Machine learning and advanced analytics- In this category, Marketers typically don’t know what they are looking for in their data.However, the end requirement is still to be able to report on known metrics. Data must be extracted from multiple sources, at varying time intervals, and transformed with a large number of business rules before it can be consumed. Business Intelligence with complex drill-down reporting using multi-source data-In this scenario, the marketing needs are too complex to be able to process data in a BI/analytics tool.All the logic for processing this data happens in the visualization tool (e.g. Most often, their requirement is to simply pull data from these sources and then push into a data warehouse without any transformation. Something like customized reports from Google Analytics, Google Ads, or Facebook. Basic reporting -In this category, Marketers are only looking to do basic reporting and most often with data from isolated sources.We divide marketing business needs into three categories: Given the range of technology options for cloud-based data warehousing, it is important to align investments to actual business needs. Aligning marketing technology investments to business needs Try Supermetrics for BigQuery with a free 14-day trial. In this article, we evaluate Google Cloud, Snowflake, and Amazon Cloud and discuss their specific technology components and capabilities relevant for marketers looking to quickly and efficiently convert data into information. Products from companies such as Snowflake, Amazon, and Google Cloud provide a full suite of capabilities for significantly expediting not just the core data warehousing phases of data preparation, transformation, and loading but also in how this data can be used for visualization and machine learning. Not surprisingly, leading technology vendors have jumped into the fray with each providing its own technology flavor for addressing the common drawbacks of on-premise data warehousing. Not only is this a significant capital investment and involves a very high time-to-insights-delivery, this model has a significant reliance on IT, and almost no self-service potential as far as marketers are considered. While marketing data warehouses are not a new concept, the legacy model to implement them has serious drawbacks.Ĭustomers need to first purchase large capacity hardware, install and manage it locally (on-premise), and then buy and deploy additional tools to extract, transform, and load data. Because of the increasingly fragmented marketing data landscape, data-driven marketers now need specialized tools to reduce the time it takes for data to be processed into information.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |