What is Data Unification?

Data unification is part of a broad category of services that gather data from different sources, and with different attributes, into a single trusted, unified view. It covers a range of data types—not only simple attributes like contact info, but also relationship, interaction, transaction, and reference data—and degrees of resolution, from structured to semistructured and unstructured data. With multiple types of data interacting in distinct ways across various platforms, many enterprise businesses find it hard to harness the full value of their data. This is where data unification comes into play. Data unification is made up of three layers of services:

  1. Multi-domain master data management
    Augmented capabilities that create and care for a single source of truth for data, drawn from many sources.
  2. Entity resolution
    Capabilities that identify and resolve duplicate data records.
  3. Data products
    Data models, the data fed into them, and key performance indicators of data quality and governance.
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Why Unification Of Data Is
Important For Business

Unifying data helps organizations improve data quality and enables business users to effectively manage risks, find efficiencies, and drive growth. Data unification provides:

Trustworthy, interoperable data

Data spread across multiple platforms is prone to inconsistencies, errors, and duplication. Unified data from a rich set of sources leads to trusted and high-quality data sets that can be used across different use cases.

Business responsiveness

Using data from many different sources, like CRM systems, support tickets, and website interactions, helps organizations rapidly gain a 360 view of their data, enabling them to meet market opportunities and customer needs better.

Improved productivity

Unifying data cuts down on the time and effort needed to gather and process data, helping teams focus on more strategic tasks.

Data-driven decisions

With access to trusted, comprehensive views of all their data assets, companies can better support their decisions, identify trends, and adapt business strategies.


Unification drives interoperable data sets and promotes data collaboration and data sharing across different departments and business units.

Scalability and growth

With business growth comes data growth. Data unification gives businesses a structured, well-governed framework , making it possible to grow without getting tangled up in data management.

Why Data Unification Is Important To Customer Data

Data unification can help B2C organizations, in particular, improve their customers’ experiences through:

  • Simplified decisions and operations
    Unified data can make digital transactions and engagements smoother. Limiting how often a user needs to find or enter unique information—shipping details, for example—makes it easier for them to make a decision or finish their transaction quickly.
  • More personalized experiences
    When an organization treats people personally, by tailoring their recommendations or giving them relevant content, people tend to feel more satisfied about their overall experience. By unifying data across customer engagement touchpoints like past purchases, browsing history, and account profiles, businesses can improve customer intimacy and make people feel valued and understood. These efforts bring concrete rewards in repeat business and brand loyalty.
  • Coherent communication and omnichannel experiences
    Coherence means trustworthiness. If a company sends an email offer to a customer, for example, data unification means that same offer can be shown on the website or on a mobile app. It also supports better-targeted marketing campaigns and loyalty programs, making sure any such offer matches what each customer actually cares about.
  • Better service
    Data unification can improve customer service, and customer service can make or break a B2C business. Consider the difference between getting help from a support agent who already knows your profile, purchase history, and current issues or getting help from an agent who doesn’t.

How Is Data Unification Different From MDM?

While data unification and master data management (MDM) are closely related, they serve different purposes. Data unification deals with integrating data sets from diverse sources. Master data management concentrates on managing key data entities and maintaining a single source of truth.

Data unification refers to the larger process of bringing together data from disparate sources to create a one unified view. It’s about cleansing and normalizing data, creating unique identifiers for each entity, and identifying and merging duplicates into trustworthy entities. Data unification considers broad data attributes—like relationships, interactions, transactions, and reference data—and types of data, whether structured, semistructured, or unstructured. Essentially, data unification supports better analysis and more informed decision-making through one unified and comprehensive view of data.

Master data management is more specialized. MDM focuses on governing an organization’s core or master data, which often includes critical entities like customers, products, employees, and other core business elements, to create a single, consistent, and authoritative source of truth across all systems and processes in an organization.

