What is Data Integration? Definition & Importance
What is Data Integration?
Data integration is increasing in popularity as businesses realize the immense benefits it offers. Although there is no single approach for data integration, there is a common definition. Data integration is the technical and business processes for combining data from disparate sources in order to view data comprehensively. Even more simply put, data integration consolidates different data sources into a single, unified view. Data integration allows analytics tools to produce business intelligence and actionable insights.
However, it is easy to understand why data integration is perceived as intimidating when considering its breadth. There are so many potential different data sources to evaluate ranging from cloud data to information sensors gathered. Different data storage types such as a data warehouse or a data lake need to be differentiated between. There is also big data and small data characterized by velocity, volume, and variety.
Additionally, there are different data management and integration techniques to consider. For example, data virtualization or data transformation or extract transform load techniques. Then there are the common challenges data integration brings, from data silos to data quality that is unideal. Unsurprisingly, with so many variations possible in data integration, there is no one integration solution available. However, there are common aspects and customer stories that may be beneficial to review.
Common aspects of data integration include a master data server, disparate sources of data, and data access from master server. Reviewing customer stories regarding the integration process can help guide other professionals interested in data integration for their business. A standard data integration process would include a master data server request initially. The master data server intakes necessary data from both internal and external sources. Then, internal or external data source extraction occurs in order to load data into a single source. Finally, the single data set is served back to the original requester.
Importance of Data Integration
Data integration is a powerful business intelligence and data management component. The benefits data integration offers businesses are outstanding. As such, data integration should be considered not only important but also as a top business priority.
Data integration is not limited to benefiting solely data analytics or data science professionals. With increased collaboration, business departments ranging from the customer service resource center to international supply chain management benefit. Likewise business processes across the board are enhanced through improved collaborative capabilities. Considering that multiple business processes are often occurring simultaneously, the ability to view data in real time is incredibly important. The ability to access data in real time is also crucial for remote workers and teams in different geographical locations.
The time data related processes require is decreased with data integration. Previously, any efforts to process data were largely manually facilitated. Manually collecting data wastes valuable employee time and can deplete morale. For example, an employee unaware of new data warehousing efforts would likely need to reperform data analytics efforts. Instead, time data related processes previously required can be used towards increasing business productivity. Additionally, when the employee experience is improved, customer service is also likely to improve.
Beyond being incredibly time consuming, needing to manually process data is also prone to human error. Employee error based data issues can result in significant data management and data governance problems. Considering the value data offers businesses, both real time data access and high quality data are essential. Thankfully, there are many data management and data integration tools available for businesses to invest in. Additionally, machine learning advancements and data integration tools developments are only further optimizing data analytics and management.
A common example of a benefit that data integration tools offer is automatic reporting. Automating reporting lets businesses load data reports in real time whenever necessary. In fact, various business processes benefit from integration techniques and tools. Another example of how data integration benefits business processes are for developers. Instead of having to manually hand code, data integration tools can assist. Alternatively, supply chain decision making efforts are often assisted through data integration.
Key-: An integration solution improves collaborative abilities for all staff members.
Common Use Cases of Data Integration
Evaluating common use cases for data integration can assist those pursuing an integration solution for their own business. Common use cases for data integration range from leveraging big data to business intelligence simplification. Leveraging big data is a use case that is especially common for tech companies. For example, social media sites receive real time customer data from billions of users around the world. For these companies, big data integration is absolutely considered a top priority.
Data lakes and data warehouses are often discussed within big data and business intelligence use case conversations. Data integration initiatives are frequently utilized to produce data warehouses. A data warehouse combines different sources of structured data into a compatible database for a specific purpose. A data lake consists of raw data for which a purpose has not yet been designated. For example, the constant real time big data social media sites receive are data lakes. Understandeing data warehouses and data lakes is essential to use cases of data intergration.
Business intelligence simplification is another common use case for data integration. Business intelligence focuses on both historical and real time data for strategic decision making purposes. As such, a comprehensive and unified view of data from various data sources simplifies business intelligence significantly. The ability to view data and analyze data in real time leads to enhanced business intelligence and actionable insights. Due to data visualization potential, data warehousing is often recommended for business intelligence use cases. A business intelligence use case benefits from the high data quality and unification that a data integration solution supplies.
Note-: Common use cases for data integration range from business intelligence simplification to big data warehousing efforts.
Key Takeaways For What is Data Integration
- Data integration is crucial for businesses to accomplish high performance and bottom line profitability.
- Common use cases of data integration include leveraging big data and business intelligence simplification.
- Gaining Insight into Data Integration
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