Unified Data Platforms: The Complete Guide to Modern Data Management

If you believe your siloed systems may be preventing you from putting AI to work, causing customer dissatisfaction, and driving down productivity, it may be time to consider unifying your fragmented data.

This guide will explain unified data platforms, discuss their benefits and challenges, and give you a step-by-step guide to help you on your unification path.

A unified data platform, or unified platform, receives, stores, cleans, and manages data from systems such as e-commerce platforms, ERPs, web management systems, CRMs, content management systems (CMS), mobile apps, data warehouses, and data lakes. A unified data platform can solve the trapped data problem: siloed data, accessible only to certain departments. A unified data platform can ingest internal and external system and app data. It is designed to be used by employees in various teams, giving everyone access to the same, single source of truth, improving operational efficiency and increasing productivity.

In the context of customer data, a customer data platform (CDP) can unify sales history, web browsing data, and customer service interactions by gathering data from your CRM, marketing systems, ad management platforms, marketing systems, and data warehouses. The end goal of the unification in this case is to give you a full view of your customers’ profile– in other words, letting you see all the information you have collected about them. Just as importantly, a unified data platform can keep this profile alive by updating it in real or near-real time.

Under the hood, a unified data platform architecture is usually formed by these three layers.

1. Data collection (or ingestion). You can bring data into your unified platform through various methods: batch ingestion, streaming real-time or near real-time data, and bidirectional federation . Batch ingestion technologies, such as ETL, move data in batches, creating a copy in the new location. Streaming technologies, such as zero copy, don’t physically move or copy data. Instead, they create “virtual tables” that describe the blueprint of the data to both systems, making it accessible to both systems at the same time.
2. Data integration. This step usually involves normalizing and harmonizing data, making it ready for analytics, AI, and agentic AI. Unified data platforms can integrate unstructured, structured, and semi-structured data–each with its own internal format and set of requirements—by creating a common, standard format that analytics, AI applications, and AI agents can work with. This, along with cleaning data, are the main goals of data harmonization. Normalization involves standardizing, such as adding dashes or expanding abbreviations where needed, e.g., when 8009559090 becomes 800-955-9090.
3. Analytics and AI. At the top level of a unified data architecture you usually find data analysis and AI. Clean, harmonized data can be analyzed to predict, for example, next season’s sales or the likelihood of a virus outbreak becoming a pandemic. AI agents acting on unified data can act as customer service consultants, talking to customers and processing orders; or generate your next marketing campaign.

The movement or copying of data usually introduces the potential for errors and complicates data governance, security, and privacy management. Because of this, unified data platforms typically come with tools to manage security, privacy, and governance.

For many CIOs and executives, siloed, disparate data is the biggest obstacle to AI preparedness, operational efficiencies, and intelligent decision-making. Let’s take a look at the advantages of unified data platforms.

CIOs and CDOs are under pressure to deliver on AI initiatives that will relieve their overburdened IT departments, differentiate their companies with innovative AI offerings, and secure revenue growth. The fear of missing out is in fact driving AI adoption.

As everyone knows by now, AI and agentic AI are only as good as the underlying data. And the reality is that the majority of companies today face data issues; 98% of IT departments report at least some degree of challenge with their digital transformation efforts, and 80% cite data silos as a concern.

A unified data platform forms the backbone for analytics and AI, feeding AI algorithms and agents with accurate, harmonized, normalized data that create the necessary context for successful AI deployment. Not just AI that can analyze, but agentic AI, which takes action based on how AI agents perceive and interpret their environment.

A healthcare provider, for example, can use AI agents trained on unified patient data to automate the verification of patient benefits, freeing the front desk staff to move patients quicker through the queue. ezCater is an example of a company that deploys AI agents trained on their unified platform. The company has integrated CRM, data warehouse, and transactional data into a unified platform, and uses AI agents to manage orders based on the customers’ specific dietary requirements.

We’ve all been there: searching for a document that’s nowhere to be found, feeling the frustration increase as time goes on. Employees spend enormous amounts of time looking for data–by some estimates, as much as 30% —searching and preparing data, and up to 20% duplicating work.

Data unification can reduce time spent searching for information in every department that uses the data. In the context of a Customer Data Platform (CDP), Marketing can find campaign results or launch automated marketing campaigns, Sales can analyze recent performance, and customer service agents have access to the entire customer history–all in one place, updated in real or near-real time.

