The Definitive Guide to Data Modeling
Content Marketing Manager, Sigma
In the early days of driving, filling stations didn’t trust motorists to pump gas—effectively becoming the gatekeepers of gasoline. Filling stations were controlled by attendants who could lock the pumps when they weren’t around. While most of today’s drivers can’t imagine gas stations without self-service, it took some evolution to get to that point. Today’s business equivalent of gasoline is data—it’s what fuels business growth and progress—and data teams are increasingly reliant on self-service analytics tools to make data accessible to domain experts. Data modeling is what makes that possible.
This guide will explain what data modeling is, its importance and benefits, as well as how you can simplify the process for your organization. It will also explore why data modeling is transforming into a more collaborative process that allows domain experts in an organization to not only access output, but create, change, and work together with data teams on the evolving data models. Whether they do this out of necessity or curiosity, this approach helps lead to better business intelligence and improvements.
What is data modeling?
Data modeling is the process of organizing and presenting raw data using business logic. Traditionally, members of the data team would create a model from raw data in the organization’s data warehouse or data lake. They would identify data they wanted to use and the associations they wanted to make— and then present the results in a consumable way. Ideally though, data modeling should be easy enough that domain experts can create data models on their own.
Data modeling is the process of organizing and presenting raw data using business logic.
Data models give users the right information in the right context so they can access the right data for queries, generate accurate insights, make decisions, and take action. For example, teams may know your organization has collected data on customer location, purchase history, and geo-targeted advertising campaigns. They likely have ways of determining the ROI of those campaigns. Still, they may decide to delve deeper and look into whether differences in location or ad distribution affected the products that were ultimately purchased. This requires understanding how data is organized, where it “lives,” and whether it’s up to date.
Why data modeling is important
Teams like marketing, finance, and sales have not always had an easy way to explore the kinds of data relationships because data scientists spend approximately 80 percent of their time collecting and cleaning data. So, presumably, traditional methods would take the average business person a lot longer. And making use of data is only getting more difficult. Global data consumption increases by 50 percent every year, and experts predict that in a few years that number could reach 70 to 80 percent. Sheer volume alone makes sorting through data a problematic endeavor. But then determining what to use and how to do it accurately makes it exponentially more challenging.
Data modeling allows analysts and domain experts to make meaning of all the data your organization has been collecting and generating. It helps identify business problems as well as solutions. Because data gets provided to business experts with better context, they can use it to create more meaningful, actionable insights. These actions may include improving the quality of data and databases, advancing internal practices and processes, providing better customer solutions, services, products, and more.
Despite these benefits, only about half of companies use data modeling. In fact, only 31 percent of executives say their organizations are data-driven. For many of them, it’s not the lack of motivation that keeps them from taking on data-oriented initiatives. They may not have the resources or the right expertise—and others aren’t sure how to implement them effectively.
of a data scientist’s time is spent collecting and cleaning data.
Data modeling solves common data challenges
Organizations generate and collect troves of data every day. Yours may be storing data about customers, employees, products, sales, and so much more. That data exists throughout organizations—in applications, tools, and programs—and often gets siloed within departments.
Business intelligence and operations teams often take charge and create a secure and reliable data warehouse to centralize that data. But making use of all that data can be tricky. Common user problems with extracting insights from data include:
Utilizing the right data
Not knowing all the data available. For example, domain experts may know the organization has collected customer phone numbers and addresses, but they may not know that many have provided specific product preferences. This knowledge will improve the predictive analytics they want to create and may ultimately lead to new product development and increased sales.
Finding the right data. Business leaders may know data exists somewhere within the organization but can’t find it within the tools and systems they’re able to access.
Wasting time looking for data within complex data structures. Domain experts in search of specific data may be unfamiliar with the organization of a particular system and get lost trying to find it.
Generating accurate insights
Using data that is outdated by the time users can organize it. Some data takes so long to collect that it’s no longer relevant by the time it can be organized, let alone analyzed.
Determining data accuracy. Domain experts are forced to use the data they have, but they may not have any way of knowing if it’s flawed or if better data exists. Sourcing the right data is paramount when trying to make the most informed decision. Using flawed data—or data that isn’t endorsed by data teams—creates erroneous reports that teams should not use to take action.
Misunderstanding what data values represent or misinterpreting data that is named in complex or obscure formats. Without standardized and organized processes, users can’t even begin to make sense of what their data means. Even when a column is comprehensible, it’s not always clear that it is valid or useful.
These issues cause business teams to make well-intended decisions based on flawed logic and inaccurate reports. These problems also may cause business leaders to make mistakes or miss crucial opportunities they could have taken if they had the right information at the right time.
Data modeling best practices
Data modeling can only prevent and solve data analytics challenges when done the right way.
When building data models, there are best practices that help produce optimal results. These are some essentials:
- Limit domain access to a single point. This improves data security and helps achieve compliance.
- Define your organizational goals and objectives. Performing this exercise before creating data models helps ensure you get answers that are relevant to your business.
- Standardize naming. Standardization saves time because it’s easy to query the necessary data, and it ensures result accuracy because you can be sure you’ve identified everything you need.
- Use processes that are scalable and adaptable. Design models to hold up as data loads increase.
- Design a system for appropriate accessibility. For example, ensure data models are accessible via different systems, but limit set up permissions by user role.
- Validate models. Perform accuracy and stress tests on your data models to make sure they work as intended.
- Automate reports. Ensure dashboards update in real time, and that reports can be automatically sent to the appropriate stakeholders.
