7 Data Integration Strategies That Will Save Your Business Time and Money
The human mind can’t fathom the amount of data business entities collect. They discover and extract data from numerous data sources and use it to get real-time insights that power quicker and better decision-making. However, the amazing things data analysts and business owners do with data are possible without data integration
Data integration is various processes of gathering and formatting data for immediate or later use. Some of the use cases include sharing business intelligence, upgrading legacy systems, and sharing data. The process can be grueling and expensive, but it’s necessary. You’ll have a better understanding of data integration solutions and strategies after reading this article.
1. Data Virtualization
Data virtualization is the optimal business data integration process for many enterprises. It enables business users and data analysts to query and find real-time business insights.
Data virtualization is like creating one big data store for a bunch of databases. It’s also called a data federation. Virtualization provides access to a vast store of data from disparate sources. The best part is that there’s no need for data migration because the data remains in source systems.
2. Manual Integration
Manual integration is one of the more time-consuming and laborious integration strategies. As the name suggests, all of the processes are manual. Extract, transfer, load (ETL) platforms are the data integration system of choice for this strategy.
The ETL process involves humans manually extracting and transforming data from disparate sources before making it ready for use. It takes a dedicated team of data scientists and business users to run an ETL operation successfully.
3. Enterprise Application Integration (EAI)
Enterprise application integration is another critical data integration process. It involves combining workflows and databases from different sources so they can share data seamlessly internally and externally. Data cleansing makes data from disparate sources recognizable to different applications such as CRMS. Enterprise application integration ensures the enterprise apps companies use have the same information and format. EAI helps to ensure data quality.
4. Data Consolidation
Data consolidation is what its name suggests. It’s the process of collecting data from various sources and storing it in a centralized location. ETL systems are the most common data integration tool for this process as it allows data scientists to format data from varying source systems.
5. Enterprise Data Replication
Data replication is the process of making multiple copies of data and storing it in various locations to improve accessibility. One of the advantages of replication is that it allows teams, partners, and stakeholders to collaborate better using the same business insights.
6. Data Propagation
Data propagation is a great data integration strategy for when you need to share data. It’s a process of copying data from one system to another. It’s an event-driven process, meaning certain actions and commands initiate the integration process. The event can be undertaken by a human or via artificial intelligence.
7. Data Warehousing
Data warehousing is the most labor-intensive data integration technology. They serve as repositories for vast amounts of data from different disparate sources. Data analysts and business users have to manually extract, format, and transfer data into the data warehouse.
As you can see, there are many different data integration processes and tools. With the right data integration system and strategy, your company can turn business data into business intelligence. The best way to pick the optimal data integration strategy is to analyze your company’s current and future needs. It’s also important to assess your enterprise analytics infrastructure to gauge compatibility with different data integration platforms. The key to success is to determine your end goal and work from there.