The Unmapped Challenges of Data Integration
Every senior executive recognises the importance of data-driven decisions for their business. Yet many have learned, often the hard way, that data projects can be expensive and frequently fall apart during implementation. We’re not talking data exploration, but the projects destined for production.
According to Gartner, over 85% of these projects fail, for various reasons. But one major issue is that organisations often grossly underestimate the immense challenge of integrating their data and systems.
The biggest mistake organisations make is assuming they provisioned sufficiently for eventualities during planning/scoping phases.
However, the actual complexity of integrating legacy systems, poor quality or siloed data, old business logic, etc is usually much higher. Scoping phases miss the nitty-gritty integration details that only get discovered after the teams are knee-deep in it.
Most projects are ill-prepared for the additional work and miss the timescales and budget and then fail.
Data Integration complexities must not be underestimated
Data integration is the process of combining data from diverse sources to offer a unified view. It is central to any data project. Check any project plan and notice that data integration eats a substantial portion of the resource allocation. Get it wrong, and your project is derailed.
Adopting the right strategy, resources, and tools to deal with data integration is thus crucial. Your strategy should account for adaptability, reserve capacity, and rapid but robust integration capabilities (it’s too tempting to put a temporary band aid).
Companies often compromise on technology, timelines, and expertise to save costs, but this approach bites them later.
Why is Data Integration challenging?
Businesses today juggle a ton of apps, databases, and cloud services. This means data’s everywhere, in all forms and ages. Some data’s been around since bell-bottom jeans were cool and some still live on actual paper. I don’t envy the poor sods tasked with stitching these together.
No project can escape integration challenges. They stay hidden and surface at the worst of times. The best you can do is be prepared to deal with them as they arise. Most project plans underestimate the required effort and don’t provision enough time and resources to deal with the integration issues.
Typically, experienced data engineers and ETL developers handle data integration within the organisation. Their roles include designing data pipelines, establishing connections between systems, and ensuring data quality. They must be released from the BAU to focus on the project. Many businesses put their engineers on the project part-time or prefer to outsource data integration entirely to a third party, which often leads to project delays.
Imagine a company using a modern cloud CRM system, a legacy on-prem financial system, and an external supplier database. Each of these systems store data differently, creating isolated data islands that don’t easily communicate with each other. Then the engineers realise that the financial system is a summary output of two intermediate aggregator models with no documentation and people who remember the business logic. It takes time and expertise to figure it all out and rewire.
Then there are all sorts of other integration puzzles to solve.
Legacy systems
Consider an old Point of Sale (POS) system that a retail store uses. It might store data in a proprietary format and might not even have an API. Extracting data from such systems is pure archaeology — it requires special tools, expertise and techniques. Worst still, the people who built those systems have long gone, taking the knowledge with them.
Challenges with external data sources
Say a company wants to integrate data from an external market research firm. You then realise this firm uses different terminology with a completely different data structure. Suddenly, you are dealing with a puzzle piece that doesn’t quite fit your existing set and needing to trim it meticulously to fit in.
Cloud integration issues
Many organisations are transitioning to cloud services like Azure, AWS or GCP. Each of these platforms has its unique data storage mechanisms and structures. Integrating with them sometimes feels like learning the rules of a new playground every time. You need to train/hire your people and obtain specialist certifications for each provider.
Data standardisation
Then you uncover data sources where one system labels gender as “M/F” while another uses “Male/Female”. Such discrepancies might seem trivial but can lead to all kinds of integration problems. You need to build a mapping logic to unify those conventions into a singular format. Not rocket science but can be quite a time-consuming task, sapping your resources away from the main delivery.
The ever-evolving data landscape
Just when you think your data is seamlessly integrated, a system gets updated, a new data source is added, or business objectives shift. Staying agile and adapting to these changing scenarios is like a river finding its course — it needs to be fluid and dynamic. But it also must not be an all-consuming job that you have to throw ever more tools and people as your grow at the problem.
Real-time data integration presents additional hurdles
Integrating real-time data presents its own suite of challenges. Systems must be robust enough to manage constant inflows of data, ensuring quality, order preservation, and low latency.
Then you need event processing for simultaneous analysis of multiple data streams. Merging this data with other sources or batch data introduces another layer of integration complexity. The immediate nature of real-time data requires robust fault tolerances and speedy recovery capabilities.
Legacy system integration, scalability, security, and skillset requirements add further to the complexity. We’ve decided we need real-time data from our A/S 400! OK…
Time to decrease failure rates in data projects
Linking up data systems isn’t just about hooking Point A to Point B. It’s a deep dive into each system’s quirks, making sure everything speaks the same language, and prepping for the unknown. Many times, the full scope only becomes clear once you’re knee-deep in the project.
To guarantee success in your data projects, make sure these aspects are in order:
Set a clear scope, realistic delivery expectations and a sufficient budget.
Never do a “big bang” project – your chances of failure go up exponentially. As we discussed above, you can never plan for every eventuality. Have a big vision but execute in manageable (modular) chunks. Aim for an MVP and evolve from there. The business will see the value much faster this way, improving the buy-in. And you will better track your spend.
Assign a competent leadership team with the authority to make necessary decisions.
Various factors can affect original plans. The leadership team must see the project through and be able to adapt effectively within the overarching constraints. Ensure you fully engage the business stakeholders who know their data and systems.
Gather a rockstar data integration team with the necessary expertise to deliver the project.
Bring in external help if you lack expertise internally. Do not scrimp on this. It will be cheaper in the long run. Modern integration tools will help to keep the team compact but do bring in the best talent you can afford. And make sure the team is fully focused on the task. If they do it part time, your project will fail.
Give the integration team the freedom to use the most appropriate integration tech.
Do not just use what you have “in the cupboard”. The team will need the best-in-class tools to address integration challenges quickly and efficiently. There is plenty of modern technology available to massively increase your team’s effectiveness. Just keep an eye on the cost. Some of the best tools do not cost a fortune to learn, use, run and maintain.
We have developed IOblend with exactly such use cases in mind after working on numerous data projects over the years. We have standardised and abstracted away all the coding complexity to allow the engineers to focus on architecture and business rules. Our approach reduces the development effort and cost by over 70%. Think of it as a data integration “accelerator”. Tools like IOblend give your teams the capability to quickly resolve challenges and keep the project on track and budget.
Conclusion
As you can see, the success of data projects hinges on solid delivery, which, in turn depends heavily on successful data integration. The world of data integration is intricate, with countless moving parts, hidden obstacles, and constant evolution. Whether dealing with outdated legacy systems, the complexities of real-time data, or ever-changing cloud environments, the process is undeniably daunting.
Do not underestimate the complexities of data integration in your data projects. It’s not just about connecting the dots. It’s about creating a resilient, dynamic, and coherent data ecosystem that will drive your informed decision-making.
Get in touch if you want to discuss your data project challenges. We can definitely help.
Addressing data integration challenges effectively, IOblend offers a powerful solution with its real-time, production-grade Apache Spark™ data pipelines. This platform excels in integrating diverse data streams, whether from on-prem to cloud or between different cloud environments. Its seamless integration with key technologies like Snowflake and Microsoft Azure, along with support for both centralized and federated data architectures, ensures a smooth data integration process. The automated DataOps approach of IOblend simplifies the entire data journey, from extraction to transformation and loading. By handling streaming and batch data efficiently and offering robust data management and governance, IOblend effectively overcomes the complexities inherent in data integration, making it a valuable asset for any organization facing these challenges.
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