15 Data Modeling Tips and Best Practices

Data Modeling is one of the most important parts of information modeling. A good data model, tightly integrated with its applications or systems is easy to understand, maintain and change. In this post, we will discuss top 15 data modeling tips and best practices.

The theme of dimensional data modeling is straightforward. It involves organizing data with an approach that is easy to understand with precise analysis and reporting. This theme is still applicable. The only thing that has altered is that today’s data warehouse has several applications than merely analysis and reporting.

Data science, machine learning, and data engineering are a few of the emergent applications for big data stored in modern-day data warehouses or data banks. However, this alteration doesn’t need us to develop an entirely new approach to data modeling. Some tweaks in data modeling design can meet the extensive data requirements of today’s much-extended audiences.

Without a good data model, the data and business processes will be unorganized and disorganized. In this blog, I’ll attempt to address best practices when working with relational database models.

1. Understand the business needs and required outcomes

The objective of data modeling is to assist an organization work better. As a data modeling professional, precisely capturing business requirements to know which data to prioritize, gather, store, alter, and make accessible to users is often the principal data modeling challenge.

So, we recommend you completely understand the needs by asking stakeholders and users about the outcomes they require from the data. It is better to initiate well-organizing your data sets with stakeholders and user pointers in mind.

2. Explicitly visualize the data and information to be modeled

Watching uncountable rows and columns of alphanumeric records is not likely to bring insights. Most people are far more relaxed looking at graphical data illustrations that make it swift to view any variances. Furthermore, people must be given access to simple drag-and-drop screen interfaces to quickly review and connect data tables.

Data visualization assists you in cleaning data sets to turn them steady and free from mistakes. It also aids you to identify diverse data record categories that link to the actual entities to alter them and then use simple fields and formats, making it straightforward to blend data sources.

3. Initiate with primary data modeling and scale afterward

Data sets can turn complex speedily, owing to aspects such as size, category, structure, maturity rate, and query language. Maintaining data models small and modest at the primary level makes it simpler to correct any issues or wrong turns.

When you are sure your preliminary models are precise and expressive, you can bring in more data sets, removing discrepancies. It would help if you utilize tools that make it simple to start yet can back extensive data models later, letting you swiftly “mash-up” numerous data sources from diverse physical locations.

4. Split business inquiries into dimensions, facts, filters, and order

Understanding how these four factors can state business queries will assist you with well-organized data sets in approaches that make them simpler to answer.

For instance, your retail company has stores in diverse places, and you want to find the top-performing stores in the last 12 months.

In this scenario, the facts would be the historical sales data sets, the dimensions would be the product and store site, the filter is “last 12 months”, and the order will be “best five stores in declining order of sales.”

By well-organizing your data sets, leveraging individual tables for dimensions and facts, you can enable the analysis for identifying the topmost sales performers in every period and even answer other business intelligence queries precisely.

5. Use merely the data you require instead of all the data accessible

Computers and software working with big data sets can soon run into issues of memory and rapidity. However, in many scenarios, merely limited data sets are required to answer business queries.

Preferably, you should be able to merely checkboxes on the software to state which portions of data sets are to be leveraged, allowing you to shun data modeling waste and avoid performance challenges.

6. Make calculations in advance to avoid user discrepancies

A significant goal of data modeling is to build a single version of the truth, against which a diverse user base can ask their business queries. While people may have varied choices on answers, there should be no divergence between the original data or the calculation leveraged to get to the solutions and answers.

For instance, you might need a calculation to accumulate day-to-day sales information to originate monthly numbers, which you can then match to showcase the best or nastiest months. Rather than leaving other people to reach their calculators, you can evade issues by setting up this calculation in advance as an integral portion of your data modeling report and making it accessible on the business dashboards for different users.

7. Validate every stage of your data modeling before moving ahead

It will help if you verify every action before moving to the following data modeling stages. For instance, an attribute called the primary key must be selected for a data set so that every record can be found exclusively by the primary key’s value in that particular data record.

The same method can be applied to a join of 2 data sets to validate that the association between them is either one-to-one or one-to-many and to avoid many-to-many associations that direct to excessively multifaceted or uncontrollable data models.

