With the advent of the technological era, an increasing number of companies are bringing their work into the online sphere. Specifically, as businesses become more connected to the cloud network, company data and statistics are now commonly stored and presented in a digital format.
Data analytics is an important process in assessing business health and formulating effective, long-lasting strategies. With this, companies that generate significant chunks of data each day must consider utilizing tools that efficiently streamline the process of analyzing numbers in marketing, sales, R&D, and customer service. Data integration is one such approach that can help your business thrive, and in this article, we’ll describe the benefits of data integration and how you can implement it in your business.
What Does Data Integration Mean?
In short, data integration is the practice of collecting data from various independent sources and storing them in a single centralized dataset, usually called a data warehouse. The warehouse needs to be sufficiently flexible to allow the management of a large assortment of data under different types. Data integration allows information to be condensed into a single, unified view, which makes analytics more accessible and efficient.
Collecting data might be an easy process nowadays, with so much information available both offline and offline, but actual, meaningful analysis of this data can pose a significant challenge. Thus, the process of data integration provides a pathway for business owners to carry out timely data management, acquire substantial insights about recent numbers, and obtain knowledge that can be used to implement better policies and strategies. While there isn’t one single approach to incorporating data integration, typical solutions involve a data source network, a primary master server, and clients requesting data from the server.
What’s the Difference Between Data and Application Integration?
Data integration is commonly confused with other terms, like application integration. While data integration involves redirecting multiple sources of information to a unified data warehouse, application integration is more concerned with the real-time transportation of live, operational data between two or more individual applications to keep them in sync. Application integration deals with checking for data consistency throughout different information channels and managing interconnected operations carried out by separate platforms — to put it simply, application integration is useful for operational situations, while data integration is important for analytical situations.
How Does Data Integration Work?
As previously mentioned, there is a vast range of approaches to data integrations that you can utilize, depending on many factors such as overall business infrastructure and IT development. In usual processes of data integration, however, the first step begins when clients release a request for data access from the master server in a data integration system. The next junction involves the master server collecting heaps of data from both internal and external repositories. As data extraction is ongoing, the information is combined into one comprehensive data set, which is sent to the client for use.
Data integration is a processing machine that churns out data insights and trends for all types of business information that you have available. There are many real-life examples of data integration being used in the digital world at the moment, which include Google Analytics for website analytics and customer tracking as well as MySQL databases for user information storage.
Present Challenges to Data Integration
For businesses unfamiliar with data integration mechanisms, it can be challenging to begin incorporating these processes into the regular work routine. Even though time and money are both saved in the long run by effective data management, companies and organisations must resolve several challenges common to building and maintaining suitable integration systems. These include achieving the end goal — if a business decides to acquire a data integration system, it must understand the types of data required for analysis as well as how the analysis will be conducted.
Furthermore, data integration systems must undergo constant updates to accommodate changing demands coming from regulatory agencies as well as to keep up with new sources of data, like IoT devices and videos. Lastly, data needs to have sufficient levels of detail, especially considering that external data sources usually do not have the same precision as internal data sources.
Benefits of Integration in Data Management
When it comes to systematic and structured data management, various integration techniques can provide benefits regarding an organization’s data assets.
- Promotes active collaboration: Members of all departments within a business organization can easily access all types of company data for different projects.
- Increases efficiency and ROI: Data integration significantly cuts down the number of hours needed to collect data and perform analytical procedures.
- Decreases data inconsistencies and errors: Automated synchronization of data systems allows employees to keep up with existing data repositories and obtain data reports in real time.
- Delivers valuable business insights: Data integration ensures that issues regarding data quality are easily identified by the system, which means more improvements can be implemented to improve present sources of information.
Real-World Importance of Data Integration in Modern Business Fields
Many enterprises make effective use of data integration techniques to promote company growth and improve business operations. We discuss three important industry sectors that use data integration for comprehensive insights and analytics below.
Banking
Financial crimes, such as fraud and money laundering, are rampant around the world, depleting heavy resources from banking institutions. Data integration allows banks to identify outliers that may point to possible cases of fraud or laundering, thereby preventing financial loss.
Sales and Marketing
Sales and marketing are customer-oriented processes that involve a full understanding of a consumer’s needs and wants. With so much individual detail required to carry out successful campaigns, data integration enables easy management of marketing information, which increases overall success rates.
Retail
Online shopping is a huge industry, and retail owners must deal with heaps of data about different competitor products and their target audience. Since a data integration system unifies data analytics into a single location, retailers are quickly able to create better business strategies using information about inventory, labour hours, and other metrics.
Strategies for Implementing Data Integration in Your Business
Choosing the right technique for incorporating data integration in your business involves determining business size, business needs, and available business resources. Data integration also depends on the level of automation that business processes are executed with, so it’s important to assess which data management tools are best for you. Here are a few examples to help you get started.
- Manual data integration: This type of integration technique requires individual users to manually gather data from different sources through direct access to interfaces. Data clean-up and combination are also executed manually, which means this process only makes sense for smaller businesses with minimal resources.
- Middleware data integration: This approach involves the transfer of integration logic using a mediator called a middleware application. Middleware data integration helps in normalizing important data points and carrying them to the unified data pool. The process is especially useful when dealing with legacy applications by enabling data integration systems to have easier access to data.
- Common storage integration: This is the most frequently used data integration process when it comes to data storage. A new integrated system is created in which the original source of data is copied. This copy is processed to finalize a unified view, which makes common storage integration particularly useful for traditional data warehousing.
Features To Look Out for in a Data Integration Tool
Before deciding on a specific data integration tool, check to see if it meets certain standards that will overall simplify processes within your business. One feature to watch out for is whether the tool has an open-system architecture. These tools are more flexible compared to those that have other types of system architecture, and they also mitigate the issue of vendor lock-in. Also, make sure that your integration tool is portable and easy to use, as data has to easily be transported across different cloud models. Lastly, a data integration tool with many connectors will increase efficiency by calling on multiple systems and applications for data gathering.
Improving Present Data Integration Strategies
You might have already implemented certain data integration techniques to streamline crucial business processes. However, there’s always room for improvement in your implementation of data integration technology. Three objectives that you should consider incorporating in your data integration technology are easier configuration, faster testing, and efficient reusability.
Data integration tools are set to forge connections with applications and systems. As changes in data and various environments occur, the configurations of data integration tools must make quick adjustments. In order to avoid the inefficiency of manual configuration, organizations should consider the alternative of variable management through scripts or deployment interfaces.
Delays in testing should also be decreased in a data integration system to increase productivity. These delays are often caused by businesses having to deploy specific processes before they can test or by having to develop logic for testing environments. To mitigate this issue, an API can be used to infuse data and record testing scenarios, reducing the overall time taken.
Lastly, data integration must be able to leverage previous investments, like legacy applications, which remain crucial gears of many different business processes. While many aspects of legacy applications have been cast obsolete by newer platforms, they still offer important assets that can be reused in other services. Data integration systems must ensure that legacy applications can easily transition to more modern environments.
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