out every day of which 3.5 Billion are performed just on Google. It is 40,000 searches every second. Not only this, but we create 2.5 quintillion bytes of data each day, and is likely to increase soon. In the last 2 years, 90% of all the data were created.
Considering the importance of data, I am writing this article on one of the most important aspects of data science i.e. data modeling.
The phrase by Douglas Merrill “Big data isn’t about bits, it’s about talent” is as true for data modeling as it is for big data. With so much information present, it is essential for organizations & enterprises to use data in the right way for efficient and effective analysis.
However, just like data cleaning, data modeling is an important aspect of data science, since organizing the data into random relationships or structures hardly helps. To get the most from any data, it is crucial for data scientists to model it correctly.
Decoding Data Modeling!!
It is a set of methods or techniques used for converting data into a useful form. With data modeling, you can convert complex software design into easy-to-understand diagrams.
In layman’s terms, it is a process where you structurally store data in a format in a database. It is done by using data modeling tools that help in drawing diagrams for easy understanding of data.
Data modeling becomes important because it helps in making effective data driven-decisions to meet the desired goal. It might sound easy from this but in reality, it is a complex process requiring you to have a complete understanding of the organization’s structure & then offering a suitable solution that meets the goal & objectives.
What are the key steps involved in data modeling?
- Identifying the objects of business or an entity represented in a data set.
- For each entity, finding the key properties so that you can differentiate them in the data model.
- Creating or drawing a rough draft of the entity-relationship model. It is done for showing the relationship b/w entities.
- Finding out all the attributes that have to be incorporated into the data model.
- In order to properly reflect the meaning of the data for the business, you have to map the attributes to entities.
- Lastly, finalizing & validating the data model. Refining is also required to keep the data up to date as it regularly changes with time.
What are the importance of data modeling?
- It gives a clear representation of the data. This helps in easily analyzing the data, and also gives an overview of the data so that it can be further used for different applications.
- Proper representation of data in a model helps in ruling out any chances of omission or redundancy in the data. It is of great help when it comes to fast processing, clear analysis, and avoiding faulty reports or incorrect results.
- It also improves the quality of the data that allow respective individuals to make effective data-driven decisions with high accuracy.
- It gives a holistic understanding of the data for creating a physical database.
- Data models allow developers to portray business requirements effectively.
- Since it brings the data of a whole organization on the same platform, you can create a robust design. Again it could be of great help in identifying and removing duplicates, redundancy, & even finding the missing information or data.
- Good data modeling also provides consistent data for all the projects of any enterprise.
- You can even define stored procedures, relational tables, foreign or primary keys.
- It provides better quality management & scope to the project in charge. It also improves the overall performance to the core.
As data modeling is a significant step in data science, it is very important to choose the right modeling techniques for accurate results.
Also Read: Learn How Data Modelling Works in MongoDB?
What are the advantages of the data model?
- It is of great help for businesses for communication within the organizations.
- All the info within the data model can be used for defining the primary or foreign keys, r/p between tables, and stored procedures.
- It helps in an accurate representation of data objects.
- The data model can also be used for building physical databases.
- It can be of great help for recognizing the correct sources of data for populating the model.
- In the ETL process, a good data model can also help in documenting data mapping.
What are the disadvantages of the data model?
- In DBMS, there is an unavailability of the set data manipulation language.
- It is mandatory to modify the entire application even for the small changes done in the structure.
- It is compulsory for you to understand and know the characteristics of physically stored data for developing a data model.
Now, since we have a basic understanding of data modeling, let’s understand the types of data models or most commonly used data modeling methods.
What are the types of data models?
One can achieve data modeling in numerous ways, but the core concept behind each one of them will be the same. The most commonly used methods for data modeling are as follows:
#1. Hierarchical model
From the name, it will be easy for you to figure out that it uses a hierarchy for structuring the data. It is done in a tree-like format, but, it is a little difficult to access or retrieve data from a hierarchical database. This is the main reason, it is used less in current times.
#2. Relational model
It was proposed by an IBM researcher as an alternative to the hierarchical model. In these models, data are represented in the table form and greatly reduces the complexity for the developers. The relational model also gives a clear overview of the data.
#3. Object-oriented model
This model includes a collection of objects. Each object has its own methods and features. An object-oriented model is also known as a post-relational database.
#4. Network model
The network model is created from the inspiration taken from the hierarchical model. But contrary to the hierarchical model, you can convey complicated relationships without any hassle. It is done because, in this model, each record is linked with multiple parent records.
