How Poor Data Integration Drains Productivity & Profits
Data is one of the most valuable assets a company can possess. We all know that (and if you still do not, god help you). Businesses rely on data to make informed decisions, optimise operations, drive customer engagement, etc. Data is everywhere and it’s waiting for us to make good use of it. We just need to get it from where it resides (or being generated) and make it useful for decision-making. Take the data, clean it, validate it, apply business logic and derive insights from it. Sounds straightforward, right?
Well, not quite. Not by a mile even. One of the biggest data challenges businesses face is its integration. Companies spend anywhere from 40% to 80% of their data teams’ time doing data integration. We see it in most organisations we work with. It’s a never-ending hive of activity.
Armies of developers constantly coding, checking and validating reams of data of which they often have no understanding. Layers upon layers of custom code that is rarely supported by good documentation. They are orchestrating stacks of tools and technologies from whatever was popular at the time of purchase, going back decades. Then, when their integrations inevitably fall apart, the teams spend nights in cold sweat firefighting. And everyone is permanently buried in this type of work pretty much all the time.
This is so painfully inefficient it boggles my mind every time I encounter it. I am sure, if you have been working with data in any large organisation, you know what I am describing here. It’s such a waste of time and money in today’s age of advanced tech and AI. Yes, the data people are super busy all the time, but the value to the business in nil. What you get as an output from all that work is wasted productivity, increased costs, and missed profitable opportunities.
The devs don’t always get this (don’t hate me). But the fact is that the value lies in the information the data carriers, not in the process of making data digestible. Let me break it down for you as to why.
The burden of data processing
Many companies still rely on manual data collection, cleansing, and transformation processes. Data engineers and analysts spend hours every week exporting, formatting, and reloading datasets into different systems. I was there myself in the past two decades, and it is still commonplace. A study Forrester Research did found that 70% of data scientists’ time is spent on data preparation. That’s 70% on the tasks that keep them away from actual analysis and insights. Let me reiterate it again: the business value lies in the insights, not in the data prep.
Siloed data across disconnected systems
Companies often have multiple platforms—CRM systems, ERP tools, marketing automation, finance software, and legacy databases—all storing different pieces of crucial data. Without seamless integration, teams spend days searching for, reconciling, and verifying data. Look around your own business and see how many opportunities you are missing out on because of these issues. I bet you have a list of great ideas to improve your area that get shot down due to the lack of data or high effort of getting it.
Duplicate and inconsistent data handling
Inconsistent data formats, duplicate records, and outdated information require constant intervention. IT and data teams spend two days a week resolving data discrepancies. That’s two days a week that your organisation is not using the data to generate value. That is purely added cost to your operations. Imagine 2/5th of production time was wasted idling in a manufacturing line? How long will that business last?
Slow decision-making due to fragmented data
Data integration delays don’t just burden the IT teams—they slow down business decisions. When executives don’t have access to real-time, accurate insights, they rely on outdated reports or gut instincts. A survey by IDC found that organisations lose an estimated 20% of their revenue potential due to slow or inaccurate data-driven decisions. That is hugely costly for any business.
The financial cost of data integration inefficiencies
Wasted time isn’t the only issue. Poor data integration directly impacts a company’s bottom line. A report from Gartner found that poor data quality and integration issues cost businesses an average of $13 million per year. The hidden costs include:
Increased labour and tooling costs
Businesses often need to hire additional IT specialists, data engineers, and analysts to manually clean, reconcile, and integrate fragmented data across multiple systems. Instead of investing in strategic innovation, teams spend time fixing inconsistencies and errors. Then, companies resort to adopting multiple patchwork solutions, including middleware and third-party integration tools, to compensate for their fragmented data landscape. This further inflates costs without solving the core issue.
Lost revenue and business opportunities
When data is scattered across disconnected platforms, decision-makers lack real-time, reliable insights. This leads to delayed responses to market trends, supply chain management issues, and missed revenue opportunities. Businesses using outdated and inaccurate reports make suboptimal pricing, inventory, and investment decisions.
Inability to productionise AI
Everyone is trying to implement more of the “traditional” AI and the latest LLM capabilities in their businesses. The trouble is, very few have been successful in rolling out these technologies in production settings. The biggest blocker? Yup, suboptimal data that produces inaccurate results that then leads to people losing trust in the tech before it is even given a proper chance to succeed.
Compliance risks and regulatory penalties
In industries where data security and compliance are critical—such as finance, healthcare, and manufacturing—poorly integrated data increases the risk of non-compliance with regulations like GDPR, HIPAA, and SOX. Data discrepancies, unauthorised access, and reporting errors will lead to substantial fines, legal disputes, and reputational damage.
The long-term impact
No one ever looks at these issues from a long-term perspective. Beyond the immediate costs, poor data integration creates a ripple effect throughout the company. As teams struggle to consolidate and verify data manually, overall productivity drops, customer experience suffers, and innovation stops. The costs of these are not always immediately visible. So they tend to go unnoticed. Companies start to think that this state of things is just a cost of doing business with data. Trust me, it doesn’t have to be at all.
