Introduction to Data Integration The idea of data integration isn't anything particularly new. It was a lengthy and complicated process in the past. Smaller enterprises carried out the manual process by copying and pasting a lot of information into spreadsheets. During this period, larger marketing departments and agencies mainly used Extract, Transform, and Load (ETL) software to combine data in data warehouses. Data from each data silo would be extracted using ETL software from vendors like Oracle, Microsoft, and IBM, and it would then be converted (transformed) to comply with the marketing data warehouse's data formatting standards. Once the data had been altered, it would be imported into the target database, where marketers could compare it to other data and mine it for insights, frequently with the aid of a specialist business intelligence team. This was acceptable when data came in relatively slowly, but "big data" has opened the floodgates. According to a report, 32% of businesses have abandoned ETL software because they cannot handle the volume of data. The necessity for self-service data across the enterprise is mentioned by another 24%, while 23% claim that better data quality is required for machine learning projects. There are many different alternatives for integrating data today, some of which are more complex than others. Marketers must take into account the finest data integration techniques based on your team's particular data requirements and sources. The Next Generation of Data Integration Cloud-Based Data Warehouses: Most data-driven marketing questions may be answered using data warehouses, such as comparing open email rates to clients who sign up for premium memberships. It's not the most incredible option, though, if you want greater freedom to experiment with how different data streams and sources interact, and you still need a way to standardize formats before your data can be sent to the warehouse. Integration Apps: The simplest data integration technique is application-based data integration. When you execute a query, it employs software to directly connect and integrate data from several sources rather than combine it in a single repository. For on-premise databases, it is most frequently used. The more effective integration apps are easy to set up and suitable for small marketing departments. This is frequently a workable approach in service-based industries when there aren't too many data silos or too much data volume. But application-based integration can't keep up when the volume of data or data sources increases too much. Data Integration Middleware: Middleware connects a web server with your database system via a different layer of software, much like application-based integration does in terms of data integration. You won't necessarily have all of your data stored automatically in a central repository using middleware, but you will be able to view what you need, when you need it, with little to no lag time, as the web server directly accesses each data source to retrieve the data you need for your query. Middleware is more suitable for marketing departments or firms that manage several client accounts since it can function more effectively in the cloud than application-based integrations. Data Lakes: The greatest connections and analysis between various data sources are possible with this option, making it the most advanced one. In contrast to data warehouses, data lakes maintain unstructured data in its raw state in enormous repositories. In this manner, you can access all the data points you require and combine them in any way you like. Large businesses that consume enormous volumes of data every minute, such as those from IoT devices, security sensors, or social media interactions, are best suited to use data lakes. They can handle much more intricate queries, such as keeping track of seasonal patterns related to cyclical economic changes and email subject lines. However, to be able to edit the raw data, you'll need some amount of data science skills. When your marketing data is integrated, you may utilize it to advance the level at which marketing campaign decisions are made. As a result, your team can integrate all available data signals for improved marketing results over time by choosing the best new data integration method for your marketing needs, adhering to best practices to ensure that your integrated data is consistent, compliant, high quality, and uses automated procedures. Integrated Data Produces Better Marketing

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Target

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ML Practitioners - Ready to Level Up your Skills?

Why is Marketing Data Consolidation and Integration Necessary?

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