Best data consolidation practices to take your data from chaos to clarity

Marketing data is everywhere — Google Ads, Facebook, CRM systems and even offline sources. But without a unified system, you’re left with conflicting numbers, wasted time and decision-making based more on gut feeling than strategy.

That’s where data consolidation comes in to save the day. 

What is data consolidation?

Data consolidation is about centralizing your scattered data into one system for easier management and analysis. 

Combining sales data from Shopify with customer behavior data from Google Analytics and ad data into one central place gives your teams quick access to important insights. This also saves time by reducing issues with inconsistent data.

The differences between data consolidation and data integration

Data consolidation and data integration may sound similar, but they serve different purposes in data management.

Data integration acts like a translator — it allows different systems to “talk” to each other and share information while still maintaining their separate records.

Data consolidation is like merging all those records into a single, standardized dataset, ensuring consistency and accuracy for analysis.

Here’s how this works in practice.

Imagine you’re managing marketing campaigns across multiple platforms:

Data integration: Google Ads and Facebook Ads sync audience lists, but their performance data remains stored separately within their respective platforms.

Data consolidation: Google Ads, Facebook Ads and LinkedIn Ads data are extracted, cleaned and centralized into a single data warehouse, ensuring standardized reporting across all platforms.

Unlike simple ETL (Extract, Transform, Load) processes, data consolidation goes further — it involves data cleansing, transformation and governance to ensure that marketing and business teams are working with reliable, standardized insights.

By continuously centralizing and automating data, businesses eliminate inconsistencies and gain a single source of truth for faster, more informed decision-making.

Some platforms (like Funnel) use “data integration” as a broad term that includes extraction, transformation and consolidation. In this article, we’re focusing on the consolidation layer — turning scattered, inconsistent data into a clean, unified source of truth that’s ready for decision-making.

Why you’re struggling to consolidate marketing data (and how to fix it)

When it comes to marketing, data is everywhere, but it's rarely in a unified format that’s easy to work with. From tracking tools to ad platforms, each source has its own structure and language. That’s where the real struggle begins. 

Too many data sources

Facebook Ads, Google Analytics, HubSpot, offline sales data — marketing pulls insights from a massive landscape. There are over 14,000 tools available to marketing professionals today, and it’s not abnormal for a company to use close to 100 marketing-related apps. However, only one in five marketers works with fully integrated data. Meanwhile, 30% are stuck with disconnected tools that don’t sync properly. This fragmentation leads to inconsistent insights, making it harder to make quick, informed decisions.

And the problem isn’t just in marketing platforms. Nearly a third of teams struggle to access data from other departments, and many find it hard to share their own. This isn’t just an inconvenience. It leads to broken reporting, incomplete insights and bad decisions based on outdated or unreliable data.

Without a solid data consolidation strategy, marketing teams are stuck working in silos while data teams spend their time fixing problems instead of delivering actionable insights. Reports are stitched together manually, which can lead to duplicate efforts, conflicting numbers and wasted time. The more data sources, the harder it is to maintain consistency across platforms.

How to fix it: Prioritize key pipelines. Instead of trying to integrate everything at once, start with high-impact sources — ad platforms, CRM and web analytics. Automate ingestion to reduce manual exports and eliminate redundant data pulls.

Inconsistent data formats create confusion

Different platforms use different terms to describe conversions. If these aren’t standardized when you’re implementing a data consolidation process, you might see inaccurate results. 

It only takes a single metric to be calculated differently across platforms to make cross-channel reporting a nightmare.

Let’s look at an example of cross-channel performance reporting challenges.

Without standardization, the values and naming conventions vary between platforms.

This inconsistency leads to:

• Mismatched performance metrics, making it difficult to compare campaigns.
• Incorrect budget tracking due to currency discrepancies.
• Errors in reporting, forcing marketers to manually clean and reformat data.

