Data modeling and analytics – definitions, types, and best practices

Introduction 

Data modeling and data analytics are two components that lie at the basis of modern business information management. Both of these processes help organizations understand what their data holds and derive data insights to power their decision-making capabilities. 

In this post, I share common data modeling and analysis techniques, along with best practices for each. 

What are data analysis and data modeling? 

Data Analysis helps examine data to create reports that are helpful in decision-making. Among others, it brings together data from various data sources and explores the data to help users understand what information it holds. Data analysis is at the very core of making data-driven decisions. 

Data Modeling is the process that informs organizations on how to manage data. It involves different techniques such as ERD to explore the high-level concepts in data and identify how these concepts are related to other data in the system. Analysts need to analyze data to make data modeling decisions. That means Data Modeling can include some Data Analysis for making the right data models. 

Benefits of data modeling  

If you plan how you’ll organize your data, you’ll be able to improve performance and keep errors to a minimum. As a result you’ll save both money and time  – that’s the number one benefit of data modeling.   

On top of it, it will make data analysis a lot easier for everyone at your company, and you’ll gain access to better insights, and as you’re aware better insights result in better business decisions. Also since you’ll be able to pick a type of database you want to use, it will be easier for you to scale without worrying about performance.  

Data modeling techniques 

There are different types of data modeling. Let’s have a look three types that are commonly used by businesses: 

The hierarchical model 

The hierarchical model is a bit like a family tree with data structured in a top to bottom way. Just like in a family tree, the data is organized hierarchically, and each entity has one parent and numerous children.   

This data modeling technique can work well for databases where there is an apparent parent-child relationship, for example, organizational structures or file systems. Bear in mind that making changes to this model might be challenging as its structure is very rigid.  

Relational data model 

The relational model can be regarded as the foundation of modern data modeling. Think of a really nicely-organized spreadsheets where tables symbolize entities while columns attributes. The relation between the two is formed through “keys”. If you go with this technique, you can expect flexibility, efficient querying and analysis. All elements fit with each other nicely – you can dynamically link tables within the relational model to promote strong interactive data connection.  

Network model 

The network model offers a simple way to represent objects and their relationship. Its unique characteristic is its schema, shown as a graph where arc represents relationship types and nodes the types of objects. As opposed to other data modeling techniques, the network model doesn’t follow a lattice of hierarchy. Instead, it uses a graph that clearly shows the connections between nodes.  

Data modeling best practices  

Here are a few best practices for data modeling that you can inspire yourself with.  

Remember about your business objectives  

Keep your business objectives in mind, that’s the most important thing. Sudhir Khatwani, 

Founder at The Money Mongers says that “your model should mirror what your business is all about. Don’t get lost in complexity – a model that’s too intricate can be a headache. 

Big mistake? Not chatting with the folks who’ll actually use your model from the get-go. Their two cents can make or break its usefulness. And hey, things change, so your model should be able to roll with the punches and stay current” 

Start with a high-level conceptual model 

Your data modeling should always begin with a high-level conceptual model, which includes data entities and relationships without any technical details. This will allow you to focus on the business perspective.  

A Senior Java Engineer at Dr Smile, Sergei Dzeboev says that “ when developing a model, it’s crucial to find a balance between normalization, which is essential for minimizing redundancy and enhancing data integrity, and the potential impact on query performance. However, achieving this balance on the first attempt is rare”.  

He adds that “as your understanding of the data and business needs deepens, you’ll have the opportunity to refine the model accordingly. Implementing standard naming conventions, data types, and relationships is another key aspect, as it enhances the model’s comprehensibility and maintainability”.  

Make sure your data model is properly documented  

Even the most impressive data model is useless, if people don’t know how to use it. That’s why it’s so crucial to create detailed documentation. Roman Milyushkevich, CTO and Co-Founder at scrape-it says that “the right documentation helps modify and update data easier. Among the different data modeling techniques, choose models based on the type of data you are working with and the specific workload”. 

Having everything documented will not only aid you with bringing new members on board but it will also be easier to modify or implement any changes to your model making sure you have one source of truth that everyone can turn to.  

Ensure access to data for your data teams  

Before picking a data model, you should understand what your data teams need access to and how they approach various metrics. Blake Burch, Co-founder & CEO at shipyard says that “your role in the modeling process is to ensure that the minimal amount of data can be cleaned for consistency, defined with documented logic, and tested to ensure that future data loads won’t mess up the models”. 

To better illustrate her point, Burch shares an example “if you look at data in the advertising space, even a calculated metric like ROI could be debated. Does your organization calculate investment based on ad spend alone? Or is it ad spend + COGS? Is the revenue calculated as one-time revenue or is lifetime value used instead? Is a 1:1 ratio of spend to revenue equal to a value of 1 or 0? These are decisions that have to be discussed with stakeholders beforehand and codified into transformation rules”. 

She adds that “many data teams fall into the trap of modeling every data point and ensuring that every last column is appropriately cleaned and documented. You can save yourself a lot of headache by focusing on data that you know is going to be used right now to make business decisions”. 

Make sure your model can be adjusted  

Your model shouldn’t be set in stone, you should pick one that allows for some flexibility so you can make changes and update it with as little difficulty as possible. It’s hard, if not impossible to get your model right at first attempt. And even if you succeed, you’ll have to add new data in the future – bear this in mind, it will save you a look of stress.  

