3 Early Warning Signs of Data Integration Disaster

When your company decides to integrate, it’s because there’s an immediate unmet need. Data silos are inhibiting team performance. Regulatory compliance demands more seamless data tracking and accessibility. Redundant and ineffective processes are costing the company money.

Whatever the reason may be, the goal is to walk away from the project with a new level of efficiency, innovation, and competitive advantage. Unfortunately, not every project enjoys those outcomes. In fact, some studies estimate more than 80% of all integration projects fail.

Don’t start panicking, yet.

Smart and strategic planning can make your project a success. Here are three of the most common warning signs of integration disaster, and how to avoid them.

#1 Inconsistent Data

Imagine this: You’re baking a cake with the wrong flour, old eggs, and only some of the other necessary ingredients. What would you expect to end up with? A mess!

When it comes to data projects like integration, what you put in will directly affect what you get out. That’s why it is so important to do data cleaning and preparation beforehand. Inconsistent data is a red flag that can lead to inaccurate insights.

Luckily, there are a few things you can do to mitigate this risk:

Establish data quality standards

 If you don’t have them in-place already, use your integration project as an opportunity to create data quality standards. Your standards should set requirements for data accuracy, data completeness (Which fields must be populated?), required formats and naming conventions (Are we adding addresses with “unit #” or “apt #”?), etc.

Each record or entity should also be auditable, so you know who entered it and when. Any special data security and compliance considerations for your business or industry should also be clear from your data quality standards.

Audit and cleanse data sources

With your new data quality standards in place, your teams (or your integration partner) can audit and cleanse your data in preparation for the project. This is another crucial step for eliminating inconsistent data and avoiding a data integration disaster.  Some of your data cleaning will have to be performed manually, but some can be automated by a solution like StarfishETL. Even after the integration is complete, you must set a schedule for regular data auditing and cleaning procedures to continue. Without them, your integration will never reach its peak potential.   

#2 Slow Data Processing

Latency can be a subtle precursor to data integration disaster. It hurts the user experience and the overall performance of the integration. Getting rid of junk data at the start of your integration helps mitigate data processing latency, but there is still more you can do.

It starts by choosing an integration solution that has powerful scaling capacity. What makes an integration platform scalable? Factors like load balancing, parallel processing, multi-tenancy, and a distributed architecture all contribute to scalability.

An iPaaS offers these scalability features and leverages an underlying cloud infrastructure to scale up or down based on demand. It can adapt to growing data volumes, increasing transaction rates, and expanding integration needs.

Most organizations will process changes between the systems on a set schedule every day, week, or month, depending on their integrating systems. Many times, however, integrations require real-time visibility so teams can see changes immediately. This is especially true in industries like manufacturing, financial services, transportation, and ecommerce where it's crucial to respond to shifting conditions and inventory levels.

If the volume of data is a concern, consider implementing caching mechanisms to store frequently accessed data. Caching reduces the need for redundant processing and speeds up access to commonly used data. If you’re using an iPaaS, it will support parallel processing to execute multiple integration tasks simultaneously. This is essential for handling large volumes of data and ensuring that integrations can occur concurrently, improving overall throughput.

#3 Communication Breakdowns

Internal communication gaps lead to integration failures. The entire organization should be aware of the integration project and its goals, and they should understand the role they play in helping reach those goals. It’s up to the team heading the integration project (usually IT and a few key players from other departments) to document the integration process.

This team must also hold regular meetings to discuss progress, challenges, and updates. Project management tools are helpful to centralize information and track progress along the way.

Meanwhile, the integration provider should keep you looped-in on the progress of your integration, discuss any issues that have come up and how to solve them, and any changes that might affect your go-live date. Clear and consistent communication is paramount to the overall success of your integration.  

Early detection and proactive measures are key to preventing data integration disasters. By staying vigilant and implementing the suggested strategies, businesses can ensure a seamless and efficient data integration process, safeguarding their valuable data and supporting informed decision-making.