By applying schema mapping and transformation rules, data is normalized and analysis-ready, as you can see in this chart:

How to fix it: Use schema mapping and transformation tools to standardize key fields. Align campaign names, normalize currencies and unify attribution models before data hits the dashboard. This automates your data transformation so all platforms speak the same language.

Bad data quality means bad decisions

Duplicates, missing values and outdated records turn your consolidated reports into a guessing game. Marketing teams end up wasting time questioning numbers instead of acting on real insights. 

If the VP of Marketing is asking, “Which report is right?” every week, it’s not the dashboard that’s the issue — it’s the data. Clean, accurate data is the foundation for trustworthy reports. Without it, you’re just spinning your wheels.

How to fix it: A centralized data hub like Funnel simplifies the data integration process from any type of marketing platform and automates common data management tasks like normalizing fields, optimizing ingestion and avoiding duplicate data.

Slow, manual ETL processes kill momentum

Marketing campaigns move fast, so you need processes that can keep up with the pace. Using an automated ETL tool speeds up your time to insight. It also reduces the risk of errors and simplifies workflows. 

Instead of using manual ETL processes, automate with a data consolidation platform that continuously pulls, cleans and centralizes data. 

Best practices for data consolidation 

Consolidation isn’t just about dumping everything into a warehouse. It’s about structuring data so it’s complete, consistent and ready for analysis. Here’s how to make it happen: 

1. Data discovery

Marketing teams pull data from everywhere — Google Ads, Facebook, LinkedIn, HubSpot, Shopify, spreadsheets and custom databases. This constant data flow leads to the risk of inconsistency in your metrics.

If you don’t inventory everything, gaps and redundancies can creep in, causing missing data, duplicate records and mismatched metrics.

• Audit every source: Get a handle on all the platforms feeding your data, from paid media to CRMs, support tools and offline conversions.
• Spot inconsistencies early: Ad platforms track conversions differently. Google Analytics has different attribution windows than Meta. HubSpot’s lead data may not match Salesforce’s. Finding these conflicts upfront helps avoid reporting chaos later.
• Build a clear map: Know what each source tracks and how it connects. Flag discrepancies in naming, formats and attribution models before integration kicks off.

2. Data transformation

Different platforms use different time zones, currencies and attribution methods to measure metrics, making cross-channel analysis complicated. Without transformation, reporting stays unreliable and performance comparisons are meaningless.

• Integrate and transform your data automatically: Use a centralized tool that pulls raw data and applies business rules before it reaches the analysis phase.
• Standardize naming and metrics: Align campaign names, unify currency formats and normalize time zones to create a single version of truth.
• Fix broken data before it hits dashboards: Automation prevents manual errors, speeds up reporting and keeps marketing teams from working with bad data.
• Automate cleansing and transformation: Make sure every platform speaks the same language before data is stored.

3. Data consolidation

If marketing data lives in disconnected spreadsheets and siloed apps, it’s impossible to get a timely view of performance. A well-structured pipeline consolidates everything into one place, making reporting fast, accurate and scalable.

• Choose the right storage: Data warehouses like Snowflake, BigQuery and Redshift work best for structured analysis. Lakehouses like Databricks handle both structured and unstructured data. A centralized data hub as part of a wider marketing intelligence solution can close the gap between data storage and direct insights, speeding up decision-making with data you can trust.

• Decide between batch versus real-time loads: If campaign optimization depends on up-to-the-minute data, real-time ingestion is key. If marketing reports run on daily snapshots, batch processing may be enough.

• Eliminate mismatched fields and missing data: Automated ingestion prevents discrepancies that lead to broken dashboards and inaccurate insights.

• Centralize your data: Build a consolidated source of truth where marketing teams can access reliable, up-to-date insights on dashboards that provide a holistic overview of the metrics that matter without relying on manual exports.

4. Data governance

Campaigns launch, tracking shifts, new platforms pop up. Without solid governance, these and other inevitable changes can open the door to outdated records, duplicate leads and a lack of control. This leads to unreliable insights and costly mistakes.