How companies can reduce data integration waste
Call me a conspiracy theorist, but I have a sneaky feeling that the devs actually love doing this data integration work manually. And I can’t blame them. They are genius craftsmen who enjoy new challenges and finding cool ways to solve them. That’s in the dev’s DNA. Especially, when they are still learning and growing in the data industry.
The trouble is that the rest of the organisation pays for this. It is a financial burden and quite a heavy one too. If you look at it from a senior exec’s perspective, data integration work is immediately poor value for money. It just is. No other department tolerates so much waste of resources to produce a unit of output.
So this is where we come to my usual pitch (I won’t stop until we make the message loud and clear to everyone on this planet).
While data integration challenges are significant, solutions do exist on the market today that save time and resources in a huge way.
IOblend is designed specifically to minimise the time and effort companies spend on data integration. We have generalised and automated all the work that drains your resources and wastes time when integrating your data.
Here’s how IOblend addresses common data integration challenges:
Automation of data integration processes
IOblend automates the flow of data from all sources across any environment, eliminating the need for manual data entry and processing. This automation accelerates data pipeline development, reducing the associated effort by up to 90%.
Real-time data synchronisation
We have developed a dedicated Change Data Capture (CDC) engine that can handle any data source, whether it has a log or doesn’t. We handle all aspects of CDC and ensure that the data you land is reliable and ready for consumption.
IOblend enables true real-time synchronisation between systems of any vintage. This ensures that data remains consistent across platforms, reducing the time spent on reconciling and verifying data. Automatically.
Simplified data transformation and management
IOblend provides an intuitive interface for designing, building, and testing data pipelines. It supports complex transformation logic and data quality rules, allowing for in-memory data processing without the need for intermediate staging layers. This streamlines the integration process and reduces latency. In the latest development, IOblend now integrates AI agents as part of the ETL process, so you can use advanced logic for data extracts, validations and business logic.
Integration with diverse data sources
IOblend seamlessly connects with various data sources and systems, including Snowflake, GCP, AWS, Salesforce, Oracle, Databricks, Microsoft Azure, and SAP. This versatility allows companies to consolidate data from multiple platforms efficiently with a single tool.
Low-Code/No-Code development environment
This is where the true time saving comes to the fore. By abstracting complex coding requirements, IOblend enables rapid development and deployment of data pipelines using just SQL or Python. Every other aspect of the production ETL process has been automated to reduce dev time and cost. This approach reduces the dependency on specialised IT resources and massively accelerates project timelines. Coders don’t like to hear this, but the trend is heading this way, especially with the advent of GenAI.
Conclusion
It always pains me to see companies waste countless hours and money on data integration. They often stubbornly insist that the work they are doing is the best way forward. Until one day, after spending months or even years on something that doesn’t work for them, they give us a call. We have “rescued” a number of projects that went down a popular rabbit hole and found only darkness and pain. Don’t go down the rabbit hole.
We are here to help. If you are either struggling with data integration already or are evaluating a new digital transformation project, reach out to us. You’d be surprised how much better, quicker and cheaper you can do it with us.
IOblend presents a ground-breaking approach to IoT and data integration, revolutionizing the way businesses handle their data. It’s an all-in-one data integration accelerator, boasting real-time, production-grade, managed Apache Spark™ data pipelines that can be set up in mere minutes. This facilitates a massive acceleration in data migration projects, whether from on-prem to cloud or between clouds, thanks to its low code/no code development and automated data management and governance.
IOblend also simplifies the integration of streaming and batch data through Kappa architecture, significantly boosting the efficiency of operational analytics and MLOps. Its system enables the robust and cost-effective delivery of both centralized and federated data architectures, with low latency and massively parallelized data processing, capable of handling over 10 million transactions per second. Additionally, IOblend integrates seamlessly with leading cloud services like Snowflake and Microsoft Azure, underscoring its versatility and broad applicability in various data environments.
At its core, IOblend is an end-to-end enterprise data integration solution built with DataOps capability. It stands out as a versatile ETL product for building and managing data estates with high-grade data flows. The platform powers operational analytics and AI initiatives, drastically reducing the costs and development efforts associated with data projects and data science ventures. It’s engineered to connect to any source, perform in-memory transformations of streaming and batch data, and direct the results to any destination with minimal effort.
IOblend’s use cases are diverse and impactful. It streams live data from factories to automated forecasting models and channels data from IoT sensors to real-time monitoring applications, enabling automated decision-making based on live inputs and historical statistics. Additionally, it handles the movement of production-grade streaming and batch data to and from cloud data warehouses and lakes, powers data exchanges, and feeds applications with data that adheres to complex business rules and governance policies.
The platform comprises two core components: the IOblend Designer and the IOblend Engine. The IOblend Designer is a desktop GUI used for designing, building, and testing data pipeline DAGs, producing metadata that describes the data pipelines. The IOblend Engine, the heart of the system, converts this metadata into Spark streaming jobs executed on any Spark cluster. Available in Developer and Enterprise suites, IOblend supports both local and remote engine operations, catering to a wide range of development and operational needs. It also facilitates collaborative development and pipeline versioning, making it a robust tool for modern data management and analytics
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