Businesses in modern times are starting to adapt to various types of technology to get more benefit in deciding strategies to advance their business. Some of the technologies that are often used lately are data integration. In this article, we will try to discuss more about Data Integration.
What is Data Integration?
integration or Data Integration is a process of combining data from various sources into one. This data integration and combination process starts from a data collection process to cleansing, and mapping ETL, as well as data transformation. The purpose of data integration is to generate effective business intelligence processes resulting from the analysis process.
Data integration usually involves several elements such as data sources, data storage servers, and clients accessing data from data storage servers. In the process, Data integration starts from the user (client) request to the data storage server. After that the data is extracted from the data source and displayed in a data set for further analysis.
What Are the Benefits of Data Integration?
The next question is why do we need data integration or data integration in business? What benefits can we get? To answer this question, we will discuss in more detail some examples of its application.
Every company and business always has data generated from various sources. For example, there is data that must be combined from the CRM system, website traffic, and also some other marketing software. All data obtained from this data source must be combined for the benefit of data analysis with data analytics tools in order to generate insights for business strategy.
With data integration, all of these processes run smoother, faster, and are efficient and effective. This example is one of the benefits of data integration. We have some examples of other benefits, below:
1. Improving Systems for Collaboration Between Divisions
Every company has functions, divisions, with different tasks. With an integrated system, data will become easier to access for a particular project. In addition to being accessible to anyone in a company with data integration, we can also secure data using several data network security techniques that are usually available in these tools.
All divisions of a company can also make data better according to the needs of each business. This is where businesses get the benefits of being more efficient and effective in data processing.
2. Reducing Errors Data
Redundancy in a data processing makes the analysis results not optimal. With manual data collection, we often experience unavoidable data duplication, especially if data collection is carried out from various sources. Not to mention the data analysis process itself will be more appropriate if the data collection is really complete. One of the benefits of data integration is to ensure that the data collected is completely complete and accurate without any duplication of data.
The Data Integration solution is capable of synchronizing data from various sources so that data storage can automatically be done from anywhere and anytime. Changes in data can be stored in real time, and data can be viewed in real time as well.
3. Generate More Valuable Data Data
integration strives for quality data that is more valuable to the business so that it can be used to make business strategic decisions. During the data integration process, usually data integration platforms and solutions will use various data processing techniques needed to ensure that data quality is not reduced and is more accurate.
Application of Data Integration in Business
Data integration solution is a versatile solution in meeting business needs according to needs. Some examples of the use of data integration tools are as follows:
1. Utilization of Big Data
Large amounts of data storage can cause very complex problems. Companies like Facebook and Google process data non-stop from their huge range of users. We can call it big data with a very large level of information. With this amount of data, the need for data integration or data integration becomes more useful in business operations. Data integration is certainly needed in the use of Big Data or large amounts of data.
2. Development of Data Lakes and Data Warehouses
The implementation of Data Integration, especially for large companies and enterprise businesses, one of which is used to create data warehouses or data lake solutions, which also combines data sources into a relational database. The data warehouse allows the use of data queries to produce analysis more quickly and effectively. Some examples of companies utilizing data integration for data warehouses are such as Microsoft Azure and AWS.
3. Simplify Business Intelligence (BI)
Data integration is a platform that is able to provide a unified view of data from various sources so as to simplify the analysis process for Business Intelligence. Companies and businesses can quickly understand, see the available data sets for immediate action according to current business conditions.
ETL and Data Integration
ETL or Extract, Transform, Load is one part of the data integration process where data is taken from the data source and sent to the data warehouse or data store. This process runs continuously to produce consistent data information for business intelligence and analysis.
Challenges for Data Integration
The data integration process that takes data from several data sources and converts it into structured data certainly has a high level of technical challenges. Although data integration techniques have a high level of effectiveness, there are several obstacles and challenges that must be faced.
Some examples of common challenges below are those that are often faced by data integration tools and solutions:
- How to Get Quality Data: A business must know the data it needs. Implementing data integration for business requires users to know what data to collect and analyze. They also need to know where the data comes from, and how often it needs to be updated.
- Retrieval of data from the old system: efforts to integrate data, of course, include transferring data from the old system to the new system. Here we often lose a certain range of data so it is very difficult to get data from the old system to change to the new system.
- Retrieval of data from new devices: New technologies are constantly evolving where the types and types of data collected are also constantly evolving. Sometimes the data obtained from IoT devices, sensors, and so on is still unstructured. The challenge of data integration is often to speed up the process of integrating data from these various data sources.
- External Data: Data taken from external sources may not have the same type as internal data owned by a company. This will be a challenge in itself.
Data Integration Processes in Business
Some data integration has several processes and strategies that are generally the same from one business to another. Some examples:
- ProcessManual data integration is a process of collecting data from various data sources manually. This process is very inefficient because we ourselves do data cleansing, and combine it to be stored in a data warehouse.
- Middleware data integration is a technique using middleware applications that serve as a supporting tool to normalize data.
- Application-based Integration is an integration process where the software performs the search, retrieval, and integration of data automatically according to our needs.
- Uniform access integration is a type of data integration that focuses on making data appear consistently when accessed from various sources.
- Common Storage Integration is a process that is very often used in the storage process. Data retrieved from the source is stored in an integrated system and processed into one unified view.
Implementation of Data Integration for Your Business
After knowing various information about data integration or data integration, of course you want to make sure your business gets the benefits of this technology. Data Integration from BigBox will help you implement unified data integration very easily. All data in your business can be stored and processed into an insight to be used as a business strategy.