A Comprehensive List of Best Practices for Data Modelling

Data plays a crucial role in decision-making processes in a business. Similar to how an architect studies a blueprint before making pivotal decisions, businesses must look at data to make meaningful, data-driven decisions. This makes data one of the most valuable assets for any establishment and data analytics an important practice.

However, before you move on to understanding the data and drawing insights from it, you must adequately document the data and communicate it with relevant stakeholders who can then take it ahead from there.

So, for enterprises to verify the fact that their data is being fully utilized to improve business decisions, they must check the accuracy, extensibility, coverage, as well as interpretability of that data.

Data modelling is a tool that helps you create a visual description of your business, and in turn, helps you analyse and explain data requirements of your business. With it, you can maintain clean, good quality data that your business can trust to make powerful data-driven decisions.

Ideally, you want to curate a data model that:
• Is comprehensible by data analysts as well as data scientists which will prevent them from making mistakes while writing queries
• Works hand-in-hand with the BI tool that you’re using
• Minimizes time-to-build
• Lowers response time to both the BI tool as well as ad-hoc queries
• Reduces costs associated with data management

To accelerate your acquaintance with data modelling, I have curated a list of best practices that will help you adopt it in an effective manner.

Now, you may be aware that data can be categorized as structured, semi-structured, or unstructured, and each of these types require a different approach to storing and modelling mechanisms. In my blog today, I will outline the guidelines and best practices associated with Columnar databases and how they can be used for different types of data.

1. Ensure Model Correctness:
   a) Ensure that the model accurately captures the material
   b) Confirm that the design accurately represents the data requirements
   c) Ensure the conformance of data elements with different formats than industry standards
   d) Fix incorrect cardinality and keys defined incorrectly
2. Aim for Model Completeness:
   a) Check whether the scope of the model matches the requirement
   b) Verify whether the model is complete yet incorrect or incomplete yet correct
   c) Clarify any vaguely defined terms
3. Review Model Structure
   a) Impose standard modelling practices, independent of content
   b) Conduct entity structure review
   c) Review each data element
   d) Conduct thorough relationship review
4. Enhance Model Flexibility
   a) Ensure that the correct level of abstraction is applied to capture new requirements
   b) Aim to achieve the right level of flexibility
   c) Derive value from every abstraction situation
5. Comply with Modelling Standards & Guidelines
   a) Ensure correct and consistent enterprise, conceptual, logical, and physical level as per standards & guidelines
   b) Use the correct names and abbreviations
6. Check for Accurate Model Representation
   a) Ensure optimal parent and child entities placement
   b) Deploy intelligent use of colour in grouping or highlighting entities
   c) Maintain proper relationship lines crossing each other or through unrelated entities
   d) Use subject area optimally
   e) Maximize readability and understanding
7. Maintain Physical Design Accuracy
   a) Ensure that the design works is the real world as well as is specific to application
   b) Consider null values
   c) Use partitioning adequately
   d) Utilize proper indexing and space
   e) Consider denormalization
8. Ensure Data Quality
   a) Verify that the design and actual data are in sync with each other
   b) Determine how well the data elements and their rules match reality
   c) Avoid costly surprises later in the development process

You can use these best practices to properly define data layers to make data-driven decisions for your business and avail a plethora of advantages such as:

• Avoiding joins completely that are caused due to de-normalization to achieve faster retrieval
• Enhanced ability to scale horizontally without any limitations on the number of columns
• Compression to use less memory for storage
• Reduced time to design, model and load data through ETL packages into fact tables
• Faster design, modelling, and loading as well as rapid analysis of the cycle
• Effective dealing of unstructured and semi structured data with the help of a columnar database layer using MPP architecture that acts as the middle layer/bridge between traditional Enterprise Data Warehouse (EDW) and the Hadoop ecosystem driven by multiple tools and technologies

Proper data storage and modelling can be a game changer for your business, especially if you’re dealing with voluminous data that piles up rapidly. With this comprehensive list of best practices, I hope you can begin your journey towards effective data management and avail the benefits that come with it.

Reach out to us at Nitor Infotech to learn more about our Big Data engineering services and take a look at our whitepaper that chalks out some more guidelines and best practices for Columnar and NoSQL databases.