Should You Consider a Unified Data Model?

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Read more about author Nahla Davies.

A unified data model allows businesses to make better-informed decisions. How? By providing organizations with a more comprehensive view of the data sources they’re using, which makes it easier to understand their customers’ experiences. 

A singular, interrelated network that’s connected to one source of truth gives organizations a more efficient, accurate, and comprehensive analysis of their user performance. Considering the fact that, as of 2019, companies on average work with data that’s coming from more than 400 sources, having that singular network connected to a single source of truth is more important than ever.

So, should your organization consider using a unified data model? Maybe so – let’s talk about how unified data models can give you more reliable insights so that your organization grows more quickly. We’ll also cover a few of the challenges that this model presents to help you determine whether using one aligns with your business goals and plan.

Unified Data Models: What Are They and Why Are They Important? 

Unified data models (UDMs) centralize data from heterogeneous data sources (think CRMs, ERPs, or BI tools) thanks to a singular access point. All this data gets stored in one data warehouse, which lets a business’s data teams analyze all that centralized data to come up with AI/ML-based learning algorithms. 

You can think of a UDM as a database schema; UDMs use integration identification to de-cluster data stored in various places. After this de-clustering occurs, all the data from those disparate sources gets stored in a single data warehouse.

The single most important thing about UDMs is that they allow organizations to view all the data points they collect, which means they also get to view the complete narrative their data tells. In the absence of a comprehensive data narrative, organizations are left to deal with lots of silos that store potentially incomplete data. 

If you’re reading this, you probably know how big of a pain it can be to sift through multiple data silos, and you’re probably not surprised to learn that companies using manual processes for their standard operating procedures spend an average of 19% of their workweek searching for data. With unified data, though, organizations get data that is both actionable and accurate.

Before Creating a Unified Data Model, Consider These Three Things

There are three big things to consider before you create your first unified data model. The first thing to think about are the data goals you have that are specific to your business, as well as the ways you want to collect and report on your data. Your unified data is only as valuable as the specificity of your data-related goals. This is also a good time to start thinking about how best to coordinate your business units to unify your data processes.

Second, you’ll want to consider which of your data platforms and sources are currently being used. By knowing which platforms and sources are in use, you’ll be able to understand the compatibility of your data sources and determine which ones you need to convert.

Last but not least, you’ll need to figure out who’s going to be accessing your data and the data platforms they’re going to use. You’ll have a much easier time figuring out which UDM is best for your business if you can identify different things in common among your data teams.

Making Your Unified Data Model Work for You 

Making your unified data model doesn’t need to be complicated, but it does require that you follow a few important steps. You must make sure you can extract and import your data to the same platform in which your other data will be stored. Remember that it’ll be easier to extract your structured versus your unstructured data – you’ll have an easier time extracting and importing a CRM database, for example, than MP3 files or documents. 

Also, keep in mind that importing and connecting disparate datasets can be difficult if they’re incompatible. To overcome this challenge, you need to convert your data so that it becomes readable in your single storage location. The data that you store in your central platform must be readable so that your data teams can analyze and report on it.

What Challenges Do UDMs Present?

Because UDMs aggregate different sets of data that are stored in different places, it’s not uncommon to run into data platforms that aren’t compatible and therefore don’t behave as intended. To overcome this issue of incompatibility, you’ll need to make sure that you’re regularly cleansing your data to prevent your data warehouses from becoming too disorganized. While it’s true that you’ll incur some additional maintenance costs by investing in regular data cleansing, it will prove to be more than worth your while in the long run. 

As you’ve likely gathered by now, there are plenty of benefits that organizations can enjoy when they unify their data in a single storage location. Whether it’s improved efficiency or better access to data, UDMs allow your organization to work with scalable solutions and virtualization on a high-level basis. 

What’s more is that organizations can watch their data teams become more productive thanks to UDMs, and their process of data analysis will incur fewer costs and benefit from advanced predictive data modeling. At the end of the day, and for better or worse, data is currency in our modern hyperconnected world; the power to optimize and predict your data is highly coveted, and rightly so. By overcoming the challenges that UDMs can potentially present, your organization can also overcome ineffective data practices. 


Thanks to the endless points of data available to us these days, organizations are enjoying growth rates heretofore unseen. There’s no question that data – and lots of it –can empower businesses and give them greater insights into the ways their customers behave. 

What’s also certain, though, is that suboptimal and ineffective Data Management delivers underwhelming results that are both expensive and fractured. It’s no longer enough (or feasible) for organizations to host disparate data models while also trying to maintain and update them. 

Thankfully, UDMs make it possible for you to solicit disparate data sources and ingest data from myriad platforms to gain a more comprehensive view of the data you’re using and connect your multiple system suites.