Benefits of data analysis 

Data analysis is what enables businesses to make informed decisions. It identifies trends and enhances strategic planning capabilities, optimizes processes, and fosters innovation. By understanding customer behavior and market trends, organizations gain a competitive edge and are more adaptable to changes in the business environment. 

Data analysis techniques 

Here are the four data analytics techniques you can turn to. Each varies depending on your analytics goals: 

Descriptive analysis 

Descriptive analysis is about gaining an understanding of what happened. It only takes into account past data and allows you to generate a summary, usually in the form of graphs and charts. It’s the easiest and most prevalent data analytics model in business.  

An example of this analysis type is measuring goal completion, for instance, how far the team has gone in reaching the annual recurring revenue objective. 

Diagnostic analysis 

This technique takes the “what happened” and turns it into “why it happened”.  

Diagnostic analysis lets you notice any potential correlations in data, spot recurring events, and understand what was their cause. You can take your and your teams’ hypotheses that arise after descriptive analytics, and validate them. 

While engaging in this analysis type, you can choose an approach known as solution-based diagnostics, It’s commonly used by data teams to identify and highlight ‘symptoms’ of the problems you’ve spotted, and then run a broader scan of your data to pinpoint its cause. As a result, you can understand the scale of the problem and, potentially, escalate it as a cause of concern for the business. 

Running a diagnostic analysis doesn’t require an advanced tool – you can use Excel or run it by using an algorithm. 

You can turn to this technique if you want to understand the root cause for supply chain delays or a change in customer satisfaction metrics.  

Predictive analysis 

Predictive analysis answers the question “what will happen” based on its overview of past events and data trends. It requires a much more advanced analytical and statistical know-how than the previous two methods, i.e., a Business Intelligence skillset. Yet, predictive analytics is crucial for making accurate, data-powered business decisions. 

Bear in mind that how accurate your forecasts turn out depends heavily on the data quality, as well as its level of detail and integrity.  

A great example of a predictive analysis use case is running a risk assessment or sales forecast for an upcoming quarter. 

Prescriptive analysis 

Finally, prescriptive analysis tells you not only what will happen, but also what are the best measures/recommended course of action given the data it analyzed. If a team is tentative about taking a course of action or has several potential solutions, prescriptive analytics will guide them to the much-needed data-backed evidence. 

Tech Target perfectly sums up how this technique works by saying that it “operationalizes insights” from your other analyses and data tools.  

A great example – and, likely, one of the most popular ones – are credit score rating engines, which take everything the company knows about an applicant to measure their ability to take out and pay off a loan. 

Best practices for data analysis 

Now that we understand the capabilities of turning data to insights from analytics, let’s look at the best practices. 

Agree on the metrics you want to measure 

Before you engage in any data analysis, agree with your team and other decision-makers which metrics are the most important for your organization. Knowing your areas of focus will help eliminate any data that could overcomplicate or distort the informational value of your analyses. It will also tell you what kind of technique you need to employ – for example, descriptive vs prescriptive. For instance, if you oversee expenses in a company in the pharma industry, some KPIs that could concern you the most could include Operating Expenses (OPEX) and Gross Profit Margin.  

Focusing on data that holds information on these KPIs would make it easier to create simple graphs and charts and make them more inclusive of non-IT team members. 

Preprocess your data 

Gaining access to data insights is impossible without making sure your data is the right format.  

Sergei Dzeboev, Senior Java Engineer at DrSmile, says that “early familiarity with your data’s sources, structure, and nuances can save significant time and effort later. It also helps identify potential problems before they escalate. This will help eliminate issues such as missing values, outliers, and inconsistencies, which are essential for ensuring the accuracy and reliability of your analysis”.  

Make sure you’re modeling your data correctly 

Here’s where we, once again, see the relationship between data modeling and analytics. It’s worth keeping an eye out for common mistakes like ignoring sources that hold a small volume of data or using inconsistent or complicated standardization methods for names and numerals. How you model your data will affect how you analyze it, which is why you should validate your models before proceeding. This will help you ensure your data is reliable and can be used for analytical purposes across the entire business. 

Use the right tools – including those for data visualization 

Choosing the right tools for your BI projects is an art in itself. The market is huge, and it might be easy to become overwhelmed with the choice. 

As it’s always better to do more with less, search for a comprehensive tool that lets you do multiple jobs in one – for example, run the analysis and generate visuals. Also, shortlist those platforms that are scalable. As your business grows, so might the size of your datasets and the number of sources you’ll need to pull data from. You’ll also have more and more business questions to validate, which is why deciding on a powerful tool now will save your IT department time in the future.  

Finally, search for an inclusive solution, i.e., one with a soft learning curve, which will democratize access to data insights and accelerate your organization’s data journey. 

Derive the right data insights with effective analytics and modeling 

There are several data modeling and data analysis techniques, each tailored to specific use cases and business objectives. Before deciding on the right approach, it’s important to select the right method. You should then carefully pick the tools that will help you not only model and analyze data, but also visualize it for a wider company purpose. Whichever model you go with, make sure you can easily modify it in the future.