#5. Entity-relationship model
It is also called ER model and in this model, entities and their relationships are represented in a graphical format. Here an entity could be anything like an object, a concept, or even a piece of data.
What Are The Best Data Modeling Tools?
As data modeling is important for data science, data modeling tools are equally important for creating the appropriate models. Right tools can significantly help in creating a correct database structure from the diagrams. Because of this, connecting data and creating a suitable data structure as required becomes very easy.
Though there are numerous tools present for data modeling, we will list some of the most popular & commonly used tools. Most of them support Windows operating system, and some data modeling tools also support Linux & Mac operating systems. All these tools also support various databases & offer several features like forward or reverse engineering, documentation, data structure creation from the diagram, reporting, supporting multiple databases, import & export options, and so much more.
Some of the listed tools also give you the option to integrate with big data platforms like Hadoop Hive or MongoDB. You can also call these tools as big data modeling tools. So without wasting any time, let’s check out some of the best & widely-used data modeling tools.
1. SQL DBM
It is one of the popular tools used for data modeling, and it provides one of the easiest ways to design your database on any browser. It doesn’t require any other database engine or any modeling applications or database modeling tools.
Why to use SQL DBM?
- You can easily import an existing database schema.
- Easy & simple way to manage small or large databases and data models.
- You can use this data modeling tool to create ERD or a physical model of your database, and can also add tables, relationships, columns, and databases.
It is a cost-effective solution for a majority of modelers & enterprise architects. Archi data modeling tool provides description, analysis, and architecture visualization for the entire business domain.
Why to use Archi?
- You will get dynamic viewpoints for quickly changing the ArchiMate at any given time.
- You can brainstorm your ideas.
- It let you quickly and easily create all ArchiMate elements in ArchiMate views, and also allows you to edit or create your own canvas.
- With Archi, you can view the selected model element along with all its relationships with other model elements. It can be viewed in a radial-tree graphic.
- Archi also includes hints view for providing the right information at your fingertips.
3. Erwin Data Modeler
It is a tool for data modeling that anyone can use for creating physical, logical, and conceptual data models. You can also create an actual database from the physical model.
Why to use Erwin Data Modeler?
- Since it uses standards set for enterprise data, it is known for driving quality.
- You can model any data from anywhere.
- You can also automatically compare the database & the model.
- You will get a centralized model management.
- Erwin data modeler can help in collaborating with business & technical users.
- This tool provides the ability to extract data from ERP, CRM, and others for modeling with utmost accuracy.
- It includes a simple & easy to use graphical environment for the data modeler.
- Erwin data modeler lets you synchronize forward efficiently along with reverse engineering of definition code for the data.
It is an open-source tool that you can use for editing or creating database models. PgModeler includes an intuitive interface and also supports the creation of basic objects like a single column, functions, the user defines operators, and language.
Why to use PgModeler?
- It allows you to restore previous work. It is beneficial for situations where a failure occurs.
- You can automatically generate constraints and columns.
- Includes extra features that support translatable user interfaces and geospatial data types.
- A majority of its features are customizable.
- PgModeler uses a reverse engineering process. This helps in developing a reliable model based on the current database.
- You can use SQL scripts from synchronizing both database & model.
- You can also use a command-line interface or CLI for automating processes.
- With PgModeler, you get complete access to its source code along with the contents of any file that has been generated.
- You can also report models to PNG file, or SQL file, or even directly to PostgreSQL server.
- You will get a validation feature that lets you automatically create a database model without any errors.
5. Sparx Systems Enterprise Architect
It is a tool for designing a diagram that you can also use for modeling, building, maintaining object-oriented features, and documenting.
Why to use Sparx Systems Enterprise Architect?
Suitable for effective project management.
You will get end-to-end traceability.
Sparx Systems Enterprise Architect offers a powerful document generation.
With this data modeler, you will also get a model repository with the best performance.
6. Oracle SQL Developer Data Modeler
This data modeling software is known for increasing productivity & simplifying various tasks related to data modeling.
Why to use Oracle SQL Developer Data Modeler?
- It helps with editing, creating, multi-dimensional, relational & data type models.
- This data modeler also helps with collaborative development via source code control.
- You will get reverse as well as forward engineering capabilities.
- You can also use Oracle SQL Developer Data Modeler for cloud or traditional environments.
7. IBM InfoSphere Data Architect
It is another data modeling tool that accelerates and simplifies the data integration design for statistics & business intelligence. It can align applications, services, processes, and data architectures.
Why to use IBM InfoSphere Data Architect?
- IBM InfoSphere Data Architect helps you by providing easy & rapid development.