8. Look for connection, not merely correlation

Data modeling comprises guidance in the approach the modeled data is leveraged. While enabling users to access business intelligence for themselves is a significant action, it is also vital that they avert hopping to erroneous conclusions.

For instance, possibly if we view that sales of two diverse products seem to increase and drop together. Are sales of one product steering sales of the other, or do they happen to upsurge and fall together as of another aspect like the economy or the weather conditions? Puzzling connection and correlation here could target the wrong direction and thus worsen resources.

9. Use modern tools and techniques to execute the complex tasks

More multifaceted data modeling may involve programming to process data sets before analysis starts. However, suppose you can manage such complex tasks using software or an app. In that case, this frees you from the requirement to explore diverse coding languages and lets you invest time in other functions of value to your organization.

An explicit software can enable or automate all the varied phases of data extraction, transformation, and information loading. You can retrieve data visually without any programming needed. Also, diverse data sources can be clubbed-up using a drag-and-drop interface, and you can even execute data modeling automatedly concerning the specific query category.

10. Make your data models progress and advance

Data models are not ever engraved in stone as data sources and user requirements alter repeatedly. Consequently, it would help if you better plan on their updates over time.

So, store your data models in a source that makes them simple to access for alterations, and leverage a data dictionary with the latest insights for the objective and format of every category of data to be processed.

12. Verify and test your data analytics execution

Test your analytics execution like you test any other functionality you build and implement. A test should check whether the complete data set volume and data are precise. Also, contemplate whether your information is well-structured and enables you to get a key metric.

Furthermore, you can generate some queries to understand better how it would be workable and applicable. We also suggest building a diverse project for testing your execution and implementation.

13. Check for data type or category mismatch

Ensure that your data sets are in the precise format. If you have an explicit property like “number of products” and you input the value as “4”, you can’t add the values to regulate a “total number of products” as it is a string.

We recommend viewing and checking the event properties you have gathered. Perform a thorough quality check to ensure the object has the data type or category you expect.

14. Evade tricking your data sets

We suggest that you sidestep leveraging lists of objects. Most of the filters diversely behave when working with lists. The “in” and “eq” filters are significant. Additional filters and analysis will not enable object values in a detailed list, so avoid tricking with your data sets.

15. Avoid using lists of objects

Explore a query on how to model an activity such as a shopping cart transaction that comprises numerous items. A possible solution is to generate one order collection by every possible transactional deal with one event. However, this is not a definitive solution.

You will not be able to view what the most bought products are as they are stuck within the shopping cart list object.

To evade this issue, don’t use lists of objects. We suggest the best approach to model shopping cart transactions is to generate two separate collections and then analyze the data sets.

Data Modeling Best Practices #1: Grain

State the granularity at which the data anticipates to be stored. In most scenarios, the most suggested grain would be the lowermost grain to start with data modeling. You can then change and combine data to attain summarized insights.

Data Modeling Best Practices #2: Naming

Naming stuff stays an issue in data modeling. The best practice is to select a naming scheme and hold with the same.

Leverage schemas to name-space relations like data sources or a business unit. For instance, you might utilize the marketing schema to comprise all the tables most applicable to the marketing team and the analytics schema to house superior concepts such as longer-term value.

Data Modeling Best Practices #3: Materialization

It is one of the most vital tools for developing a superior data model. By this practice, if you generate the relation as a table, you can precompute any needed calculations, and your user base will view quicker query response times.

If you let your relation as a view, your user base will get the newest data sets when they apply a query. However, response times will be slow. Relying on what data warehousing technique and tools you are leveraging, you might make various trade-offs according to materialization.

Data Modeling plays a vital role in designing data centered solutions. The data model is the blueprint for the persistent tier in the application. It is the basis for developing Data Access Layer (DAL), business layer and service tier components. When developing data-centered enterprise applications one has to create a robust data model to facilitate enhancing, migrating to future release and most importantly increasing performance.

Consider the user’s demands, plan and put efforts to create the data model that will best assist those planning. Once all the criteria match, you and your small-sized or enterprise-level business can expect your data modeling to bring substantial business value.