By following these best practices, you’ll get better results and ultimately enable business teams to make better decisions.
Want to build a data model with Sigma? Read our best practices guide first.
Why data modeling is evolving
Data and IT experts have traditionally owned data modeling. It required coding abilities and was too complex for those without a background in data engineering. Whether they liked it or not, data professionals were the gatekeepers of data. In some cases this may not have been totally necessary, but the technical limitations required data experts to manage models and act as the intermediary between domain experts and the data they wanted. This often takes time away from solving higher-value problems.
The steps to get business experts the information they want can be complicated and follow standard frameworks. These approaches often require time-consuming conversations between data teams and other business units. Data teams must try to communicate the kinds of questions that are possible to model, while the data owners and stakeholders must communicate what they want to find out. It becomes a game of telephone, leading to constant back-and-forth conversations before self-service analysis could get underway. And without the ability to run queries themselves, domain experts sometimes ask for reports based on limited understanding of what’s possible.
As the amount of data organizations collect and generate grows, the old methods of data modeling become increasingly restrictive, time-consuming, and even frustrating for business teams that understand the value of data, but can’t put it to good use. This paradigm has left both the data teams and the domain experts wanting more—leading to the emergence of tools that take new approaches to data modeling.
These include automated and visual techniques, that simplify the modeling process, clearly define datasets, and allow those traditionally left out of the conversation to have a greater understanding of the data available to them, and even play a role in modeling the data themselves.
By eliminating the technical requirements and providing a way for those closest to the data—the business teams—to merge and structure disparate data sources together in a way that resembles business, you open the door for faster insights and less hand-holding by the data teams.
The benefits of simplifying data modeling
More than 54 percent of executives have said the greatest competitive threat to their organizations is lack of agility when it comes to data. So it makes sense to adopt a solution that speeds up the modeling process and allows for more people to play a role in shaping a data model and producing insights faster.
Fortunately, like so many modern business tools, data modeling solutions have evolved to be more accessible and user-friendly—connecting data owners with data models without a reliance on code. From automating modeling of new data tables to suggesting data joins, to providing improved UIs and visual descriptions for greater context, these tools have simplified the process —making it easier for the less technical to get involved in modeling.
These are ways everyone in your organization can benefit from these changes:
Better data modeling means it’s no longer just data professionals who can create, update, and manage models. Data team members can still support or coach others if necessary, but domain experts in sales, marketing, finance, human resources, and elsewhere can get the visual models they need with minimal help from busy data teams. Tools that support a self-service data culture encourage curiosity and learning. They also allow employees to be more engaged and take initiative, which can lead to unexpected sources of innovation.
If your data modeling tool, and underlying data model, are approachable to people outside the data team, then more people can be involved in the modeling conversation. This encourages greater involvement in the modeling process and ultimately can unlock both data access and insights at a faster pace.
Better business intelligence
By bringing business teams into the modeling conversation, data teams can leverage their domain expertise to create more robust data models that are easy to understand and lead to better, faster insights. Simplified data modeling allows greater understanding and collaboration among all teams and users.
Be wary of solutions that limit users to pre-defined data models. This prevents domain specialists across your organization from using their expertise to answer questions and explore ideas. However, done correctly, modeling makes data accessible and consumable in a more intuitive way.
Choose the best data modeling tool for your organization
If you want more users to access, integrate, manipulate, and analyze data with minimal help from data specialists, choose an analytics solution, like Sigma, that:
- Provides self-service capabilities and allows data modeling to be a two-way conversation between data teams and domain experts. Be wary of tools that require you to play gatekeeper.
- Empowers experts across teams to work with visual data models that don’t require coding.
- Is valuable to all users, from non-technical experts who don’t write code to SQL users.
- Provides up-to-date data because you don’t have to submit requests to long queues.
- Unlocks real-time, collaborative analysis—in many cases shortening query times from minutes to seconds.
- Scales easily because new database tables get added automatically to your model, and data owners can add descriptions to new data immediately.
- Automates report delivery with scheduled emails to data stakeholders.
- Provides industry-standard security and compliance thanks to a single point of access, permissions by team and namespace, metadata encryption, and other protective features.
- Connects with popular data warehouses or can be integrated with your data source of choice.
- Evolves as your company grows, accommodating enterprises of all sizes.
If you’re interested in the more technical side of data modeling, some of the features to look for include:
- Links— Add data to worksheets with just a few clicks.
- Datasets— Easily create reusable analyses.
- Materialization— Write datasets back to your cloud data warehouse as a table.
- Definitions and metadata— A friendly UI enriches data objects.
- Collaborative interface— SQL professionals and business experts work together in a single environment.
- Object badges— Data team members can mark data blocks, worksheets, and dashboards as “endorsed,” “warning,” or “deprecated.”
When choosing a solution, talk to your organization’s domain experts to discover the features they want.
Try Sigma’s visual data modeling for yourself. Sign up for free and get two weeks to build the perfect data model.
Put your data to good use
All that data coming in can be a treasure trove for your organization. You want a way to make the most of it. You’re also investing in collecting and storing it, so it makes sense to maximize the return on these investments.
Whether you’re already using data modeling and want to improve the process to include more users, or whether you haven’t started due to the complexity of it all, a simpler data modeling solution can be the answer. Organizations that are fueled by curiosity-driven insights are more likely to flourish in this era of self-service data cultures and technologies. Fortunately, tools have evolved to accommodate users of all kinds. Adopting such a tool and following data modeling best practices will allow the domain experts in your organization to capitalize on your data.