Picture this: you are a trusted advisor to the CTO who was given a sizable budget to expand your company’s tech stack and analytics team to reap the benefits of the AI revolution and make sure your competitors don’t leave you in the dust. You move quickly, hiring top talent and investing in cloud-based tools from the modern data stack. Keen to generate ROI quickly, you instruct your team to move swiftly and get building so you can secure tactical wins.
However, you quickly get bogged down, and the wins do not materialize into meaningful strategic victories. Your warehousing costs keep increasing, but the quality of your insights does not. The pressure is increasing from executives who want you to show concrete value, but you aren’t sure how to do it.
It’s becoming clearer by the day: you are stuck in a data swamp, getting dragged into the abyss by technical debt, and you wish you could rewind time and start again. The worst part? This could all have been avoided by starting with some simple data modeling.
What is Data Modeling?
Data modeling suffers from a lack of good branding. To some, it sounds incredibly complex, loosely related to machine learning, forecasting, or even the mighty Large Language Models, requiring a specialized academic degree and deep theoretical knowledge. To others, it might just sound dull—a formulaic exercise best left to people with strange, nerdy hobbies that are nothing more than a compliance-related task that pulls your resources away from the real value-generating build-related activities.
At its core, a data model is nothing more than a structured representation of data elements and their relationships:
There are a few key types of data models, each designed to facilitate the understanding, organization, and management of data within a specific context and to accomplish certain goals. A data model used to power online transaction processing will look different than one used to enable analytical querying of your data, for example. Ultimately, the data model is the bedrock of the data-related activity that occurs in your organization – and designing one effectively requires the input of all the individuals involved in the data’s value chain.
By the end of this guide, you'll see data modeling not as a dry or overly complex task but as a crucial step to unlock the potential of your data. Whether you're a business leader looking to leverage data for strategic decisions or a technical practitioner eager to implement best practices, we will equip you with the insights and tools to make data modeling an engaging and valuable endeavor.
How Does a Strong Data Model Help Your Business?
When discussing the business value of data modeling, it is common for stakeholders to question the idea of spending time on creating a conceptual model before building tangible outputs for a new analytics project, especially after securing the necessary budget.
Common complaints might include:
- It simply takes too long - in terms of crucial technical staff time and the opportunity cost versus building tools & solutions to deal with immediate issues.
- Businesses and the processes that underpin them are too complex and change too often. Attempting to build a useful data model to capture all their activity is a fool’s errand.
- It’s an irrelevant exercise – because you already have a data model! Your data providers built one into the different source systems that you are getting your data from. Why would you try to rebuild one yourself?
Despite these concerns, our experience shows that a data modeling exercise delivers tremendous return on time and capital investment. Building a robust data model relevant to your specific organization will enable you to unlock your data's true potential from day one. Those that ignore this key step will have to operate with a crutch and will struggle to achieve any real levels of sophistication, where the highest value activities lie, in the future.
The Benefits of Data Modeling
Here are some of the key benefits of creating an effective data model at the start of your analytical journey:
Speed to Analysis
The value of your data lies in the decisions that can be made based on its analysis. By shortening the time between collecting a data point and making a decision, you are increasing the value of the analytical process across multiple dimensions. You will be able to make more decisions on more topics in a more relevant timeframe – this is a key part of the recipe for being a truly data-driven organization.
Let’s explore what this would look like when your team of analysts is tasked with dissecting the results of a poorly performing business and presenting results in a meeting next month. Data modeling would not only allow the team to compile an initial analysis for the meeting by typical slices like time, location, or product but also enable the team to answer the inevitable next-level questions in the meeting on the fly and get live answers in front of the stakeholders. Assuming the data is modeled, new visuals and slices of the can be created quickly, reducing the number of follow-up emails and re-analysis, and allowing your team to spend time either adding more data to the model for deeper analysis or moving on to another use case with existing data.
This increased velocity will also translate into decreasing the time between your investment in analytics and realizing its value. This has financial benefits for your business, and for the continued success of the data team that will be able to justify itself as being more than just a cost center. Rather than being a time sink for you and your team, it is a way to enable them to work efficiently and effectively, particularly as you mature along your analytical journey and the volume and complexity of your data sources and use cases grows.
A Single Source of Truth
Businesses are complicated. They are a collection of tools, processes, and activities – and it can be difficult for all the teams involved to align on a shared understanding. To a trained team of executives, data modeling is a secret weapon they can use to align this organizational knowledge, creating a single source of truth.
The impact of any output from your data platform—whether a spreadsheet, a detailed PDF report, or a dashboard—depends on the trust the business has in your data. Without trust, users may disengage or constantly request raw data, leading to inefficient, low-level analysis and resource drain. A single source of truth fosters user engagement and reduces the manual effort required to resolve data integrity issues arising from multiple, poorly governed sources.
