Data plays a huge role in the important decision-making of many business leaders. Many companies are looking for new advanced ways to get more accurate data. Here is where data enrichment comes in handy. Merging data from various sources into a dataset allows companies to get more useful insights.Â
Adopting data integration is quite expensive and usually accompanied by different difficulties. However, with these five tips, you will be able to easily overcome any data integration challenge.Â
What is data integration?Â
Data integration is the practice of combining data from various sources into one dataset. The main purpose of data integration is to fulfill information needs, and provide users with all data across different subjects and structures.Â
There is data integration software that facilitates the processes. It connects and routes data from sources to target systems. Data integration is also essential for other important processes like reporting, predicting trends, and analysis.Â
How best practices can helpÂ
When businesses follow best practices for data integration, they reduce the chances of making common mistakes that usually lead to financial losses and frustration. The following practices will help you get the most out of the data integration process:
1. Find a data integration solution that brings long-term benefitsÂ
The data integration process is not a one-time thing. As you get more relevant data, you can update your integration solution, adapt it to your new tasks and needs. Successful and well-prepared data integration projects bring long-term tangible results. This is also helpful to avoid potential data integration challenges, like having too much irrelevant and low-quality data.
2. Identify your business needs firstÂ
Take some time to identify your business needs before committing to any integration platform. This will help you to build a relevant approach to fulfill those needs. Determining the goals of the data integration will help you avoid using too much data, and find the most accurate and relevant data for better insights.
3. Track your system to know when something failsÂ
Make sure your integrations have tracking features, so you can easily notice if any issue occurs. This will help you quickly react and fix failure before it causes any larger issues. This will also help to avoid collecting irrelevant and low-quality data that may negatively impact final insights.
4. Choose smaller data integrations, over large and complex onesÂ
It’s worth doing small and simple data integrations, as you can get narrowed and more detailed insights into different business processes. In large data integration, there is a higher chance of losing clarity and important details. Large data integrations also require dealing with large volumes of data, which may cause challenges with the storage and maintenance of this data.
5. Use system integration software that lets you add changes easilyÂ
Make sure your integration software allows you to quickly add changes and updates. As your organization evolves, and as new data sources become available, you need to update data integration techniques and align them to relevant objectives. Having integration software also allows you to share access to data with other authorized users, making data easily accessible for different departments in your business. This solves an issue with missed data updates and helps to avoid outdated and irrelevant data.Â
ConclusionÂ
Data integration allows businesses to have access to enriched and valuable information about different aspects of the business. Many businesses face challenges – like low-quality data, or an abundance of data – that impact the quality and effectiveness of the whole data integration process.Â
However, by using best practices, you can minimize the chance of facing any difficulties and make the whole integration process run smoother. With the right data integration solution, you will get an overview with useful insights that will help you make better decisions overall.
