Enterprises and large organizations generate a high volume of data every day. While small organizations can manage and analyze their data with in-house solutions, that’s not the case of enterprises. To stay competitive, they have to manage and analyze the growing data in real-time. This requires the use of intelligent data integration coupled with process automation. What is data integration and why is it important business-wise?
What is data integration?
Intelligent data integration helps to connect multiple sources and automate the workflows across the organization with no code efforts.
Data integration software automates and triggers events across multiple, cross cloud and on-premise solutions to orchestrate business processes for cross-functional execution.
Why is data integration important?
The use of a data integration platform allows business professionals to merge databases between organization departments, and accelerate the analysis. Moreover, it helps to mitigate the risks of data processing, eliminate human errors, and drive operational savings.
Challenges in data integration
Some organizations delay starting with automated data integration. And they may face the next challenges:
- the vast majority of data is isolated and stored in different places – which prevents data-driven decision-making
- data has various formats and structures, which makes the analysis more resource-intensive
- problematic and costly to manage and connect the data with in-house IT resources
- employees typically can’t access data on request, and it slows down the communication with a customer
- departments have to manually process, collect, and analyze high volumes of data – no time to scale the business and work on new opportunities,
- no unified view of information in the organization, which prevents business leaders from analyzing the information in a consistent way
- lack of synchronized data prolongs the decision-making process.
Common techniques used for data integration
There are numerous data integration automation techniques that streamline business processes. The most noteworthy ones include:
- Data consolidation, which helps to create a centralized data repository used for reporting and data analysis.
- Data federation, which helps to create a virtual database with documents from different sources.
- Data propagation, which makes it possible to copy data from different sources , and frequently relies on methods such as enterprise application integration (EAI) or enterprise data replication (EDR)
- Data warehousing, which makes it possible to use a common storage place to organize, and format data. In this case, data from the entire organization is stored in a special data warehouse.
What outcomes does data integration bring?
Deploying data integration solutions accelerate data analysis and helps to:
- connect cloud, on-premise applications and systems in the organization
- develop strong data infrastructure with a full control
- automate data workflows in the organization
- obtain higher data quality with no gaps and issues resulting from human errors
- increase the effectiveness of the collaboration between multiple departments within several processes
- grant immediate access to the data to every employee
- streamline decision-making with real-time data collection and actionable insights
- enhance customer experience and purchase processes
- identify new leads and opportunities.
How to choose the best data integration solution?
The market abounds in data integration solutions that offer various techniques. However, while choosing the solution it’s imperative to ensure that the selected data integration platform enables:
- multi-source data extraction and synchronization
- process and workflow automation
- efficient processing of data in different formats,
- quick and transparent data view.
Organizations striving to stay competitive have to use innovative technologies such as intelligent process automation and data integration. This is a driver of efficiency to digitize and automate processes and operations, eliminate human errors, get extra time and decrease costs.
