Tangled in the Data Web
Data is now one of the most valuable assets for companies across all industries, right up there with their biggest asset – people. Whether you’re in retail, healthcare, or financial services, the ability to analyse data effectively gives a competitive edge. You’d think making the most of data would have a direct impact on the bottom line (cost and revenue).
Data comes from all sorts of places. Companies today collect bucketloads from internal systems (e.g., CRM/ERP, operational, analytical) and external sources, including social media platforms, sensors, third-party APIs, and market intelligence platforms. This mix of internal and external data has massive potential for driving profitability (or efficiency in non-profits), especially when it comes to AI-powered applications and advanced analytics.
However, as appetising as using diverse data sources sounds, integrating it often turns into a technical and operational nightmare. Data integration issues can significantly slow down analytics, lead to costly mistakes, and disrupt AI adoption.
Let’s see why this is the case.
Disparate data formats and structures
One of the biggest challenges companies face when integrating data is dealing with a wide variety of formats and structures. Internal systems might use structured data, such as SQL databases or Excel sheets, while external sources often deliver unstructured or semi-structured data (e.g., JSON, XML, or social media feeds in plain text).
Take the private equity industry, for example. Firms need to merge structured data from portfolio companies (e.g., revenue figures, cash flows, balance sheets) with unstructured data from industry reports, market sentiment analysis, or news articles. The financial data is typically organised in databases or Excel files, while the external data may come in freeform reports or irregular formats like PDFs. Standardising the data for comparison and analysis becomes a tough challenge.
Converting and normalising these formats is necessary to get a full picture of a portfolio company’s performance and the external factors influencing its value. But this task is time-consuming and prone to errors. Inconsistencies between data types must be reconciled before meaningful analysis can take place.
Data silos and legacy systems
This one’s a favourite. Many companies still operate with legacy systems that are outdated, inflexible, and incompatible with modern data platforms. But they work—and often work reliably for operations. Over time, these systems turn into silos where data remains isolated. Instead of being accessible for wider business use, this data gets forgotten or has to be unlocked manually after days (or weeks) of nagging the SME to give it to you.
A manufacturing company we recently helped had separate systems for inventory management, customer orders, and employee records. They bought an ERP but struggled to integrate the ops systems’ data into it in an automated way—plenty of quality issues and manual interventions. Decommissioning legacy systems wasn’t an option due to the “ain’t broke, don’t fix it” principle.
Modernising or replacing legacy systems is expensive, which is why companies often try to bridge the gaps with complex middleware solutions. But this causes more integration complications, increases costs, and delays projects. You really have to think through the architecture, processes, and tools to get this right.
Data quality and consistency issues
Data integration isn’t just about moving data from one place to another. Not in my book, anyhow. It also involves ensuring the quality, provenance, and fit-for-purpose of that data. Merging data from different systems and sources introduces inconsistencies, duplications, or outright inaccuracies that must be resolved before analysis or AI models can be applied.
Here’s an example from another use case. A government organisation collects customer data from multiple touchpoints—online registrations, call centres, contracts, etc. These systems were connected only via manual extracts. If one system records a customer’s name as “John Smith” and another as “J. Smith,” merging the two without proper data cleansing caused confusion. Lots of manual post-processing, until we put automated validation in place and synced their systems in real-time.
Data cleaning with traditional methods is a resource-intensive task. Data scientists spend around 60-80% of their time preparing data, leaving less time for actual analysis. This (mostly manual) process slows down analytics and AI projects considerably, driving up costs.
Security and compliance concerns
Another significant hurdle is complying with strict data privacy laws and regulations. Companies handling sensitive information, like healthcare data, must comply with frameworks like GDPR or HIPAA. When integrating data from internal systems and external sources, companies must ensure they don’t violate any privacy laws or expose sensitive data to unauthorised entities.
For example, integrating patient health records with external data sources for a healthcare AI project is no small feat. Personal data must be anonymised, access restricted, and stringent audit trails maintained. If not done properly, post-processing for compliance adds costs and delays—all while patients wait for treatment.
Beyond compliance, data integration introduces new security risks. Transferring data across systems, especially cloud-based ones, exposes it to potential breaches or unauthorised access. This calls for extra layers of encryption and security protocols, which can also be costly to implement. Plenty of companies (and even entire nations) are still wary about moving to the cloud.
Cost spiral
And then, of course, there’s cost. Integrating data from various systems and external sources can quickly spiral out of control. We see this a lot. Several factors contribute, including the need to acquire new tools, invest in modern infrastructure, and hire skilled professionals to manage data integration.
Many businesses underestimate the effort required to integrate their data successfully. “The source comes with an API, so we just hook it up, and we’re good.” Not always that easy in reality. They might not realise the need for specialised software to handle structured and unstructured data or the additional cloud storage and compute required for growing data volumes. Add staging layers, too.
They stick to familiar processes and tech, which aren’t always the best for the job. So, tech and labour costs rise because data engineers, data scientists, and AI specialists are left doing the stitching using a plethora of tools—often with miles of custom code, poor documentation, and an army of expensive devs (no offense to the hardworking engineers, but you know what I mean).
Over time, expenses related to data cleansing, security standards, and updating legacy systems quietly add up. Budgets get stretched, and teams are too busy to take on new work. This is why data integration projects often face “scope creep,” where complexity and costs balloon well beyond initial estimates—and integration fails when it’s needed most.
Management buy-in
A fish rots from the head down, as the saying goes. If the top management doesn’t truly care about the state of their data, forget about using it properly. Senior management must articulate a clear, company-wide data strategy aligned with business goals. This includes defining data integration’s role in driving growth, improving efficiency, or enabling innovation. Leaders should focus on measurable objectives like enhancing customer experience, reducing costs, or accelerating decision-making and directly link these to data initiatives.
Leaders need to lead by example, showing the importance of data in making key business decisions. They must take ownership of key data-driven projects and be involved. Advocating for data integration and participating in initiatives sends a strong message that this is a strategic priority.
Experiment with new data integration techniques and tools. Don’t settle for what’s been used for years. The world moves forward, and so should you. By fostering innovation, top managers can help discover faster, cheaper, and more effective ways to integrate data from diverse sources.
And avoid quick fixes like the plague. Focus on building scalable solutions that can grow with the organisation. Data integration should be seen as a long-term investment, with a strategy that accommodates future data growth, emerging technologies, and business needs. Trust me, it’ll be much cheaper in the long run.
Conclusion
Data integration is no walk in the park. It’s messy, complicated, and can easily drain time and resources if you’re not careful. From clashing data formats and outdated systems to security headaches and skyrocketing costs, the roadblocks are real.
But here’s the kicker: if you get it right, the payoff is massive—think smarter AI, better decisions, and a serious edge over the competition. The key? Don’t wing it. Get your strategy straight, know what you’re up against, and set realistic goals. Otherwise, you’ll be left with ballooning budgets and stalled projects.
Reach out if you want to learn how we make data integration simpler at IOblend. We’re always happy to chat.
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.
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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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