With ever-advancing technologies, businesses are constantly adapting with the change and making money in the process. Such advancements are generating an enormous amount of data. Companies which are embracing the opportunity of gaining knowledge from the data are experiencing a steep upward curve. Moreover, companies having the right data modelling tools and correctly using the types of Data Model are unparalleled.
With data, comes responsibilities!
We are living in a world where data have become a necessary evil. The onset of Industrialization 4.0 has forced the companies to take the data-driven approach. Using data ingeniously can do wonders but, it comes with a cost. Only efficient and securely modelled data are useful in the long run. Having an enormous amount of data is both an asset and a liability. If not handled with care, a company becomes prone to external threats (data breach, ransomware, etc.) and internal ruckus (reduced efficiency).
No great marketing decisions have ever been made on qualitative data.John Sculley
What is data modelling?
The term data model refers to the structure and representation of data. The primary purpose of a data model is to ensure that the data required by the business is accurately represented and stored efficiently in a database. There are three types of Data Model.
Types of Data Model
- Conceptual (What it should be): It describes a high-level view of the data required in a business process. This type of data model identifies the data which should be collected and stored for a business process in future
- Logical (How it should be): It provides detailed data description without considering physical implementation of data storage. It also describes attributes and relationship between entities
- Physical (How it is): Logical data model is used to derive it. It represents the implemented design of the data. It describes how the relationships between data, are implemented using table structures, primary keys, foreign keys, column names, column format, etc.
Different types of Data Model are suited for various stakeholders
- Business-leaders and decision-makers tend to avoid nitty-gritty of data handling. Thus, a Conceptual data model is best suited for them
- Project managers or project leads require data in Conceptual as well as a Logical model. Most of the managers are not good with technicalities. Therefore, it is fruitless to represent data in the physical model for them.
- Architects or developers need to implement the Logical model. Thus, both Logical and Physical data model is essential for them.
Why are data modelling tools required?
The types of Data Model are a crude form of data representation. The mind-boggling nature of the technology of efficiently storing and accessing data has left many managers, perplexed. Therefore, it has become imperative to have access to data modelling tools.
Imagine a life when suddenly you are deprived of all modern technology. It is the same feeling one will get when asked to work digitally without a proper data modelling tool. There are numerous tools developed for the sole purpose of managing the business data and representing it in the required model.
Some of the data modelling tools are mentioned in the table below:
|Navicat Data Modeler||Cross-Platform||Ability to generate Data Definition Language from diagrams.|
Synchronizing schema of two databases.
|IDERA – ER/Studio||Windows||Flexibility and ease of use.|
|SAP – PowerDesigner||Windows||Impact analysis.|
Availability of technical support staff.
|Oracle SQL Developer Data Modeler||Cross-Platform||Merging different versions.|
Flexibility in working with diagrams.
|Erwin Data Modeler||Windows||Forward engineering for the Physical data model.|
|ArchiMate||Cross-Platform||User-friendly, lean and simple language.|
Suitable for essential architecture design only.
Why is it important to choose the right tool among various data modelling tools for your business?
Data has become an inevitable aspect of the businesses. Many applications create data, and the way of utilising that data depends upon the business leaders. A misfit of a data modelling tool for your business-specific data can cost you dearly.
There is always pressure from the regulatory authorities to maintain the standards. The standards are subject to current situations, and they always change. Thus, change in the architecture or types of Data Model is required frequently to keep up with the regulatory guidelines. Having a modelling tool that fits your business scenario will cost you at the beginning, but it will benefit you in the long run.
A business leader needs to keep an eye on the performance of the company so that he/she can make correct decisions. Visualisation of the business-related data in the graphical or flow-chart format is its best representation. A data modelling tool whose strength is data visualisation is apt in this scenario.
Is it wise to put resources for a tool just for the sake of data visualisation? Let’s see.
Not just for business leaders, data visualisation is critical for end-users as well. Customers love to see data as graphs, and the sole motto of a business should be to make the customers happy. Absence of exceptional visualisation reduces the significance of a product or a service. Therefore, it is imperative to use a tool which has this robust feature.
Not all businesses receive data through automated tools. Some of them have humans behind the curtains entering the data. Humans are prone to errors, but businesses cannot afford to have them. To prevent loss, the company need to run an errand. Such a process could be costly, both in terms of money and time. A fault-tolerant and robust tool can do wonders for a company in this situation. If your company faces such scenarios day-in and day-out, it makes sense to invest in such data modelling tools.
The above scenarios help in understanding that different data modelling tools will fit different requirements and different types of Data Model. Their strength is an essential factor while deciding the suitable one for investment. The best tool for your business would be the one whose features match your necessities. There may be a requirement of more than one data modelling tool to cover all the aspects of the business. It is always wise to invest in it early on so that the data can be up-scaled in parallel with business growth.
What are some important features of data modelling tools?
Data modelling tools need to be robust to facilitate smooth operation. It should be intuitive for the developers so that they can create and maintain databases without any hassle. Such software should have the characteristics of entity-relationship diagrams (E-R diagrams) for designing databases to run the business efficiently.
The tools should provide a highly sophisticated visual data modelling environment making the database development process simple. It should bring a reduction of faults in the process of database development which will enhance productivity.
Using the tools, one should be able to visualise the database structures to create new databases or undertake reverse engineering of the already available databases. It should also bring a change in the document for the analysis and optimisation.