Data Unification, Step By Step

Data unification involves collecting, cleansing, normalizing, and harmonizing data from different systems, formats, and databases, then consolidating this unified data in a searchable and organized repository. The steps in unifying data are:

  1. Data consolidationThe first step is ingesting data from internal and external sources. This data may have different formats, such as structured databases, unstructured text, or real-time streams, and usually has different degrees of quality. The ability to ingest data in real time is important for trusted interoperable data so data latency does not lead to outdated data sets that some users cannot trust for operational use cases.
  2. Data cleansingAfter it’s collected, data is cleaned and transformed for uniformity. Inconsistencies, missing values, and errors are corrected. Data is standardized and redundant entries are merged into newly authoritative entities.
  3. Data storageUnified trusted data is stored in a repository to support different teams and business processes
  4. Data integrationTrusted interoperable data needs to be shared across the enterprise when it is needed where it is needed. Data integration enables timely data mobilization to support different users and systems.
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The Difficulties Of Data Unification

A company might face as many challenges to data unification as their data types, interactions, and sources, including:

  • Data silos
    Data often accumulates in department-specific systems—silos that make it hard for teams to share information.


  • Data consistency
    Discrepancies in data formats, standards, and naming styles across different sources can lead to inaccurate data and make it harder to create a reliable, unified data set. Data cleansing and transformation are key.


  • Integration complexity
    Unifying data from diverse sources can be technically and logistically complex. For example, companies might need to align complicated data structures and formats, or integrate real-time data streams.
  • Security and compliance
    Data can include customer information and personally identifiable data. To unify sensitive data from diverse sources, companies need tight data security and to comply  with privacy regulations like GDPR or HIPAA.


  • Technological compatibility
    Companies often use a mixed bag of modern and legacy technologies and tools throughout their operations, and they can find it hard to bring these technologies together.
  • Change management
    Unifying data requires working across different teams, and aligning on governance rules.


  • Structural adaptability and flexibility
    As an organization grows, their data architecture must be able to handle greater amounts of data—and different data sources—without running out of storage, hurting processing speed, or causing performance problems.

Best Practices For Data Unification

Several strategies and tactics can simplify data unification efforts:

Use comprehensive data governance 

Setting quality standards, clearly defining which teams are responsible for what outcomes, and enforcing data security can set the stage for trusted, interoperable data.

Invest in technology and tools

Low-code/no-code technologies make it easier to streamline integration workflows and unify data without needing specialized skills.

Continuously monitor and maintain

Regular maintenance is key to catching data-quality, compliance, and integration issues before they become bigger. Automated monitoring tools significantly help.

Collaborate and measure impact

Foster cross-functional teams that include data engineers, analysts, and domain experts to make sure data unification efforts are aligned with business objectives and track KPIs that matter to the business.

Educate and train

Solid communication, change-management strategies, and training programs can help employees understand and adapt to new processes.

Use scalable data architecture

Design the data architecture for scalability. Using cloud-native storage and processing means a data infrastructure can grow with its company.

What Makes AI-Powered Data Unification Better?

Most large or enterprise-scale businesses deal with massive amounts of data. Any human-driven data unification efforts at these scales would be similarly massive. Take an organization with, say, 20 separate data sources—supply procurement, for example. Traditional ETL (extract, transform, and load) and MDM (master data management) efforts might take months just to unify the data.

Instead of wasting tens or hundreds of human worker-hours gathering and organizing data, Reltio’s AI-powered data unification and management capabilities deliver trusted cleansed data in milliseconds, for data analytics and operational systems alike. Reltio Connected Data Platform’s prebuilt data models, preconfigured integrations, and prescriptive implementation approach allow unified data to flow to downstream systems in 90 days.

How Can Reltio Help With Data Unification?

Reltio’s rich data unification and management capabilities, from entity resolution to multi-domain master data management to data products that give 360-degrees views of business entities, go far beyond the features of traditional master data management features.

Today, Reltio Connected Data Platform, offers expanded capabilities for extensive attributes from a large number of sources, dynamic survivorship, and the capability to create a 360 view of any entity with relationships, transactions, and interactions while offering built-in data quality, integration, and reference data management.

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