Data Integration's 6 Biggest Problems and Their Solutions

The breadth of valuable information that data integration provides enables your organization to develop new and innovative services. However, data integration is not free of data integration problems. Without the right mindset, technology, or strategy, the way you manage your data can hinder your BI, analytics, and innovation goals. The result? A lethargic company that lags behind its competitors and is unable to meet customer needs.

But what steps can your company take to avoid this fate? You need to avoid the six biggest obstacles to data integration.

Data Integration Challenges:

What is data integration difficulty?

data integration obstacle is anything that prevents you from gaining control over the processes and products of data integration. It is the obstacle preventing you from obtaining a single, unified picture of your facts.

6 biggest data integration challenges you can't ignore

Now that we have given you a general idea of what a data integration difficulty is, let us examine some common scenarios.

Below are six data integration difficulties that may arise in your organization and possible solutions

1. Your data is not where you need it to be

You want a centralized location for your data, but you are having trouble getting it there. Sound familiar? 

These data integration difficulties are often the result of relying solely on human labor. It takes developers time to collect and merge data from multiple sources. This is the time your company should rather spend on studying the data and implementing useful business strategies. 

Therefore, to cut out the middleman and accelerate your innovation goals, it is better to deploy an intelligent data integration platform. This will do most of the work for you. It is a fantastic solution to solve your data integration problems.

2. Your data exist, but they are overdue

Some operations require real-time or near-real-time data collection. For example, if you run an e-commerce site as a store, you may want to provide tailored, targeted advertising to each consumer based on their search history. This is another problematic data integration issue.

However, if your data is not collected in the timeframe you expect, you will not be able to meet these requirements. Unfortunately, relying on your staff to manually capture data in real-time is difficult at best. Most likely, you lack the financial resources and manpower to undertake such an endeavor.

The only way to achieve real-time data entry and thus creative and responsive services is to use an automated data integration solution. This solution can reliably provide near real-time (or real-time) data without you having to expend additional resources.

3. Your data is not properly structured

The value of anomalous data that is incoherent or has the wrong format is lost. However, manually formatting, reviewing, and correcting data is tedious and takes up a significant amount of your engineers’ time. 

Data transformation solutions avoid this problem by analyzing the original base language, determining the formatted language, and implementing the transformation automatically. This technique simplifies data integration and reduces errors, especially when your data team can tag and review code at any point in the transformation pipeline.

4. Your data is of low quality

Poor data quality leads to lost money, missed opportunities, and reputational damage. That’s why data quality management is critical to driving innovation, ensuring compliance, and making more accurate business decisions. And it’s not as difficult as you might think.

By proactively checking your data as it’s ingested, you’ll reduce the amount of erroneous data that enters your systems. What’s more, you can monitor your data pipelines for outliers and catch errors immediately, before they become major problems.

5. There are multiple duplicates in your pipeline

An estimated 92% of organizations are aware of duplicate data in their systems. And while duplicates may look harmless at first glance, they can cause significant problems in the long run. The greater the number and duration of duplicates, the greater the risk to your business.

These duplicates are often the result of “silo thinking.” If your teams are not successfully sharing data and interacting with each other, duplicates and unexplained discrepancies will become standard in your data integration pipeline.

Here is how to prevent duplication and eliminate data silos
  • Create a culture of data sharing and train your colleagues thoroughly
  • Make sure everyone understands your verified data by standardizing it
  • Invest in technologies that facilitate teamwork
  • Maintain legally required reports that promote openness and data provenance
  • With control and coordination, duplication of effort will be less common.

6. There is no consensus on the importance of your data

The importance of communication between technical and business teams in terms of data sharing has already been discussed. Equally important is establishing a common language for data definitions and permissions.

You can achieve this consensus in the following ways.

Data governance: This focuses on the policies and practices associated with the data strategy.

Data stewardship: A data steward is a person who manages and organizes your organization’s strategy, implements policies, and integrates your department IT with your business strategists.

Without a strategy and a clear understanding of who owns your data, your integration practices will continue to struggle with disinformation and misalignment.

At Nallas, you can accelerate your company’s transformation by using data as the foundation for your growth and development, if you do it right.

So, take a step back, evaluate your business goals, and determine which of these obstacles are keeping you from achieving them. With the right mindset, mentality, and automated technologies, your company can overcome even the most difficult data integration challenges.