Such tools will bring proficiency in use irrespective of the type of database modeller (beginner or an expert).
Features can be discussed primarily under the following heads:
- Visual database modelling: The tools should comprise of Entity-Relationship diagramming for graphical representation of the databases. It should use Industry-standard design notations for the diagrams. The software should also have a distinct feature of giving output in multiple display mode, and provide pan and zoom window for E-R modelling.
- Database synchronisation: Data modelling tools should extend the default functionality of comparison. The software should have a comprehensive comparison of all versions of the data to make necessary changes.
- Model-driven collaboration: The data modelling tools should be capable of developing a large number of models and place it efficiently in repository databases
- Model maintenance: The tools should have a diagram management feature for breaking a complex model into a smaller one. The feature’s object browsing capability can facilitate creating, navigating and editing the databases. Its labelling capability can organise an entire project into meaningful groups and levels
- Reporting: The tools should provide the flexibility of customising the report
- Better database designs: It is a remarkable feature of model validation and automated foreign key migration. The tools should use name, datatype, default value and validation rules for constructing a reusable domain. Its attribute packages promote consistent definitions that can share packages between types of Data Model. Name Templates Editor assists in creating naming rules for entities relationships and constraints to reuse in the model. Data modelling tools should have all the essential parts of this feature.
What are the Challenges in Data Modelling?
There is always a gap between academic studies and professional experience. Same is true here as well. If you are working with only the bookish knowledge of data modelling and types of Data Model, you’ll meet with many challenges. It is only the practical experience that gives the number of options and preferences before creating types of Data Model.
Naturally, the challenges in data modelling vary from clients to data modellers. Client-end sufferings are from lack of documents leading to less information about the business process and business rules when there are no clues or explanations from SMEs (subject matter experts) and BAs (business associates). Quite often, user requirements and demands are more than the expectations at the client end. Therefore, it lacks proper planning in the ERP (Enterprise resource planning) system.
The challenging situation from the data modeller’s perspective is a lack of understanding about the business requirements. Even if there is an understanding, they are unable to project the forecast about the entities needed for the project. If there is an understanding, then the knowledge of its attributes is lacking. If those are also known, then creating an identifying or non-identifying relationship is still a big challenge.
What are the skills required to become a data modeller?
The job role of a data modeller includes documentation, developing models, creating SOPs (standard operating procedures), peer-reviewing. A data modeller is also supposed to assist developers and DBAs in the creation of required databases and cooperate with the business analysts and employees to have a proper understanding of the requirements. All these are necessary to ensure that the entire SDLC (Software Development Life Cycle) runs smoothly.
The skillsets of a data modeller
- Knowledge of Data-Normalisation
- Understanding of dimensional modelling concepts
- In-depth knowledge of types of Data Model
- Understanding of DBMS
- Understanding data patterns
- Strong communication skills
- Thorough understanding of the business
Though not required, a data modeller should also have an understanding of the non-traditional data stores such as NoSQL, XML, JSON, etc.
In practical terms, the critical skill of the data modeller has not changed over the past several years, not even with the advent of Agile and NoSQL. Knowledge of the business, communication skill and understanding of normalization are amongst the most desired skills. However, the skills during the next 5 to 10 years with the continued growth of agile practices, non-relational databases and BigData will be a billion-dollar question. However, with the continuous growth of agile and non-relational databases, in the next 5-10 years, “will these skills be relevant?” is a billion-dollar question.
Data modelling is more than just database designing because data doesn’t only exist in databases. It is essential to put in a great deal of thought before choosing a data modelling tool and implementing various types of Data Model. Making the wrong choice at the beginning can derail your project later and adversely affect your profits. Therefore, choosing the right data modelling tool is imperative for a business to flourish in the current market.
The current market trend shows that data will play an essential role in deciding the fate of a company. The aim of the business leaders should be to let go of traditional approaches and integrate the business with the digital world. The companies which are missing out on the opportunity of utilising the data will soon meet the same fate as companies which refused to move with the change (e.g. Kodak).
The industry experts believe that data modellers must stay updated of the transformation trend of the data management industry including BigData, an ever-increasing preference for agile products and non-relational databases, the semantic web, cloud computing and other related emerging developments. Future will be data-centric. Companies should act now and grasp the opportunity.
Q1) What are different design schemas?
Answer: There are two types of design schemas in data modelling:
- Star Schema: In this schema, there is a fact table, and it references many dimensions tables. The fact table is in the centre, surrounded by multiple dimension tables. The fact table references all the dimension tables and no references between the dimension tables. The primary keys of all the dimension tables are foreign keys in the fact table.
- Snowflake schema: In this schema, similar to the star schema, there is one fact table in the centre and multiple dimensions table, but the dimension tables are normalized. This result in a snowflake-like structure
Q2) What are dimensions and attributes in data modelling?
Answer: In data modelling, dimensions are qualitative data (descriptive or contextual). Attributes identify names and define a characteristic or property of an entity type. They are the most basic unit of a database model. Usually, they are the column names in a table.
Q3) What is a surrogate key in data modelling?
Answer: Just like a primary key, a surrogate key is a unique identifier. It is a system-generated sequence number, and it can act as a primary key. It is different from a primary key because it is not a part of existing data fields.
Q4) What is metadata?
Answer: Metadata is a description of the data. It contains details like the type, purpose, name of the owner, source, etc. Usually, a user uses the metadata and not a system. In short, it is data about data.