Looking back at the scenario presented above – the CTO wanting to engage with the results of his data team and choosing to take important corrective actions based on their results, completely depends on the trust he has in the insights they produced.
By building a standardized model that performs well from the start and is trusted by key decision-makers, we create an analytical environment where people ask interesting questions about the data rather than questioning its validity.
Flexibility and Adaptability
Businesses that adapt are businesses that flourish. This is true in the world of data and analytics, where innovation is constantly redefining the landscape. Opportunities for growth, as well as existential challenges along the way. By having a modular, well-defined, and thoroughly documented model, a data team can easily take advantage of new developments.
This could be ingesting new sources of data from a new key vendor that your competitors have yet to discover, modifying pipelines to leverage a brand-new transformation tool that reduces warehouse costs.
On the other hand, having a commonly shared and well-understood data model makes it easier to handle major changes. A key team member leaving can lead to service delivery disruptions and losses in overall system integrity because they take their tribal knowledge with them. A data model helps reduce that risk by democratizing knowledge and making it more transparent and accessible. It can also make dealing with a string of critical pipeline failures easier to diagnose, isolate, and fix – without your entire data team’s week being completely derailed.
So, now that we have hopefully convinced you to take the time to design your data warehouse before filling it, let's look at practical steps to make sure that process is successful.
Best Practices to Create the Right Data Model
The key to building an effective data model is twofold: It needs to meet your organization's specific needs, and people need to use it. The good news is that the following list of recommendations will help you tackle both objectives simultaneously.
Educate Yourself on Data Modeling
Data modeling techniques are not as complex as they seem. There are a handful of widely used techniques, and understanding their pros and cons is straightforward. By reading up on or following a short course (we have you covered) on the data modeling theory, you will know what options you have in your toolkit and, more importantly, the questions you should ask yourself when choosing your modeling approach.
This is particularly important if your business must respect certain rigid constraints like the need to provide a fine data audit trail or to isolate certain datasets based on geographic restrictions. Certain data models will enable you to meet those constraints better than others, and you should know this ahead of time.
Build a Data Model That is Relevant to Your Organization
Data models have very little inherent value – it is only their ability to enable the members of your organization to accomplish their goals that makes them useful. This means that you must include data consumers in the data modeling process so that you understand their needs, align on what is possible, and build something they want to use.
We recommend using a few simple tools to make sure you align your data model with your business needs:
- Conduct in-depth interviews to collect detailed information from a range of stakeholders. This is a low-cost way of ensuring you are completely aligned, as a back-and-forth conversation will always yield more precise results than a survey. People will also appreciate having a voice and are more likely to engage with you throughout the process.
- Organize and deliver workshops to share valuable information with large groups of stakeholders in a collaborative and fun way. The workshops can be used to share progress reports and gather feedback on the model's design.
- Share transparent and regular update reports to the stakeholders involved in your data value chain. This is a low-cost way to share progress with a wide array of individuals and ensure you cast a wide net for consolidating ideas and feedback, building trust in your model with the wider organization in the process.
By approaching data modeling in an intentional and collaborative way, you maximize the likelihood that it will fit the needs of your data consumers while laying the foundation for the belief in that single source of truth and the value-generating potential it represents.
Follow Simple Technical Best Practices
The Aimpoint Digital team follows a specific set of principles when building enterprise-grade data models that will stand the test of time and deliver maximum impact. From naming conventions to data layer architecture, our experience has shown us that simplicity is the key to results.
Rather than include all of them here, we have compiled them into a downloadable document. Consider it a lighthouse in the swamp of data you might currently be navigating – a simple set of rules you can always fall back on to ensure you build something valuable.
Evangelize the Data Model
No matter how perfect the data model design is, it will only be used (and therefore be useful) if there is awareness and trust in it within your organization. Therefore, you should dedicate as much of your time and energy to building the best data model as to being its chief evangelist.
A tried-and-tested way of accomplishing this is to include all levels of stakeholders in its conceptualization. This will ensure it’s useful and relevant and builds crucial trust in its validity. After the model is built and available, the most effective way to raise awareness is to build high-impact data products using it, which leads to our final tip.
Build an End-to-End Use Case
Our final tip is to pick a highly visible and relevant end-to-end use case to use as a guiding light throughout the planning and implementation phase.
This use case will serve many vital purposes:
- It will allow you to test the effectiveness of the model you have built by ensuring that it contains the crucial data needed to power this use case.
- It will enable you to validate the single source of truth you have built using real data flowing through the pipelines. This will empower your engineering and analysis team to build the monitoring and evaluation systems they need in partnership with the business to ensure their system supplies data of the finest quality.
- Finally, it will allow you to evangelize your data model by showing a concrete example of the successes it can enable.
Our team spends hours every day helping clients think through their data modeling challenges – it is our bread and butter, but most importantly, it is our passion. Please get in touch if you would like to share any feedback with us or if you would like some input to help you get started on your data modeling journey.
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