DATA ANALYTICS

The Benefits of Building a Great Data Model

Devon Tackels

Content Marketing Manager, Sigma

You can check your smartphone for the current temperature, the weekend forecast, and even weather patterns of the last century. Similarly, your streaming apps track your viewing history and offer suggestions for shows you’re likely to enjoy. So when you get to the office, why is it so hard to get your hands on data you need—and even harder to make sense of it?

Employees want access to data within their domains, and they want to be able to analyze it with the ease and intelligence available to them outside of the office. Members of your finance, sales, marketing, product development and other teams all want insights they can use to improve results. They may have questions like:

  • Can we predict monthly sales figures for the year based on daily sales?
  • How are customers using a specific product?
  • Which marketing campaigns performed best, dollar for dollar?

There’s no limit to the questions that domain experts can ask. They’re specialists in their fields and are interested in more than KPIs. They may have access to the reporting tools and dashboards in various platforms— but they often can’t connect that information in a way that answers their pressing questions, or helps them tell the stories they need to be successful at work. For example, over 90 percent of B2B marketers said they have trouble aligning sales and marketing data in their multichannel campaigns.

On the other hand, if business leaders rely solely on the data team, getting the right answers takes time. The data experts may be overwhelmed by ad-hoc requests, and it often requires some back-and-forth communication to understand exactly what domain experts want to know, as well as for them to understand what’s knowable.

How do you make data accessible to your domain experts in a way that makes sense to them and that allows them to extract insights? Data modeling plays an essential role in making this happen. Good data models ensure accuracy, context, and trust between teams— especially if your organization is one of the many building a data-driven culture with a self-service analytics tool.

What Makes a Great Data Model?

Not all data models are created equal. Before you can see maximum benefits, you should make sure you have the means to build quality data models. They should be:

  • Simple and accessible. Domain leaders outside of the data team should be able to understand data tables and relationships, and even generate models that help them analyze data quickly without intervention from data teams.
  • Consumable. Data sources and tables should be presented in a way that’s easy to understand using clear descriptions and naming conventions that are known across your organization. It should be easy to determine if a data source is accurate, up-to-date, and endorsed by the data team before they begin analysis. 
  • Adaptable and scalable. Adding or changing data within the warehouse shouldn’t affect the model. As your organization and data sources continue to grow, thinking ahead and adopting a flexible approach to data modeling can make your life easier.

These characteristics allow you to maximize the benefits of data modeling.

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Want to build a data model with Sigma? Read our best practices guide first.

3 Data Modeling Benefits

Once you’ve made the efforts to ensure your teams are working with good data models, you can start seeing the benefits. High-quality data models help in these ways:

 Business leaders make sense of all the data your organization collects

Your data warehouse is like a construction lot full of building materials. You know the material is valuable, but you can’t build anything until you have a blueprint. Up to 73 percent of business data can go unused, but a data model serves as the blueprint for your data. When business experts can easily understand a data model— and modify it themselves —they can make use of much more information. A data analytics solution, like Sigma, provides the first step to building something useful from your data, all without relying on the technical coding skills required in the past.

 Domain experts make better business decisions because they have better context

Some examples of ways data models can advance business include improving the quality of data and databases, streamlining internal processes to get products to market faster, increase sales by developing better products, and improve customer experiences.

It’s easy to misinterpret data and come to faulty conclusions when you source the wrong data, don’t use the latest endorsed data, or use data out of content. For example, your team may discover that the blue sneaker is your company’s best-selling shoe, and draw the conclusion that it’s selling because of the color. With a good data model, it can be easy to dig deeper and realize that the blue sneaker is actually made of different materials and most popular in specific regions or times of year. With that information, you won’t jump to the conclusion that your customers prefer blue. There may be more to the story for you to explore.

 Data and business teams build trust and work in collaboration

Trust between data teams and other teams within an organization can sometimes be on shaky ground due to different perspectives, lack of a shared language, or even negative past experiences. However, teams can rebuild trust when they use data modeling effectively. It shouldn’t be a guessing game, which can sometimes happen when data owners aren’t as involved because they lack technical skills. When data and business teams work in concert, domain experts in sales, marketing, finance, product development, and elsewhere benefit. They know where and how to find the endorsed data they can trust to generate accurate and timely insights. And they can do this when they want without having to involve the data team—reducing business intelligence bottlenecks and time-to-insight.


Invest in a User-Friendly Data Modeling Tool

Chances are, your organization has more data than ever. That means you have to find a way to make sense of it faster—all while minimizing human error. While that may seem like a tall order, it doesn’t have to be as complicated as you might think. A new generation of self-service analytics solutions offer simpler ways to merge, explore, and analyze data housed in cloud data warehouses in minutes.

Here are the top features to look for in a data modeling solution.

  • Usability. Look for a solution that anyone in the organization can use, regardless of coding abilities. Make sure the data modeling tool doesn’t require SQL— or another proprietary language —so that domain experts can get involved in the modeling conversation. This will only restrict users, increase the time it takes to glean insights from data, and leave people frustrated. Modeling solutions that employ automation and visual interfaces can make modeling much more approachable for those traditionally left out of the conversation.
  • Speed. Allowing domain experts to handle more of their own data needs will cut down on communication time between data teams and business users. Don’t waste precious time meeting and emailing about the questions you plan to ask. Instead, use your time to formulate higher-quality questions and derive insights that can move your business forward.
  • Cloud-based. Cloud solutions integrate with popular cloud data warehouses, making it easier to access the data inside when you need it, without bugging the data team. They should automatically add new database tables to your model so users can immediately put the new data to use.

To make things easier, we’ve put together a Best Practices Guide on data modeling with Sigma. It covers how you can build collaborative data models that empower business users without compromising control and data security.

When you use data modeling with the right analytics solution, domain experts can play a more significant role in shaping data models, effectively adding calculations, definitions, and overall business context to data sets. This gives them the freedom they need to ask questions that spark innovation and better business processes.

Still have questions about data modeling? Get more information on data modeling in our definitive guide.

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