A unified data model brings together data from different sources and platforms in one place so that all your data is considered when conducting analyses and making decisions.
What is a unified data model, and why is it important?
Most companies have multiple data sources, platforms, and ecosystems that operate in a void. Maybe they connect to an internal tool, or an analytics platform that only a handful of teams use for a very specific use case.
In such scenarios, it’s highly likely that certain data points are overlooked when important decisions are made simply because those data points aren’t included in the dataset that was used in the decision making process.
A unified data model bridges together various disparate data sources—like CRMs, BI tools, product analytics platforms, ERPs, and others—by consolidating them into a single data set or warehouse. This provides you with unified data, enabling you to run analyses and make decisions while being cognizant of every data point.
Unified data is important because it enables you to look at every collected data point so you can understand the entire data narrative. Without it, you’re left with multiple silos of (often incomplete) data which can obstruct the bigger picture—and thus prevent data from being accurate, effective and actionable.
What is unified data management?
Unified Data Management (UDM) is a framework that enables unified data by implementing processes to consolidate data from disparate silos by identifying integrations between data sources and storing the unified data in a common data warehouse.
Directing data from various silos to a single system is easier said than done, however, since platforms used to gather data are often incompatible. Each platform is unique in the way it operates and has its own data model, which means it may not speak to other platforms, or have a compatible interface. Making the most of all your data requires a well implemented unified data model.
Key considerations before creating a unified data model
Before figuring out how you want to unify data, it’s important to consider the following:
Business Specific Data Goals
Each business has unique goals, and thus the collection of—and reporting on—data needs to be specific to these goals. Unified data is most valuable when it adheres to business specific data goals and the unified data management process should begin by defining these goals.
Compatibility of Data Sources
It’s important to know which data sources and platforms are currently in use so you can understand which ones are compatible, which sources need to be converted, and what central platform fits best in your current stack.
Data access and democratization
It’s also important to identify who will need access to the data, and what platforms they’ll use the data on. Finding commonalities across all the teams that will use the unified dataset can help in understanding what data model works best for your organization.
Making a unified data model work for you
Making a unified data model work for you requires three primary steps:
Extracting and Importing Data
To start, you need to ensure that data can be extracted and imported from its original source, like a CRM platform or an analytics tool. Once the data is extracted, it needs to be imported into a central platform where the data from other sources will live as well. Structured data, like a CRM database, is easier to extract and import than unstructured, like documents and audio files.
Data Conversion and Storage
As mentioned above, datasets and platforms are often incompatible which makes importing various datasets and connecting them difficult. This necessitates converting data (deduplication, restructuring, etc) to make it readable within the central platform. Once converted, the data can then be stored for future use.
Data Reporting and Analysis
This is where unified data creates real value. The data from a central platform needs to be able to be read and interpreted so you can run analyses on it and create reports on important metrics. This reporting and analysis can either be done on the central data platform with a connected analytics tool, or on another system or content repository post-data export.
Characteristics of a successful unified data model
Ensuring that your unified data model works well for the specifics of your business is important for its success, but there are several traits that every unified data model should have.
Data collection, storage, and management are continuous processes. As the amount of data you have builds over time, your unified data model needs to be able to scale up to handle the increased volume, as well as any potential increase in the types of data it has to manage.
Flexible and integratable
To ensure compatibility as your data collection and tech stack grows, a comprehensive unified data model should be flexible enough to integrate with new platforms and data sources. Ideally, your unified data model should be able to integrate data from each platform with limited manual intervention, and preferably be able to sync changes in the unified data with important tools like product analytics.
Structured and Intuitive
Unified data models are meant to be extensive by design. This will result in a ton of data that will need to be sifted through for reporting and analysis. A unified data model needs to be well structured and intuitive enough to be usable by all the teams that need access to it, despite the complexities of the underlying data architecture.
Data, unified or not, is no good if the people who need it don’t have access to it. A unified data model should be accessible across your business so that teams that need access to data are able to use it to make the right decisions. Democratizing data eliminates bottlenecks in analysis and ensures that insights from data are shareable across the organization.
Automation makes it easier for teams to analyze data even as the amount of data collected and stored grows. Automation can happen at various stages within the unified data model—from the moment data is generated, to importing, reporting and analysis. This can also be used to create automated anomaly detection and notification, which can help you identify key problems as soon as they arise.
Making the most of your data
Data can be empowering, but when fragmented, valuable insights will likely be overlooked. Understanding your users is only possible if multiple data sources are pieced together to give you data and insights across the entire user lifecycle. This can only happen when data ingested from multiple platforms, such as product analytics, is unified with other platforms, like a digital experience or session replay tool, to give you a more complete picture of your users and their product usage. A unified data model will not only make data storage more manageable and comprehensive, but can also help to future proof data reporting and analysis as your business grows.