• Blog
  • Podcast
  • Contact
  • Sign in
CloverDX Logo
Product
  • OVERVIEW
  • Discover CloverDX Data Integration Platform###Automate data pipelines, empower business users.
  • Deploy in Cloud
  • Deploy on Premise
  • Deploy on Docker
  • Plans & Pricing
  • Release Notes
  • Documentation
  • Customer Portal
  • More Resources
  • CAPABILITIES
  • Sources and Targets###Cloud and On-premise storage, Files, APIs, messages, legacy sources…
  • AI-enabled Transformations###Full code or no code, debugging, mapping
  • Automation & Orchestration###Full workflow management and robust operations
  • MDM & Data Stewardship###Reference data management
  • Manual Intervention###Manually review, edit and approve data
  • ROLES
  • Data Engineers###Automated Data Pipelines
  • Business Experts###Self-service & Collaboration
  • Data Stewards###MDM & Data Quality
clip-mini-card

 

Ask us anything!

We're here to walk you through how CloverDX can help you solve your data challenges.

 

Request a demo
Solutions
  • Solutions
  • On-Premise & Hybrid ETL###Flexible deployment & full control
  • Data Onboarding###Accelerate setup time for new data
  • Application Integration###Integrate operational data & systems
  • Replace Legacy Tooling###Modernize slow, unreliable or ad-hoc data processes
  • Self-Service Data Prep###Empower business users to do more
  • MDM & Data Stewardship###Give domain experts more power over data quality
  • Data Migration###Flexible, repeatable migrations - cloud, on-prem or hybrid
  • By Industry
  • SaaS
  • Healthcare & Insurance
  • FinTech
  • Government
  • Consultancy
zywave-3

How Zywave freed up engineer time by a third with automated data onboarding

Read case study
Services
  • Services
  • Onboarding & Training
  • Professional Services
  • Customer Support

More efficient, streamlined data feeds

Discover how Gain Theory automated their data ingestion and improved collaboration, productivity and time-to-delivery thanks to CloverDX.

 

Read case study
Customers
  • By Use Case
  • Analytics and BI
  • Data Ingest
  • Data Integration
  • Data Migration
  • Data Quality
  • Data Warehousing
  • Digital Transformation
  • By Industry
  • App & Platform Providers
  • Banking
  • Capital Markets
  • Consultancy & Advisory
  • E-Commerce
  • FinTech
  • Government
  • Healthcare
  • Logistics
  • Manufacturing
  • Retail
Migrating data to Workday - case study
Case study

Effectively Migrating Legacy Data Into Workday

Read customer story
Company
  • About CloverDX
  • Our Story & Leadership
  • Contact Us
  • Partners
  • CloverDX Partners
  • Become a Partner
Pricing
Demo
Trial

The 6 biggest data integration challenges (and how to solve them)

Data Integration
Posted July 01, 2019
5 min read
The 6 biggest data integration challenges (and how to solve them)

Data integration offers a breadth of valuable information that lets your business implement new innovative services. But data integration doesn’t come without data integration challenges to overcome.

Without the right mindset, technology or strategy, the way you handle your data could be hindering your BI, analytics and innovation goals. The result? A stagnant organization that falls behind its competitors and fails to meet client demand.

But what can your organization do to avoid this fate? Well, you need to avoid the 6 biggest data integration challenges.

Data integration challenges:

  1. Your data isn't where it needs to be.
  2. Your data is there, but it’s late

  3. Your data isn’t formatted correctly

  4. You have poor quality data

  5. There are duplicates throughout your pipeline

  6. There is no clear common understanding of your data

Before we get stuck into the core issues, let’s first define exactly what we mean by a ‘data integration challenge’.

 

Download the article as a pdf

Share it with colleagues. Print it as a booklet. Read it on the plane.

 

What is a data integration challenge?

Here is a definition of data integration challenge: a data integration challenge is something stopping you from achieving control over the processes and output of your data integration. It's the boulder in your way from getting a single, unified view of your data.

And what's data integration? Data integration involves retrieving data from disparate sources and merging it together to create a single, unified view. This unification makes it easier to draw insights from your data that, when acted upon, can deliver faster, more meaningful business growth.

Of course, data integration challenges can come under many labels - data integration problems, data integration issues - but they always point to the same thing. 

Getting on top of data integration challenges is important when data processing at scale and when working to mature your data strategy.

6 biggest data integration challenges you can’t ignore

Now that we’ve given you a broad overview of what a data integration challenge is, let’s look more specifically at few commonplace examples.

Here are six data integration challenges your business may face and some ideas on how to solve them.

cloverdx data integration challenges

1. Your data isn’t where you need it to be

You want your data in one centralized place, but you struggle with the execution. Sound familiar?

This data integration challenge is commonly a result of depending on human power alone. Relying on developers to curate data from disparate sources and combine it takes time. And this is time that your organization should be spent on analyzing data insights and driving valuable business practices.

So, to cut out the middleman and speed up your innovation goals, it’s better to enlist the help of a smart data integration platform. This will do most of the heavy lifting for you. It's a great way to say goodbye to data your data integration issues.

