With the quantity and sources of data we use continually increasing, data integration has never been so important. According to a survey by IBM over 32% of organisations cited the need for new infrastructure as the top driver behind their decision to invest in a new data integration solution. This infrastructure supports other requirements such as having trusted data for analytics.
A vital part of setting up a data integration solution is ensuring that data is usable as quickly as possible.
Data integration tools help by standardising the process of integration. The patterns are, in their simplest form, steps to solving a problem. In this article, we outline three of the principal patterns that can be used to help set up a data integration solution.
Data migration forms a vital part of any data integration solution. It is the process of transferring data from a source to target system. Data migration is of particular importance if a new system is being introduced within an organisation. Other uses of data migration include backing up datasets and consolidating existing systems.
Data needs to be maintained in such a way so that it is accessible regardless of how it was created or how it is managed. Data migration allows us to do this, but it needs to be tackled in the right way to be a valuable part of your data integration solution. Forbes lists some best practices to follow to minimise IT downtime and to ensure a smooth migration.
Data aggregation involves combining data from different systems into one unified system. This means that you’re able to extract and process data in one place. This is much easier than having to retrieve data from multiple systems using Application Programming Interfaces (APIs). Data is up to date, in real time, meaning you can see the information you want when you want it.
The aggregated data is within a united application consisting of multiple systems. This means that you can have visibility of the information in one place without having to know all the systems. The primary uses of this integration pattern are:
- To modernise older systems
- To allow you to easily create reports with data from multiple systems
- To create a unified data system for compliance purposes
With the aggregation system, data can be merged and formatted as and when you need it. The system also has the potential to then merge data into multiple systems as an output.
Bi-Directional sync allows data to flow in both directions. This means that two datasets can be viewed as one while still existing separately. Bi-Directional sync works by sharing data across their APIs. These APIs work instantaneously so any change to data in either system can be seen in the other.
Bi-Directional sync is a valuable part of a data solution where combining systems could compromise on quality. If you’re existing systems already provide the best functions for your business, you won’t want to lose them. Synchronisation allows organisations to harness the best of both while still ensuring data is up to date and compliant.
Migrations, aggregation and synchronisation are three of the most common data integration patterns. They will be invaluable if your organisation needs to review its data integration solution.
What are your organisation’s top priorities concerning data integration? Let us know in the comments below.
Founder Dinis Guarda
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