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12 May 2023

Data Modeling for Business Analysts

devData Modeling for Business Analysts

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Data Modeling for Business Analysts

Data encompasses any form of recorded, stored or retrievable information. Business analysts help define and articulate business requirements for applications before shepherding them through IT for trialing in production environments.

Data Modeling techniques require a firm grasp for success; however, many BAs still manage to find success without such advanced expertise, instead relying on collaboration between IT specialists and other informed stakeholders for success.

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Data connectivity

Conceptual Model

Conceptual models are visual depictions of the entities and relationships comprising a data system. Business analysts use conceptual models as an effective way of representing these components without diving too deeply into technical language and jargon, making their efforts easier when communicating effectively with stakeholders and teams. Conceptual models can take many forms - from whiteboard drawings to complex design software programs - while always emphasizing real world relationships of their businesses.

Conceptual data models help clarify definitions between customer and prospect so everyone understands them, eliminating the need for further clarification memos later in the project. Furthermore, conceptual models take into account cardinalities for each entity such as one-to-one interactions as well as many-to-many ones; which may otherwise go undetected as projects progress - particularly when teams become focused on minutiae with limited time available to address it all.

Focusing on real-world and business needs, the conceptual data model can give a clear idea of what will be included in the system and how its data and rules interact. By helping avoid costly mistakes and rebuilds later on, as well as highlighting any areas requiring more analysis or modeling work. By emphasizing any confusing or unclear concepts or wording that requires clarification during development, this process can improve communication between business analysts and developers and guarantee that the right information is captured in the system.

Business analysts can then use this model as a blueprint to construct the logical data model, or LDM. LDMs are technical maps of data structures and rules typically created by data architects and business analysts to assist in decision-making about physical models. LDMs will include key attributes required by each entity needed in the system as well as how those are connected (i.e. one-to-many and many-to-many connections).

A logical model may be built using either a conceptual model or more detailed, structured data set. Such models usually include more intricate details, such as primary keys and the resolution of many-to-many relationships; it should also address any exceptions or complications in the model, such as cases when one thing can be split apart into many pieces.

Logical Model

The logical model expands upon the structure and definitions identified in the conceptual model, along with limited types of one to many relationships called sets. It usually identifies entities and relationships which form the foundation of physical databases - this information being displayed as Entity Relationship Diagrams as standard business data modeling techniques.

Logical models offer organizations numerous advantages in terms of meeting all their data needs before beginning to design physical databases. A logical model helps clarify and articulate all data requirements before embarking on physical database design. A great benefit of using one is getting all stakeholders to agree about which data needs to be captured, how and for what purposes. It helps reduce costly implementation errors such as missing or duplicating information across multiple locations or even duplicate data being captured without need.

A logical data model is typically constructed by business analysts in collaboration with Data Architects, and focuses on the structure and definitions of data requirements expressed as tables or graphs and how they connect together. A logical data model does not bind it to any particular technology or DBMS and provides the foundation of what a physical data model should resemble regardless of underlying technology.

As it's not limited by current technological constraints, an untethered system encourages future improvements by forcing analysts and users alike to identify all data needs regardless of whether they may be possible with existing solutions.

Logical data models are especially effective at communicating the information architecture to other business analysts on application development teams and database administrators who perform physical database design. A logical model makes this easier than communicating an intricate conceptual data model or an in-depth DBMS schema to these groups.

Logical data models can also help in making decisions regarding which class of database to utilize, such as between relational or document databases. They also assist in defining each table's characteristics such as columns, indexes and foreign keys.

Physical Model

Physical models are material representations, typically of smaller scale, of objects or phenomena to be studied. Engineers and scientists use physical models as an affordable way of testing theories or processes without risking expensive equipment being destroyed during tests. Physical models have applications across several fields including electrical circuits, chemical reactions and more.

Data modeling can be an indispensable tool in helping all members of an organization understand how data can support your goals. To gain expertise in creating data models, business analysts may wish to take an introductory course and also gain experience through hands-on projects.

At the core of data modeling is creating a conceptual model. This process is fundamental in identifying which information and relationships must be included in a database; additionally, this step serves as an excellent opportunity to align stakeholders around its purpose.

Once you have developed a conceptual model, the next step should be creating a logical data model. This adds more details by detailing which data will be stored where and how it connects. An entity-relationship (ER) diagram may help in this endeavor.

Logical models are an integral component of database design and serve as the basis for physical models. They can be created using various tools, including ER diagrams and SQL, in order to establish their presence.

Physical Model Creation. The final stage in data modeling involves creating a physical model. This is where things get technical, typically handled by developers and DBAs. A physical model specifies what database or file structure will be used to store your data - including tables, columns, data types, primary keys and constraints as well as replication, sharding and partitioning technologies that may help increase storage performance and access performance. Furthermore, using physical models helps businesses determine the most appropriate architecture - be it relational databases versus document databases.


Business Analysis involves extracting key insights from data to produce reports and graphs, forecast future outcomes or assess what works and why.

Business Analysts play a vital role in Data Modeling by gathering input and requirements from business stakeholders throughout the design process. They assist with creating Conceptual, Logical and Physical Data Models before aiding with communicating specific technical needs to Database Designers.

Business Analysts play an invaluable role in both conceptual and logical Data Modeling phases, but their importance increases during physical Data Modelling stage of any project. Their influence becomes especially vital here since resulting schema will have direct bearing on how databases and applications are installed - this should ensure compliance with technical specifications as well as all functional requirements outlined by Business Model.

Metadata, also referred to as meta-data, contextualizes other data for easier finding and use. Metadata might include information such as how a dataset was collected or related datasets; its collection date; any details surrounding photos taken during an event or photo taken for documentation; the time, date, and location of photo or document taken during event or even how this data will be utilized by its recipient. Furthermore, metadata may provide insight into why data exists at all.

Effective metadata management is critical to data governance and regulatory compliance efforts of any company. It allows them to better organize the enormous volumes of data they gather and use while also improving its quality, and this enables them to develop new insights more quickly while adhering to disciplined procedures and disciplines.

Digital libraries use metadata to organize and describe information resources, while improving search and retrieval of these resources. Metadata also serves to identify similar resources while permitting for the creation of a taxonomy of metadata which facilitates easy identification.

About the author: Craig Hartzel
Craig is a self-confessed geek who loves to play with and write about technology. Craig's especially interested in systems relating to e-commerce, automation, AI and Analytics.

+44 (0)20 8446 7555

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