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unified data models

In 2019, a research paper conducted by Market Pulse showed that companies on average use data from over 400 different sources and more than 20% used data from over 1000 different sources. Aggregating various types of data with unique use-cases is an analytical nightmare – one that needs a structured form for data-processing. As data sources, repositories and platforms operate often independently, being able to sync, and leverage the data allows end users and companies to appropriately engage and make informed decisions. This is where unified data models come into play.

What is a Unified Data Model?

A Unified Data Model, or UDM, combines a plethora of heterogeneous data sources, including ERPs, CRMs, BI analytical tools, Supply Chain Management models and more by creating a single point of access that centralizes all the data. With this data centralized in a data warehouse, organizations or specifically data scientists, can run analyses and create advanced machine learning algorithms to best optimize for each scenario.

UDMs act as a schema that de-clusters data from disparate sources through integration identification between similarities within the data sets. Once this is confirmed, the data is then kept in a unified data warehouse.

Although UDMs are essential tools for any company serious about using analytics to grow and extract, UDMs pose some challenges.

UDM Challenges

Data Cleaning

As UDMs consolidate data from various sources, it is quite often the case that platforms don’t necessarily behave appropriately as they are not compatible with each other. To resolve this issue and ensure each data warehouse doesn’t become unstructured and messy, it is imperative that regular data cleaning occurs. Despite this added cost of maintenance, data cleaning is a best practice regardless of the situation and implementing it in your businesses regular routine is highly productive.

Why is it Important?

There are many benefits to having all your data unified in a common data warehouse. From optimizing efficiency, improving accessibility and more, companies can leverage UDMs to create high-level virtualization and scalable solutions. Moreover, UDMs increase productivity, allow for more advanced and in-depth data predictive modeling as well as reduce costs in the entire data analytical process.

Paired with the fact that in today’s evolving and dynamic environment, data is literally money, optimizing and predicting it can be gold. Thus, leaving this potential opportunity on the table due to sub-optimal data practices can be quite foolish.

How to Use a UDM

UDMs can be complex, but to make the most out of them, it is best to simplify and streamline data sources in order to normalize and create elegant raw data that can then be transformed into robust computational models.


Compatibility is vital to analyze your existing data sources and tools to know which ones are a priority and which ones are redundant. Once that is done, you can identify which ones are consistent with your UDM and find the ones that need to be converted.

One key thing to note is the difference in structured vs unstructured data. In a previous article, we talked about how structured data and unstructured data differed, their unique characteristics and how to administer them from a top-down approach. The golden rule is that structured data is always easier to consolidate and bring on-premises.

Data Access

As with any data analytical tool, knowing who the target demographic is in regards to usability and accessibility is imperative. Within your organization, ask your data scientists what platforms work for them and what centralized values would help them make the most out of a UDM. This will not only create a smooth transition, but will also boost the employee experience.

Goal Setting

Finally, goal setting. For any project, large or small, defining the objective and goals help keep you on track. Is the UDM going to be used for reporting, real-time analytics, predictive models, etc. or is it going to be used for scaling your existing architecture. Whatever the found use-case is, defining the aligned goals with input from your data analytics team, will ensure a smooth shift.

Characteristics of Successful UDMs

Depending on the size of your organization, certain UDMs will have different characteristics that are essential for success.


First, scalability. Data collection and storage especially from over hundreds of sources if not thousands can lead to an abundance of data. As this data piles up, your UDM must scale to digest the ever-increasing copious amounts of data. Moreover, it will need to be scalable in the sense of the types of data it accepts and manages.


Paired with scalability is agility. Unified data models are investments that need to be able to quickly transform and keep up with new and tougher demands. While selecting a UDM, it should ideally allow data from various sources and platforms automatically. The key word being automatically, as manual inputs take away from the benefits of UDMs. The flexible nature of a unified data model will allow organizations to perform not only ad-hoc tasks but also integral analytics.


Next, is accessibility. If your data is unified but the right people don’t have full access to it, it’s of no use. As with any major revamp, companies must ask the right questions to the right people, in this case your data analytics team and data scientists.


Finally, as unified data models plug in from countless designations, they are exhaustive and extensive. This means there will be messy data that must be organized and cleansed before actually used for analytical purposes. As we mentioned earlier, structured data is much easier to digest, but as companies use more and more unstructured data, there has to be filters that can prevent spillage or overflow. Once that is established, then only can UDMs create effective, efficient value.


To summarize, companies are growing at a faster rate than ever before in a world that is powered by data. While data can be powerful, inappropriate, and ineffective management of it, will result in a lacklustre result which will be too costly, fragmented, and overall poor. Hosting different, individual data models for different sources and then maintaining them, monitoring them, and updating them is far too futile. This is where a unified data model can help ingest data from thousands of sources, different platforms, and essentially bridge different suites of systems together, allowing companies to analyze, synthesize and examine data. Implementing a unified data model will most definitely take your organization to the next level.

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