Why data governance is critical:

Bad data means bad decisions. Misleading reports, wasted budgets and customer experience breakdowns can all be traced back to poor data. And with privacy laws like GDPR and CCPA, mishandling customer data could result in fines and damaged trust.

• Set rules for freshness: Keep your data updated to avoid skewed insights.
• Validate before problems spread: Automate checks to catch missing values and duplicates early.
• Control access: Limit who can modify core datasets to avoid accidental changes.
• Stay compliant: Regular audits keep you aligned with privacy regulations.

Good data governance keeps your data clean, accurate and compliant, giving you insights you can trust, and instilling trust in customers.

Common mistakes data teams make when consolidating marketing data

Consolidating marketing data is complex, and it’s easy to fall into some common traps. From failing to treat it as an ongoing process to overlooking crucial details, these mistakes can lead to big problems.

1. Treating consolidation as a one-and-done project

Many teams invest time to set up data consolidation for a specific project, like gathering data for one measurement model or a single report. The mistake here is treating it as a one-time effort. 

Data consolidation should be an ongoing, automated process that continuously centralizes data, creating a single source that evolves with your business needs. Without this mindset, you risk relying on outdated or fragmented data that hampers decision-making over time.

2. Focusing on data-driven rather than data-informed

As a data analyst, you spend your day digging into the data to derive insights. But for marketing teams, a little more flexibility is required for decision-making. So data-driven vs. data-informed? What’s the difference?

• Data-driven marketing relies strictly on quantitative data, focusing on metrics without considering external factors. This approach works well for automation and efficiency but can overlook context.

• Data-informed marketing combines data with human intuition, experience and business strategy. It allows for flexible decision-making that accounts for market conditions, creative insights and industry trends.

As a data team handling marketing data, the way you consolidate and structure data shapes how marketing teams interpret and act on insights:

• Context matters – A purely data-driven approach may overlook differences in attribution models, campaign timing and channel performance. Your data pipelines should account for these nuances.
• Standardization versus flexibility – While standardizing data is essential, marketing teams often need flexibility for experimentation. Structuring data in a way that supports both is key.
• Actionable insights – Delivering raw metrics isn’t enough. Your role is to consolidate data in a way that supports deeper analysis, not just reporting.
• Collaboration – Marketing teams depend on you to build data pipelines that serve both structured reporting and exploratory analysis.

3. Not building a learning culture around data

Data analysts have a responsibility to empower the marketing team to dig deeper into data for better insights. One way you can do this is to implement learning agendas to empower the marketing team to focus on insight discovery, like asking better questions and striving for better answers, rather than just asking for the latest conversion numbers before their daily stand-up.

The onus is on you to make sure the data that informs decisions is accurate and organized and to help the marketing team make the best use of it.

Automated data consolidation with marketing intelligence built-in 

Manual data wrangling really slows things down. To keep up, you need an easy, automated process for pulling together your data that gets rid of the silos, standardizes everything and gives you timely insights without the hassle.

By simplifying your data pipelines, you’ll get faster and more accurate reports while freeing up your team from tedious manual fixes. Start small, automate what you can and keep fine-tuning your approach. The important thing is to find a solution that follows the best practices for data consolidation, so your data stays clean, reliable and ready to use.

A fully automated consolidation system does more than just collect your data; it turns those raw numbers into valuable marketing insights. With the right setup, you’ll spend less time fixing problems and more time making a real impact on your business.

You can start turning your marketing data processes around right now. Here’s what you need to do: 

• Check your data sources for any inconsistencies.
• Focus on consolidating high-impact areas like ad platforms, CRM and web analytics.
• Use an automated data consolidation tool to get real-time, standardized insights.

Say goodbye to manual work and quick fixes. Instead, enjoy clear, accurate data that helps you make smarter decisions.

Start consolidating your data today and change marketing chaos into clarity.