Guide to Unified Data: What It Is and Its Importance

Fedex was once faced with a data issue that hindered growth. Hundreds of small business customers browsing Fedex.com for international shipping rates would abandon their shipping rate quotes, similar to how consumers abandon shopping carts. Fedex had to wait three weeks for a manual process to cross-reference recent web activity with shipping info and identify which customers who abandoned the site would be open to a new quote. By then, sales were lost.

The issue facing Fedex was lack of unified data. This type of issue isn’t unique to Fedex. The average company uses over 1,000 applications and 70% remain disconnected, leaving opportunities for growth on the table.

This guide will show you why you should care about unified data, why data unification is a crucial step to better data and powerful AI, and how to overcome the common obstacles surrounding unifying data.

Unified data is data combined from different systems, platforms, or applications into one unified platform or view. For example, you could combine data from your ERP, CRM, internal or external data lakes and data warehouses. Unified data is the opposite of siloed, fragmented data. Once you unify the necessary data, you only need to access one platform or view to find what you need.

It’s not actually necessary to move data to unify it. Data virtualization and zero-copy integration are some technologies that offer all the advantages of unification without actual data movement, as we’ll discuss later on.

Regardless of technology, the goal is to integrate data from various sources into a single, accessible view that will give everyone in your organization access to the data they need, regardless of department or data “ownership.”

Fedex decided to retire many of the siloed systems that prevented Marketing and Sales from reaching out to the business customers who abandoned shopping carts. The company chose to unify web browsing, opportunity, and shipping data. Today, Fedex can pinpoint inactive customers in a matter of hours rather than weeks. If shipping data shows the customer hasn’t shipped yet, FedEx can choose how to engage–whether through an email journey, a sales campaign, or targeted ads on their webpage. Unified data brought Fedex a single, 360-degree view of their customers and opened a path to new business growth.

The significance of data unification is even more heightened in the age of AI and agentic AI, where automation, operational efficiency, and data-driven insights can drive growth and reduce costs for the companies whose data is in good shape. Disconnected, siloed, and bad quality data will slow you down and prevent you from getting the benefits of AI. Rushing an AI model into development is not a good alternative; without a solid data foundation you can face serious consequences with unreliable AI output that can ultimately hurt your brand.

After predictive and generative AI, we have now entered the era of agentic AI, which not only generates content but can converse, act autonomously, and react. AI agents can reason not only based on predictions they make from large datasets, but also on their ability to perceive the environment and take autonomous action.

But as everyone knows, AI output is only as good as the data input. Good data and a unified platform will power your AI agents with the fuel they need to make accurate predictions, automate customer service, adjust marketing campaigns on the fly, or rebalance inventory to adjust to seasonal sales. All this can translate into better customer experiences, cost savings, operational efficiencies, and additional growth.

Today, 80% of customers consider the experience a company provides as important as its products and services. Think about the experience you provide to customers with data spread across several systems: customer service agents, for example, take calls from dissatisfied customers but are blind to the customers’ recent web activity, and can’t make relevant suggestions. Or, much like Fedex, Marketing and Sales are not reaching out to prospective customers in a timely manner, losing deals to your competitors.

Consolidated data from various sources into a 360-degree view gives you depth of knowledge about your customers–not just their transactional history and demographics, but also their social media interactions, web browsing activity, preferences, and past interactions with your company. This unified profile is the foundation for a superior customer experience–so crucial in today’s competitive, AI-powered market.

What is real-time decision making worth to your company? Millions, potentially. Take a logistics provider or trucking company, for example, using real-time feeds from weather data, maps, traffic patterns and even satellites, unifying it with their own route information to avoid costly delays and update delivery times for their customers.

Decisions like these hinge on unified data. Real-time analytics can gather, process, and output data as a continuous flow, giving you the most up-to-date information. This is, of course, where AI and agentic AI excel. A healthcare AI agent, for example, can analyze lab reports from several sources and investigate the spread of disease proactively by classifying data into confirmed, suspected, or probable infectious diseases before they spread further.

Maintaining duplicate data architectures is costly. You will be incurring:

• Duplicate data storage costs
• Extra engineering costs to develop and maintain applications on different data stacks
• Additional costs to secure and govern duplicate data architectures
• Costs related to inefficient processes, re-dos, and wasted productivity
• Opportunity costs of lost business and growth. You may, for example, be unable to tap a new business segment or enter a new market because you aren’t getting visibility into your entire organization.