- It lets you create logical & physical data models.
- You can integrate with related products such as query workload tuner and data studio.
- In order to reduce time to market and enhance efficiency, IBM InfoSphere Data Architect helps you in understanding data assets.
- You will also get the advantage of integration & team collaboration.
- You can export or import custom mappings and manage source control via TFS or Microsoft Team Foundation Server.
- Thanks to the analyzing metadata feature of IBM InfoSphere Data Architect, you can automatically discover the structure of heterogeneous data sources.
It is another known name in data modeling tools. DbSchema is basically a visual database designer that allows you to manage and NoSQL, SQL & cloud database. With this tool, you can create comprehensive reports or documentation, synchronize the schema with the database, design or interact with the database schema, work offline, and do many other things.
Why to use DbSchema?
- It comes with relational data browse & has a visual query builder.
- It includes interactive layouts, schema synchronization, data loader & random data generator.
- DbSchema also has a SQL editor with auto-completion.
- There is HTML5 documentation, forms & reports as well.
9. Toad Data Modeler
It is a tool for data modeling that can maximize productivity by providing intuitive workflows, extensive automation, and built-in expertise. It is also known for providing the highest level of quality and manages code change.
Why to use Toad Data Modeler?
- It gives you powerful query tuning capabilities.
- For various servers & instances, you can easily execute scripts and T-SQL snippets.
- It let you access key data quickly for performing in-place analysis. You will also get a single-click export to an Excel instance.
- Toad data modeler automates all the repetitive processes such as schema and data comparisons.
- You can easily identify all the differences by just comparing and syncing data, schemas, and servers.
- You don’t have to restore a backup for rolling back transactions from the transaction log.
- With this data modeling software, you can perform tuning for app performance with automated query rewrites and optimization without any hassle.
- It also gives you performance health checks benchmarked from the community for free.
It is again a widely known data modeling software that is heavily used for documenting critical data elements, attributes, objects and interactions in data models. It helps you in defining business and conceptual processes representing the goal of the business.
Why to use ER/Studio?
- It helps you with sharing the data and models with the enterprise.
- You can implement naming standards to any attributes, models, and entities.
- It also has Safyr solutions for analyzing the complex structures of ERP or CRM software.
- ER/Studio lets you define a glossary team specific to the business area for reflecting the corporate structure and binding it to data models.
11. DeZign for Databases
With this data modeling tool, you can create a new database by visualizing your data structures and understand the existing database.
Why to use DeZign for Databases?
- This data modeling tool supports a wide range of techniques for data modeling.
- It provides a pan and zoom window for navigating a large diagram.
- You will get robust and easy-to-use data modeling tools.
- You can also visually make ERD or entity-relationship diagrams using this tool.
It supports architectural modeling language or ArchiMate and business process model & notation or BPMN. Since GenMyModel has a centralized repository model, it helps in simultaneously models collaboration with ease.
Why to use GenMyModel?
- You can share your versioning, knowledge, and access right with management.
- You can directly export or import as PDF documents.
- GenMyModel can combine modern web solutions with the power of desktop modeling tools.
- With GenMyModel, you can also give a meaningful presentation to project stakeholders and management.
This software for data modeling offers a wide range of add-ons related to business for creating data visualization, flowcharts, infographics, and diagrams for the business process model.
Why to use ConceptDraw?
- ConceptDraw Diagram comes with comprehensive and robust drawing tools.
- It let you use some of the most important business metrics, and also provides live object technology for tracking.
- This tool empowers your communication processes.
- It also enhances your presentation techniques.
14. Valentina Studio
This tool is widely used for creating and administering MySQL, SQLite databases, PostgreSQL & MariaDB for free.
Why to use Valentina Studio?
- You will get diagramming and schema editing.
- You can easily add or remove users, groups, run diagnostics and manage rights to see live logs.
- Without any difficulties, you will be able to manage SQL queries.
- You will also get local Valentina forms for working with your data.
- Moreover, this tool also lets you clone, dump, diagnose, reindex, defragment and compress data.
15. Software Ideas Modeler
Last but not least, Software Ideas Modeler is one of the smartest software for diagrams having modeling languages such as SysML, BPMN, UML, and ArchMate along with flowcharts, stories, and support for wireframes.
Why to use the Software Ideas Modeler?
- You can easily document your software.
- Simple layout and diagramming options.
- It provides you a UI diagram one of the best options for drawing drafts or mock-ups for your app wireframes and forms.
- You can also describe or identify use cases without any hassle.
What are the best data modeling practices?
