![]() This includes reformatting for uniformity, such as restructuring IL, Il, Ill., and Illinois to all read “Illinois.” Data is also cleansed of inconsistencies and missing values, duplicate values are removed, and data is prepared for the business to easily interact with it. Data is cleaned, deduped, and transformed to align with how the business wants to analyze the data in your data warehouse, ensuring data quality and ease of use. The data transformation stage is really “where the magic happens” as this is where you make your data usable. This could be done manually or it can be automated utilizing orchestration workflows and ETL that eliminates the manual process, making it much faster and more reliable to analyze your business data. Or maybe your marketing department, in particular, needs an ETL solution they may store customer data across multiple systems, such as your enterprise CRM, email marketing software, and a digital advertising provider. This could be financials, customer details, and internal HR information. What is ETL?ĮTL involves collecting the data that is most relevant to the key performance indicators you are trying to analyze – which could be all of the data across your organization or just pieces of the data stored in various source systems – centralizing, transforming, and modeling it, likely in a data warehouse.Īs previously mentioned, ETL stands for “extract, transform, load.” Let’s break that down: Extractĭata is gathered from the relevant data sources (e.g., ERP, CRM, application databases, SaaS applications, flat files, and/or Excel spreadsheets). But to get you where you want to go, ETL should be a part of your data story.Īt Aptitive, we aim to help you feel confident through all phases of your data and analytics journey so today I’m answering “what is ETL?,” including where ETL fits into data management and why it may be an essential precursor to your organization’s analytics goals. We understand that your CEO wants to hear, “We’ve implemented robust analytics,” not “We’re working on ETL.” ETL doesn’t sound exciting. A common component of implementing a data management architecture is ETL, or “extract, transform, load” logic. However, you can’t implement analytics without a solid data strategy. Whether you’re the CFO, head of operations, or marketing lead, you want to use your data to make better, more insightful business decisions. You want faster reporting, easy-to-use dashboards, and quick and accurate answers to your questions. As a business leader, you want to get to impactful analytics ASAP.
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