Introduction to Data Integration
Modern businesses are constantly generating large volumes of data across all departments, from marketing to customer service. To leverage their abundance of information, companies have been practicing data integration.
By unifying all metrics from disparate data systems, management teams can gain access to valuable and vital insights. This then allows for the cultivation of data-driven strategies and better business decisions.
What is Data Integration?
Data integration is the act of combining business processes to unify data. Typically, the integration process includes collecting information in all formats and sources and compiling them into a data warehouse. By consolidating data, companies can use business intelligence applications to uncover actionable insights and opportunities. Data integration techniques also help organizations gain a better understanding of their current operations and position in the market.
According to a recent study from 2020, 64% of companies said they utilize data integration to improve their operational efficiency. Additionally, 56% say they use the process for faster analytics, while 50% use data integration tools to inform their business strategy. Unifying data sources is clearly becoming increasingly important for organizations to improve their workflow. With this process, teams can find patterns in their historical and real time data. This allows them to protect their bottom line and to effectively collaborate with internal teams, stakeholders and suppliers.
What Can Data Integration Solve?
Data integration provides many solutions for various business technology problems.
Big data refers to large, complex datasets that come from diverse sources. Managing big data can oftentimes be strenuous and time-consuming due to its large volume. However, data integration platforms can help make sense of all the information that is collected, in which it automatically retrieves and translates data into digestible insights.
Data silos occur when information is stored in separate locations. This will typically arise when using legacy systems or disjointed software, or when the company has uncommunicative departments.
Data integration tools will prevent silos in legacy systems by transporting data from older platforms to new ones and making it accessible. Additionally, the integration process will promote cross-functionality since the information from teams and systems must be consolidated.
With multiple data sources, organizations may run into issues with semantics and duplicates. For example, a file may have the same information but the date of one source is in the DD/MM/YY format and the other is in the MM/DD/YYYY format. Data integration applications are able to remove variations and duplicates, ensuring a single source of truth.
Data integration processes allow organizations to have one central source of data. This lets all departments and teams have access to the same information whenever they need it. It will also lessen the waiting time associated with retrieving information and will reduce inquiries since staff can find the data themselves.
How Data Integration Adds Value
Data integration can also be useful for business processes and applications.
Business Intelligence (BI)
Business intelligence refers to the use of technology to assess critical business data. A BI platform provides key insights into a company's operation and helps managers make informed strategies. However, before these tools can be used, data integration should be employed to clean, prepare, and unify datasets. This will ensure that the generated reports are supported by accurate and reliable information.
Leaders in an organization must have a comprehensive understanding of their business in order to make good decisions. To effectively be equipped with information, the company must ensure that its data is structured and un-siloed. By having access to key data, teams can identify proper steps and strategies they need to implement to thrive.
Master Data Management (MDM)
Master data management refers to the process of managing, centralizing, and unifying key metrics. There are specific guidelines on what data can be inputted into MDM platforms, in which metrics must be accurate and unique. Therefore, data integration is a vital step that must be completed before employing MDM.
By consolidating key customer information, such as historical purchases and buying behavior, companies will have better visibility into their consumer base. This will effectively enable executives to improve their customer service and tailor it to their shoppers' preferences.
Key Takeaways - Data Integrations
- Data integration is the practice of unifying and consolidating data from various disparate sources into one central location.
- The integration process generally involves collecting information in all formats and sources, then compiling them into a data warehouse.
- Many modern companies are implementing data integration solutions into their operations to improve efficiency and promote informed business planning.
- Data integration processes can solve various business issues, such as big data, silos, semantic integration, and accessibility.
- Additionally, combining datasets can add value to many different organizational processes and functions. This includes business intelligence, decision-making, master data management, and customer relationship administration.