New call-to-action

2. Your data is there, but it’s late

Some processes require real-time or near real time data collection. For instance, if you’re a retailer running an e-commerce site, you may choose to display tailored, targeted ads to each individual customer based on their search history. This is another painful data integration problem.

But, if your data isn’t collected in the timespan you expect, you can’t meet these demands. Unfortunately, relying upon your team to manually collect data in real time is impossible at best. The likelihood is, you don’t have the resources or employee power to undertake such a heinous task.

If you would like to push for real-time data ingestion and, consequently, innovative and reactive services, your only way forward is with an automated data integration tool. This technology will reliably curate real-time (or near real-time) data without you having to sacrifice your resources.

3. Your data isn’t formatted correctly

Anomalous data that’s incoherent or in the wrong format isn’t actionable – its value lost.  But manually formatting, validating, and correcting data is mundane and takes up a lot of your developers’ precious time.

Data transformation tools eliminate this problem by analysing the original base language, determining the correctly formatted language, and automatically making the change. This process takes the stress out of data integration and limits the number of errors, especially when your data team can flag and inspect code at any point in the transformation pipeline. 

4. You have poor quality data

Poor quality data leads to lost revenue, missed insights and reputational damage. That’s why data quality management is essential part of driving innovation, staying compliant, and making more accurate business decisions.

And it’s not as hard as you might think.

By proactively validating your data as soon as it’s ingested, you lower the amount of bad data entering your systems. On top of this, you can also monitor your data pipelines for outliers and automatically spot errors before they become larger issues.

5. There are duplicates throughout your pipeline

It’s thought that more than 92% of businesses are aware of duplicate data in their systems. And, while duplicates may appear harmless at first, they can cause serious problems long term. The more duplicates you have (and the longer you leave them), the greater the risk to your business.

Most of the time, these duplicates are the result of a ‘silo mentality’ problem. If your teams don’t share data and communicate with one another effectively, duplicates and unexplainable variations become the norm in your data integration pipeline. To help combat duplicates and eradicate data silos:

  • Create a culture of data sharing and take time to educate colleagues
  • Standardize your validated data and ensure everyone understands it
  • Invest in technology that brings teams together
  • Keep regulatory reports that promote transparency and track data lineage

With some oversight and alignment, duplicates will occur less and less.

What is ETL?

6. There is no clear common understanding of your data

We’ve already discussed the importance of communication between technical and business teams in regards to data sharing. But establishing a common vocabulary of data definitions and permissions is equally as important.

You can achieve this common understanding through:

  • Data governance. This focuses on the policies and procedures surrounding your data strategy.
  • Data stewardship. A data steward is an individual who oversees and coordinates your strategy, implements policies, and aligns your IT department with your business strategists.

Without a roadmap and a clear ownership of your data, the integration processes will continue to struggle against misinformation and misalignment. 

Van Mossel case study blog banner - 5

Conquering the data integration challenge

The amount of data we create is growing faster than ever before and is infinitely more critical to organizational success. But, unless you avoid these six core data integration challenges, you won’t get the most value from your applications, functions, and processes.

Get things right, however, and you can accelerate your business transformation, using data as the cornerstone of your growth and development.

So take a step back, review your business goals, and identify which of these challenges is preventing you from making them a reality. With the right culture, mindset, and automated tools, your organization can conquer even the most complex data integration challenge.  

New call-to-action

 

Share

Facebook icon Twitter icon LinkedIn icon Email icon
Behind the Data  Learn how data leaders solve complex problems every day

Newsletter

Subscribe

Join 54,000+ data-minded IT professionals. Get regular updates from the CloverDX blog. No spam. Unsubscribe anytime.

Related articles

Back to all articles
Data Integration
4 min read

Bringing a human perspective to data integration, mapping and AI

Continue reading
How AI is shaping the future of data integration
Data Integration
4 min read

How AI is shaping the future of data integration

Continue reading
Data visualization with a dashboard on a computer screen
Data Integration
4 min read

Data literacy, visualization and the challenges of data integration

Continue reading
CloverDX logo
Book a demo
Get the free trial
  • Company
  • Our Story
  • Contact
  • Partners
  • Our Partners
  • Become a Partner
  • Product
  • Platform Overview
  • Plans & Pricing
  • Customers
  • By Use Case
  • By Industry
  • Deployment
  • AWS
  • Azure
  • Google Cloud
  • Services
  • Onboarding & Training
  • Professional Services
  • Customer Support
  • Resources
  • Customer Portal
  • Documentation
  • Downloads & Licenses
  • Webinars
  • Academy & Training
  • Release Notes
  • CloverDX Forum
  • CloverDX Blog
  • Behind the Data Podcast
  • Tech Blog
  • CloverDX Marketplace
  • Other resources
Blog
The vital importance of data governance in the age of AI
Data Governance
Bringing a human perspective to data integration, mapping and AI
Data Integration
How AI is shaping the future of data integration
Data Integration
How to say ‘yes’ to all types of data and embark on a data-driven transformation journey
Data Ingest
© 2025 CloverDX. All rights reserved.
  • info@cloverdx.com
  • sales@cloverdx.com
  • ●
  • Legal
  • Privacy Policy
  • Cookie Policy
  • EULA
  • Support Policy