Till now it will be clear about data modeling, its importance, and the popular tools used for creating data models, now, let’s check some of the best practices of data modeling that can greatly help in driving key business decisions!
#1. Keep everything as simple as possible, and don’t forget to scale with time
At first, everything will appear simple, but as you will progress, it won’t take time to get everything complicated & messy. It is highly recommended to keep the data models as simple & small as possible.
Once you are 100% confident about the accuracy of the initial data models, then you can accordingly introduce more datasets. It is beneficial in a couple of ways i.e. firstly, you will be easily able to find all the inconsistencies; and secondly, you can eliminate all of them altogether.
Things to remember:
Keep your data model as simple as possible. The best practice that you can choose is to pick the right tool that can help you to start small and scale as required.
#2. Always have a thorough understanding of your goal and end-results
You should remember that the main goal of any data model is to help businesses in their functioning. Being a data modeler, it’s your responsibility to understand the goal or objective. You can only achieve this by completely understanding the need of your organization correctly.
In order to prioritize or discard the data as per the situation, it is important for you to have a clear knowledge of all the needs of your organization.
Things to remember:
Always completely understand the need of your organization and organize your data accordingly.
#3. Always consider dimensions, facts, order & filters for organizing your data
Here’s an important tip for you! In order to find a majority of the answers for all the questions revolving around any business, it is best practice to always organize the data as per the 4 basic elements i.e. dimensions, facts, order & filters.
For instance, let’s take an example that you are asked to analyze the best store which made the most sales in the year-end among the 4 e-commerce stores run at 4 different locations by your company.
In such a case, you have to organize the data for last year. Here, the fact will be the all the sales data of last 1 year; the filter will be last 12 months; dimension will be the store location; and, the order will be the top stores as per sales in decreasing order.
This process will help in organizing all the data properly and will prepare you to answer all the questions related to business intelligence without any hassle.
Things to remember:
Organizing data properly is highly recommended. It can be done using individual tables for dimensions & facts for quick analysis.
#4. Keep whatever is necessary
It is very likely for you to have numerous reasons to keep as much data as possible, but always remember that it is just a TRAP!! In this digital age, storage might not be a major concern but it will definitely affect the performance of your machine.
Don’t forget that keeping quality, important & useful data is sufficient for answering all the questions related to your enterprise. If any problem can be solved in fewer data, then why use a huge set of data affecting the performance of your system & budget.
Things to remember:
Always be very clear about the amount of data that is required for solving all the questions. Keeping data more than required will only going to waste your data modeling and will affect the performance.
#5. Before continuing, don’t forget to keep crosschecking
Most of the time, data modeling is a huge project, especially, when it is about huge amounts of data. Considering this, it is best recommended to always be cautious. Always regularly check your data model before moving on to the further steps.
For instance, consider that you are required to choose a primary key for identifying each record from the dataset accurately. Here picking the right attribute becomes crucial. One such attribute can be the product ID. Because even if anything matches, then product ID can help you to differentiate the matched records. So it is advised that you always keep a check to know whether you are doing things correctly or not. Even if product IDs matches, then in such scenarios, you have to find another dataset that can help in establishing the relationship.
Things to remember:
Always having or maintaining one-to-many or at least one-to-one relationship is recommended. Don’t forget that many-to-many relationships will only contribute to the complexity of the system.
#6. Let data evolve
Never forget that no data is permanent, it keeps changing. As your organization will progress or evolve, it will be important for you to customize your data model accordingly. You should always keep updating your data model with the right information. This can only be done without any difficulties when you will store your data models in an easy to manage repositories so that you can make adjustments at once.
Things to remember:
Every data model can become outdated quickly before you can figure it out. It is important that you keep everything up to date as much as possible with the time.
What we have learned?
With this, I will bring this article to an end. To summarize, we have discussed some core concepts or data modeling. A majority of the time, everyone talks about data cleaning, but we often underestimate the importance of data modeling.
In this article, we have decoded several things revolving around data modeling. We understood the key steps involved in data modeling, we also uncovered its importance, the advantages, and some challenges or disadvantages of data modeling as well.
After clearing the fundamentals, we explored some of the common types of data models. These were hierarchical model, relational model, object-oriented model, network model, and ER model or entity-relationship model.
After this, we have also learned about some of the most popular & widely-used tools for data modeling. All of these have different sets of features. Talking about the tools, I will like to remind you that selecting the right tool is as important as making an accurate data model. You should always choose the tool as per your need. At last, we have discovered some of the best practices of data modeling that will help you in solving key questions and deriving precise insights for your organizations.