Table of Contents
- What is a data integration pattern?
- How do data integration patterns work?
- Users of Data Integration Patterns
- Data migration pattern
- Correlation Pattern
- Benefits of Data Integration Patterns
It is not uncommon to find different applications being used within a single organization. Applications help to meet a particular need within the company. Although there are robust ERP systems that help to combine certain functionalities within a company, more often than not, we require multiple systems.
Due to this combination of applications, companies need to integrate data from different systems. It is for this reason that we require to understand how integration should be done.
There are many ways of merging datasets from different systems and applications. However, there is a need to follow a standard format in performing such integrations. Hence, the need for data integration patterns.
What is a data integration pattern?
A data integration pattern is an acceptable standardized way of combining data from multiple sources.
How do data integration patterns work?
Data integration patterns use various methods such as connectors and APIs to help different systems communicate. The IT teams select a pattern based on the needs of the data integration. For example, a company requiring to reduce repetition of particular data in every system may use a broadcast pattern. On the other hand, if the need is to upgrade the legacy system into modern technology, a migration pattern may be used.
Users of Data Integration Patterns
The use of data integration patterns cuts across almost all retail, health, government agencies, and telecommunications sectors.
As a business grows, there is a need for more powerful systems such as ERP, SAP, and CRM. Most of these software have data silos that prevent the flow of data to and fro. Standard patterns help business executives to get insights from all the software in a single view.
The health and government institutions require a strict method of transferring data. To achieve integration of such sensitive data, a specific method must be applied.
In the financial sector, these patterns help to match users’ transaction records to user IDs.
Let us discuss the most common methods of data integration patterns.
Data migration pattern
Data migration entails moving data from one system to another at a given point in time. It happens for a couple of reasons, including a change from legacy systems or a combination of data from various operating systems platforms.
Typically, data from one system is filtered based on the requirement of the migration. Afterward, the data is changed into a standard format that allows easy transfer into the receiving system. Finally, the data on the destination system is inspected. This action is important to ensure maintenance data integrity during the operation.
There are different ways in which you can do a data migration. However, the method used is dependent on the kind of data transformation needed.
A popular way that helps in data migration today is the application of APIs. An application programming interface (API) is a tool that helps different systems communicate and transfer information from one application to another.
Data migration is essential because it helps organizations upgrade to new technologies with little interference to the systems’ daily functionality. Moreover, it prevents data loss and helps in the restoration of historical data in the new systems.
a. The Broadcast Pattern
Broadcasting moves data from one central system to multiple nearby target systems continuously in real-time. In this method, data only comes from one source to different destinations. However, the other systems can’t send data to the central system. That is to say; data flows only in one direction.
The broadcast pattern commits data to the destination system only if the transfer is successful. Otherwise, if there is a failure, the transfer cancels, and data is rolled back. This characteristic is called transactional.
The main advantage of this pattern is that it only sends data that has changed since the previous broadcast. In other words, it means that the logic does not send data for all items but only updates the other systems with new data.
Another advantage is that the systems update happens reliably and quickly without any data losses.
The broadcast pattern is mostly automated to save time. Generally, it can be programmed to respond to particular triggers such as changes in data. Otherwise, IT teams can set a schedule for the broadcast.
In most cases, the Enterprise Service Bus (ESB) acts as the intermediary to guarantee data delivery to the desired destinations.
b. Bi-Directional Synchronization Pattern
Bi-directional synchronization is the process of combining datasets from two distinctive systems to act as one. However, the separate systems retain their dataset and functionality.
The bi-directional synchronization pattern is mostly implemented where different systems help to give a single view. For example, when a group of companies wants to see how all different enterprises perform at one glance.
This pattern ensures direct and real-time synchronization of the two systems. The bi-directional sync pattern ensures every system continues to meet requirements for the individual target audience with data storage in different datasets.
The correlation pattern works in almost a similar style to the bi-directional sync. However, in this pattern, synchronization of datasets only happens if the records exist in both systems.
The bi-directional sync pattern creates new records for missing data in one of the datasets but found in the other one. Correlation is strict such that the records must be in existence for both systems for synchronization to happen.
The pattern makes data integration easy and fast since synchronization happens only for relevant data.
c. Aggregation Pattern
Aggregation is the process of receiving data from different systems and merging it into one system for a centralized view. The pattern is important where you want to get updated data in a particular given instance.
In aggregation, data is merged into a format of choices such as XML or CSV. Therefore it means that you do not require to have a different database to store the merged data.
Benefits of Data Integration Patterns
The following are some of the key benefits of data integration patterns:
i. Saves Time
Time is an important aspect of every business. The use of standardized approaches in transferring data helps the IT teams to save time. Companies reduce the need to analyze data from multiple sources to get a centralized view of the enterprise at a glance. Using unified ways to combining data helps fast processing of data.
ii. Reduces Errors
Data is susceptible to human error and interference in manual transfers of information. However, using the common approaches helps in maintaining the integrity and privacy of data. It helps companies to make decisions that are relevant in real-time.
iii. Better Business Decisions
Our world today is data-driven. Having a common way of transferring data helps business executives to understand how various departments are performing. It is easy to detect a struggling area of operation and recognize departments that are performing very well.
Such information is useful in making business decisions from marketing, budgeting to customer relations.
As technology advances, there is a need for businesses to adopt new methods to remain competitive. Data integration is useful to ensure that information is easily added into new systems for maximum efficiency and productivity.
Data is a key function in business. Historical information serves as evidence of transactions that happened earlier. Keeping such data is important when recalling defective products. Additionally, the company uses the data in future investments.
Transfer of data from one system to another may encounter challenges in formatting. However, using a unified approach in data integration leads to the reliability of the seamless transmission of data.
vii. Improves Communication
Communication is a key element in business. A unified method of data integration helps to give consistent information within the company. Additionally, the information becomes more valuable. Such flow of data allows departments to work better with each other.
Due to high technological innovations, companies need to apply these common methods of data integration for enhanced performance.