Siloed data is often duplicated, erroneous, or old. Data unified in the right platform, as we discussed above, is de-duplicated, normalized, and clean. And many unifying platforms come with the necessary tools for data privacy, security, and compliance.

Data privacy has to do with respecting your customers’ preferences, asking for consent before sharing their data, and honoring their request to delete or “forget” data. It’s a foundational step to gaining their trust.

Data security involves protecting the data you collect from unauthorized access. According to Anne Neuberger , US Deputy National Security Advisor for cyber and emerging technologies, the annual average cost of cybercrime will cross $23 trillion in 2027. Beyond financial losses, a breach can bring your organization loss of customer trust and reputational damage.

Data governance has to do with managing who has access to what data and for what purpose. A good governance framework is even more important in the age of AI.

Governance, security, and privacy aren’t “once and done.” They require constant monitoring and updating of your policies so your data can stay current, compliant, and secure.

A recent report shows that 85% of IT leaders expect AI to increase IT developer productivity—a big relief as they simultaneously report a 39% increase in IT requests in just the last year.

Data silos and legacy systems , owned by different teams, create a fragmented data model that is more expensive to maintain and operate than a unified platform. You incur not just the ongoing technical debt and cost of the extra storage, but also the hours and overhead spent searching for or de-duplicating data. Last but not least, you’ll have to consider the opportunity cost of losing out on AI initiatives.

As your organization grows, so does the amount of your data. Besides the cost of keeping this data in siloed systems that will need constant upgrades, think about scalability and plan for future needs. With a unified platform, especially in the cloud, you can get seamless scalability of infrastructure, applications, and resources as your business grows or changes.

Adopting a unified data architecture and platform has many advantages but isn’t entirely risk-free. We’ll take a look at the most common challenges in the sections ahead.

By some estimates, up to 80% of data organizations create or collect is unstructured, presenting significant challenges to making it usable for analysis and AI. On top of that, many companies are bogged down with data silos, especially legacy systems with antiquated processes and architectures, often stored on mainframe systems.

Successfully merging and integrating data from these disparate systems requires careful planning, expertise, and the right technologies. Without the right technologies and guidance, the complexity of integration can feel daunting. Many platforms and data lakehouses today make integration easier because they offer:

• Automated data ingestion
• Support for structured, unstructured, and semi-structured data (including images, audio, and video)
• Support for advanced integration tools, such as zero copy
• Scalability as your needs grow
• Robust governance, security, and privacy tools
• Templates specific to industries and use cases

New technologies can be very beneficial for your organization, but ultimately their success hinges on the people using them. And because humans have strong reactions to change, it’s important to think about the human aspect of your new unified platform.

The new platform will require training, and the employees need to get used to new processes and workflows. Top-level management and executives will need to be onboard and sponsor the project. Communication with all the stakeholders, including the reasons for and benefits from the change will help you significantly before, during, and after the transition.

Think carefully about your data needs and create a plan well ahead of time. Below are some crucial steps to consider.

1. Define your business objectives and data needs. Start with the end goals in mind. Define what you expect to achieve and what data you’ll need.

2. Audit and map all existing data sources. An inventory of your existing data sources will help you decide which data repositories will be integrated and subsequently retired.

3. Decide on the future state of your data. Design the to-be state of your data, processes, and architecture in collaboration with your stakeholders. This involves careful planning with your IT teams and external resources (platform vendors or consultants).

4. Select a data platform or in-house integration. If your organization has the necessary development expertise and technologies, you may decide to undertake your data unification in house. Beware the self-inflicted woes this has the potential to bring. A third-party platform may be the better solution if you are looking for faster integration, scaling, and quicker onboarding.

5. Decide on your integration technologies. Whether with a vendor or in-house, create a plan for your data integration that will include which integration tools you will use, whether the integration will be batch or real time, and what tools you will use later for governance, privacy, and security.

6. Offer training and encourage adoption. Start training early and keep offering it until the employees and stakeholders feel confident in the new workflows, processes, and system.

10. Monitor, optimize, and scale as your needs grow. Your data streams will continue to grow, and with it the amount of data you gather, create, and integrate. The right solution unification platform should scale as your business grows.