Data Integration: Definition, Techniques, Best Practices
Posted on March 16, 2022
Data integration is the process in which companies combine and consolidate data from more than one source in order to provide a valuable single view to the user. This process is usually the start of numerous routine data processes (mapping, transformation, and data analysis) and is a crucial part of data management.
The data integration process is often one of the first phases of a data supply chain. It’s vital that companies do this correctly as it can impact data processes and action further along the data pipeline.
If you want to learn more about this topic and find out what data integration techniques and data integration strategies you should consider, keep reading.
Why You Should Prioritize Data Integration
Why are most businesses trying to come up with the best data integration methods?
Well, most companies have several complex operations that require data from multiple sources to work effectively. Taking multiple data sources and combining them is quite a challenge.
But those who manage to handle and store data into a single system actually reduce the workload and costs of managing multiple datasets. This helps them allocate resources to other tasks.
As a business looking to move forward, you have to adapt and work with multiple datasets. That’s the main reason that data integration is essential.
Additionally, here are some other prominent benefits that should help you see why you need to prioritize data integration:
- Time-saving and efficiency. Manual data integration can be a costly and time-consuming process. Hence, using an integration tool can help you save time and be very efficient.
- Reduced chances of mistakes. Monitoring a business’s data resources and managing them is a complex process. With the right data integration tool, the chances of your employees making a mistake or duplicating data are considerably lower.
- Better collaboration. Data is something that is shared between employees. Therefore, your business needs a secure and reliable solution for data integration that allows easy and safe sharing.
Keep in mind that different businesses have different data integration strategies and data integration processes don’t look or work the same for everyone.
However, here are some examples of data integration to better understand how it can benefit your company.
Making BI simple
When you have a single view of many data sources, you get a powerful business intelligence tool. This allows a fast and practical look into the current status of the company.
Centralized data containers that power numerous departments
The data integration process comes before the process of building a database for larger companies.
In these cases, the data will often be relational. This means that it should be queryable, as well as allowing to extract relevant analysis, run reports, and access data in a consistent form. All this means that data needs to be integrated correctly.
The more data sources a company has, the bigger the potential quantity of data that needs to be ingested and integrated into their system.
For these companies, the data integration strategy requires higher sophistication. They don’t have the luxury to allow downtime or something similar.
So, if your business has a tendency for significant growth, that’s yet another good reason for proper data integration.
Challenges of Data Integration
Data velocity and data variety are often seen as the most important big data integration challenges among data managers.
Problems that often pop up have largely been solved by scalable cloud storage and in-memory computing capabilities. These breathe new momentum into traditional integrated patterns.
These problems usually arise when a user moves from a batch integration process to the integration of data streams.
For instance, in a hospital setting, delays in processing data from IoT devices using traditional ETL methods could have serious consequences. In this case, data managers need to consider integration patterns using a streaming platform to signal real-time alerts to nurses for required intervention.
Data variety problems usually revolve around handling modern data architectures which are assembled across different top-quality data repositories to store different kinds of data.
For example, time-series databases can store graph database relationship data, IoT data, as well as relational database transactional data. Traditional integration is equipped for database-to-database connections, relational databases through ETL, and also enterprise service buses.
This kind of integration usually assumes a schema that’s fixed in the underlying database. If you make a change to this schema, this creates a ripple effect that could be costly.
Data Integration Strategies
Finally, take a look at some proven data integration techniques that you can use starting today.
Keeping the end in mind
Instead of leading with a data set, lead with a question. Every data integration effort should start by establishing clear goals for the project.
Well-done data integration projects bring tangible results. So, what results are you targeting? And how can you measure them?
Therefore, start with these and similar questions and have the end in mind from the start.
Decide which data source to include
Traditional mainframe systems still play a key role in most large enterprises as well as many small and mid-sized businesses. They hold core transaction data, which is essential to the majority of data integration initiatives.
Observe the disparate software systems appearing throughout your business and industry. Spot the role that data from each of those systems should play in meeting the goals determined in your business case.
Determine data communication techniques
When it comes to data communication, real-time integration is the best standard and the one that most companies want. Batch-mode integration can address numerous cases properly and can even be created and adapted to meet an almost real-time standard.
It’s essential to consider both future and current data volumes to assess whether the capacity of the pipeline is good enough to handle the traffic. But ultimately, the communication method you select will depend on your objectives.
As you can see, data integration doesn’t assume a one-size-fits-all approach. However, there are some common segments present in most companies.
Go through this guide again and determine your business needs. Then start thinking about your data integration strategy and the best approach for your company.