How you model data now can have a big effect on its utility in the future. As our use of data expands at a rapid pace and more members of a company’s team become involved in day-to-day data efforts, every step that you take to organize your data should be optimized for inevitable migrations into bigger and better platforms and databases. If it sounds like a lot to wrap your head around, don’t worry — we’ll go over best practices for data modeling so that you can get your data prepped for what’s coming.
What is Data Modeling?
Data modeling is a blueprint of sorts for how a company’s data is organized in a database and how each piece of data relates to each other. Looking at the model, any employee in the company should be able to trace the path of a piece of data, including its precise location in a database, as well as make inferences off of it. Think of it like a map, with clear-cut directions for wherever it is that you want to end up and what you can pick up along the way.
Why is Data Modeling so Important?
The value of data modeling extends far beyond its purpose as a location tool. For all organizations, effective data modeling is a necessary component of data migration, which is an inevitability as business’s relationships with data becomes more robust and interdependent.
Data migration is the process of integrating existing data into new formats or applications — so if your company switches over to a new computer system, or if it combines multiple platforms. Without proper data modeling, migrating this data over can become incredibly burdensome and confusing, and could interfere with operations on levels both big and small.
Data modeling is also critical for the end-to-end data management style that sits at the foundation of functional usage of intelligent analytics. The more complex the data structure you work with, the more necessary it is to model the data in a clear, concise, and accessible way. After all, it’s not true universal data if it can’t be effectively understood and migrated from one system to another.
Data Modeling Outside of the IT Suite
If you’re a general office worker, you’re probably wondering how (and if) any of this applies to you. Isn’t data — in its structures and its handling — the purview of the IT team? Well, not quite.
Every single employee who works for a company has a role to play in facilitating proper data modeling across the organization. Essentially, if you contribute to your company’s data set in any way, be it through logging sales, tracking customer service calls, reporting on social media analytics, or any other type of task that’s not directly IT-focused, the way you model that data has an effect on the universal data structure as a whole. And for that reason, it’s important that you know how to do it right.
Now, data modeling outside of IT isn’t necessarily database-centered. Unless you work with data structures directly, you’re probably never going to see the actual map of how all of your company’s data ties together. Instead, your job is to set up each piece of data to succeed in the structure by following data modeling best practices that can be efficiently translated by others — both in the present and in the future.
What do those best practices look like then? Let’s take a look.
Data Modeling Best Practices
Follow the advice below to ensure that the data you collect can be put to use, as well as streamlined into existing data structures and upcoming data migrations.
Understand the big picture goals of your data
Businesses don’t just collect data for the sake of data. While it’s true that not every single piece of information that you log onto your system serves an equal degree of value, it should all be driving toward the same goals.
Think about your own designated role in the company and what you set out to achieve every day. Whether you’re in marketing, sales, purchasing, or so on, you hopefully have a clear idea of why you collect the data that you do, and what the higher-ups and stakeholders are hoping that data helps accomplish. If you’re not completely sure: ask. You need to know what greater purpose your data is serving if you want to understand how to optimize the organization of it.
If you know what the data you collect is trying to achieve — be it increasing sales, improving the customer experience or otherwise — then you’ll have a much better idea of how to model it in a way that sets it up for success.
All data is not created equal, especially in terms of how it relates to your larger objectives. The utility of a data model ultimately comes down to its simplicity — even when it’s complex, it needs to be succinct. If you add data in to the mix that doesn’t serve a notable purpose, then you muddy the waters of your data model and make it harder down to line to migrate that data into something new.
Before logging a piece of data, ask yourself: How does this piece of data fit into the larger picture? Is it directly related to the goals that we are trying to achieve in this department? Does it bring true value to the full data set?
By being selective with the data that you add to the model, you help support its structure for long-term use. Don’t be so selective that you risk omitting a key piece of information, but do use your best judgment insofar as deducing what pieces of data are directly related to your big picture goals.
Work from a template
You don’t have to be an Excel wizard to design a template that’s intuitive in design and purpose. Work with what you know, and create a template for your data that makes it easy on your end to see where you need to plug in a certain data point and how it relates to the whole. A good template simplifies the process of comparing and combining different fields of data and makes it easy to visualize how everything works together.
If you’re not sure where to start, just wing it at first. By creating a first draft of your data template — even if it is a far cry from what the end result should look like — you’ll get a jumping off point that you can then mold into something more useful. Just start simple, and build it up from there.
Keep an eye out for errors and redundancies, and in the case of the latter, combine similar data points into one column or table. For more guidance on how to create data templates in an Excel spreadsheet, head here.
Center your data in time
Time, be it years, months, days, hours (or better yet all four), is one of the most crucial functions of any appropriately modeled data set. Time centers your data in place and makes it easy to make comparisons. It also makes it easy to pull actionable insights from what you’re seeing. What’s more valuable: seeing that Instagram is your company’s most active social media account or seeing that Instagram is your company’s most active social media account and especially on Thursdays at 2pm and Saturdays at noon?
Ultimately, adding in time data is necessary for drawing connections that might otherwise be invisible to you. Pull up data points around a new marketing campaign launch, for example, or a special promotion. Time means less guesswork when it comes to correlations and causations, and means that your data is that much easier to place when it comes to future migrations.
Automate whenever possible
You can’t talk data and the future without talking about automation. Automating your data means less work on input and better predictions on output. It also means that data can be worked automatically into your company’s larger data structure instead of needing to be entered manually.
Automating data isn’t quite as tricky as you might think. Work within your team to find a software solution that allows you to quickly bring automation to the table in a way that makes the most sense for your budget and your objectives. If you’re in marketing, for example, things like customer relationship management (CRM) software and automated email marketing are incredibly smart investments both for the health of your data models and the utility that you get out of those models. If you’re in human resources, look into HR process automation software that’s designed not just to collect, store, and organize data but to improve productivity and reduce employee turnover.
Failing to automate your data sets is a recipe for errors and inefficiencies. Find out if it’s an option for your department, and if so, which automated software solution is the best fit. There are plenty of options out there, so you should be able to find one that checks off all of the right boxes.
You don’t need to be a developer, data scientist, or software engineer to participate in your company’s data modeling initiatives — or to look at the data you collect and make real valuable inferences from it. Following the best practices for data modeling above serves the dual purpose of facilitating easier integrations in the future and providing for on-the-ground improvements in efficiency, productivity, and predictability that can help you excel at your job.
Every employee can lend a hand toward optimizing an organization’s larger data blueprint. By doing so, you enable growth that benefits both the stakeholders and the staff, and that can help businesses succeed in the ever changing data landscape.