Unified Data Model

Data can live in many places, and that fragmentation can result in valuable information slipping through the cracks. A unified data model is needed to bring everything together in one place where better insights can be gained.

Unified Data: Bringing Your Business Intelligence Together

What type of data ecosystem does your company have? Chances are you actually have multiple data ecosystems that are segmented and on their own. 

Unified data, bringing multiple sources of data into a single platform, makes analysis much more comprehensive, organized and accessible. With all of the information in a central location data analysis is also more accurate. 

A unified data model (UDM) is the process and tools used to extract, funnel and store data from different sources together in one location. It can also refer to the schema or language that’s used to consolidate and standardize various forms of data using an application programming interface (API).  Essentially, a unified data model acts as a bridge between your data ecosystems.

Directing data silos to a single system is easier said than done given that companies often use incompatible hardware and software programs to gather data. There are CRMs, in-house servers, virtual servers, web applications, mobile applications, customer applications, ERPs and the list goes on. 

Each program is unique in the way it operates and has its own data model, which means it may not speak to other programs or have a compatible interface. There are also different types of data that aren’t read the same way and must be processed differently.

Making the most of the data that’s gathered requires the centralized intelligence of a unified data model.  

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How a Unified Data Model Works

A unified data model consists of three primary stages:

Data Extraction and Importing

The first step is to extract data from its original source (i.e. web application, CRM, etc.). The extracted data is then imported into the central platform. Structured data tends to be easier to extract than unstructured data (documents and files).

Data Conversion and Storage

Often the data needs to be converted so that it is readable within the central platform (i.e. restructuring, deduplication, etc.). Once converted the data is stored for future use.

Data Reporting

A UDM is only beneficial if users are able to read and interpret the data that’s collected. Your UMD should provide data reporting that merges related metrics. Reporting can either be done within the UDM (if you’re using an analytics platform) or reports can be exported to another system or content repository.

3 Considerations Before Creating a Unified Data Model

Before deciding on the best way to unify data, you’ll want to consider the following:

Data Goals

The UDM needs to be specific to your business and what you want to get out of the data. This becomes clear by defining data goals. 

Data Sources

Ideally, you’ll want the UDM to be compatible with all of your data sources. 

Stakeholders

Identify who needs access to the data. Finding commonalities across departments/datasets can also help you develop a UDM that works for all stakeholders. 

Unified Data Model Tools and Resources

A number of tools and resources are implemented in tandem to create a workable unified data model. They include:

Database

The database is where scalability comes into play. As more data is collected, the database needs to be able to scale up.

Universal APIs

APIs are sometimes needed to import data and ensure it isn’t corrupted. However, in order to collect data from numerous sources, you’ll need to use universal APIs. 

Analytics Platform

If you don’t have the time, resources and/or expertise to build and manage a proprietary UDM, an analytics platform is a good substitute and in some ways can be superior. Analytics platforms are expressly designed for data unification, although some analytics platforms are better made for unified data than others. You’ll want to find a platform that easily integrates with common systems, has universal APIs for uncommon data sources and has a good balance of usability and functionality. 

 

Characteristics of a Successful Unified Data Model

What makes a UDM successful? The answer is different for every business, but there are several characteristics every unified data model should have.

Scalability

As mentioned above, data management is an ongoing process and the amount of data you have builds over time. The UDM needs to be able to scale up to handle more volume and potentially different types of data in the future. 

Integration

The more data sources the UDM is able to integrate the more comprehensive data reporting will be. Ideally, you’ll want the UDM to integrate data without much manual intervention. 

Intuitive Usability

There will be a lot of data to comb through once everything is aggregated. A UDM needs to be easy enough for everyone to use despite the complex underlying data architecture. The interface should be intuitive and reports that visualize the data are also beneficial. 

Accessibility for All 

Another feature to look for in your unified data model is accessibility. Giving people across the business access to all of the data helps eliminate bottlenecks. Shared insights from other departments also give decision-makers related information that can prove to be highly beneficial. For instance, when the marketing team has access to sales data they can determine the exact revenue generation of an ad campaign.  

Automation

The more automated the process is, the easier it is for your team to analyze data even as more is mined. Automation can happen at various stages within the unified data model from the moment data is generated. The Mixpanel analytics platform goes beyond automatically importing data. The platform is powered by a machine-learning algorithm that’s capable of automatically creating reports for data analysis and detecting data anomalies

 

Data is empowering, but if data is fragmented valuable insights can be overlooked. A unified data model will not only make data mining more manageable and comprehensive it can also help to futureproof data analysis as your business grows.

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