A single, unified platform can save costs and position your company to grow. Consider the expectations set on customers by Amazon or Apple stores. Meeting your customers where they are, with the ease and convenience they expect, is critical if you want your company to flourish. A unified data view gives you the agility to move quickly and offer the elevated services your customers expect.

Siloed data grew over time as companies continued to add systems and applications in their stacks. Because it’s managed by separate teams, this data is not easily accessible to others.

Unified data democratizes access. Your Marketing department won’t have to ask Analytics for a report on recent ad performance or wait weeks for the results. Sales can see marketing campaigns and how effective they are, and sales reps will have the data at their fingertips to collaborate with everyone who touches your customers, and drive timely decisions.

Unifying data can come with its own set of challenges due largely to data issues, legacy systems, and data volumes.

Organizations today collect troves of structured, semi-structured, and unstructured data, 70% of which remains trapped in siloed systems. This data is usually set up in different data models and may be duplicated. For example, a financial system may be keeping score of revenues by customer, but the CRM may have updated fields for the company’s new CIO or upcoming deals in the making. The customer info and projected revenue data is undependable and needs to be reconciled.

Dan O’Leary, senior director of partnerships at Box, an intelligent content management (ICM) platform provider, sums up the challenge this way:

Data quality issues will hold back your AI and agentic AI efforts. As you unify data, pay attention to the following:

• Data cleansing: Identifying and removing errors, inaccuracies, and data inconsistencies
• Normalization: Standardizing data formats and turning it into searchable data, usable for AI and machine learning models
• Validation: Systematically checking that data conforms to your specified criteria
• Data governance: The rules and policies that define how your data will be used and by whom.

It’s also a good idea to perform regular audits, using the tools that best fit your business and data strategy goals.

This year, our world will generate approximately 147 zettabytes of data. Integrating the massive volumes of data businesses collect today presents technical and operational challenges. Remember, the data will keep coming. So think about adequate storage space, data partitioning (dividing large datasets into partitions for easier management), and the right data processing and AI tools.

You can choose to unify data in house, or through a cloud data platform that will unify data from various data lakes, warehouses, and databases. The right platform will cleanse, normalize, and contextualize your data for AI action. Cloud providers can accommodate massive amounts of data, so you won’t have to worry about running out of storage. And some come with built-in data governance and security frameworks.

Legacy systems often come with outdated processes that can hold back your digitization efforts and hamper innovation. By legacy system we mean any outdated software application, hardware, or programming language. Unifying legacy ERPs, databases, and other systems of record is an important step to modernizing your data architecture. But because these systems are usually incompatible, data unification can be a challenge.

Once you decide to unify your data and prepare it for AI, you have several technologies to consider. And you will likely use more than one.

• Data virtualization, which creates a single data-access layer, lets your users manage data as if it was unified without actually moving or duplicating the data.
• Application programming interfaces (APIs), which are specifications that allow software systems to interact and exchange data. APIs can be industry-specific; for example, a commerce API .
• Extract-transform-load (ETL) process, which lets you transfer data batches between systems.
• Zero-copy integration, which, unlike ETL, doesn’t move or transfer data. Zero copy creates “virtual tables” behind the scenes that describe one system’s data to another.

Think about a scenario where you have just finished mapping out your new integrated architecture and a new, superior integration technology enters the market. You’d be scrambling to re-define your framework. This is why flexibility is important. Consider additional data sources or formats you may have to integrate as your business evolves, and strike a balance between a unified structure and allowing for new data sources and data formats in the future.

Unifying data can have significant benefits for your organization regardless of industry. Here’s a brief roundup of industry examples.

• Healthcare: A healthcare AI agent can analyze reports from multiple sources to intelligently classify lab reports into confirmed, suspected, or probable infectious diseases so that the spread can be prevented.
• Finance: A financial service provider can segment their unified customer data in real-time and use agentic AI for automated, timely campaigns.
• Retail: A retailer can capture and update customer data from web, mobile, and in-store interactions in real time and personalize offers to the customers.

What does the future hold for data unification? Here are two key trends to watch for.

1. AI-driven data unification: AI assistants and AI-enhanced workflows incorporated into data integration tools promise to streamline unification and move data management to self-service.
2. Data mesh: This is an architecture that decentralizes data ownership by placing it on the areas of the business closest to the data. Regardless of who “owns” the data within the organization, a mesh ensures data is reusable, trusted, and secure.

Data unification isn’t only about tools or technologies. Moving past data silos and isolated organizational initiatives brings change in the ways we collaborate and set goals. Think about change management alongside the tools and platforms so that your unification efforts align with measurable business goals.