To quickly analyze data, it’s not enough to have all your data sources sitting in a cloud data warehouse. You need to get that data ready for analysis. There are a few ways to go about this and the lines have blurred on terminology and processes used to get analytics-ready data.
You may be familiar with the term data preparation — the act of manipulating raw data into a form that can readily and accurately be analyzed. This seems awfully similar to data transformation, the process that joins together siloed data from different sources; adds business logic; and embellishes metrics to take raw “captured” data and turn it into something useful.
Enterprises spend too much time and resources on data prep
Data preparation workloads can be done with an ETL or ELT solution if the use case fits the business. For instance, when the situation calls for billions of rows of data to be loaded into a data warehouse and transformed automatically, an ETL or ELT solution is your best bet for efficient data preparation. But data professionals find data preparation time-consuming: 45 percent of time spent on data analytics projects is dedicated to data preparation instead of more strategic tasks.
On average, it takes data professionals a week to aggregate and prep data so that it is useful for analysis. Without cloud-native solutions or orchestration workflows, ETL, ELT, and data preparation workloads require too much time and effort for data teams. Engineering resources are wasting valuable time that could be better spent innovating with data.
Preparing data for machine learning with ELT
Cloud data transformation solutions are built to use the performance and power of the cloud to ingest and transform large datasets of various formats to get data ready for analytics. These solutions have intuitive and automated data preparation processes that eliminate the need for hand-coding. This enables businesses to transform data for advanced use cases such as machine learning.
For example, our customer Aramex, a global logistics company, made 100 million deliveries to 37 million unique customers across 72 countries needs to use massive amounts of data, from a variety of sources, to fuel its daily operations. Using data transformation inside its cloud data warehouse helped the data team join together disparate data and improve business processes. The company deployed 200 machine learning models in a single year, used to make 500,000 predictions every day. This significantly increased the efficiency of its delivery operations and helped the customer service team reduce inbound call center inquiries by 40 percent.
Uberflip reduced data preparation time from five weeks to only one day
By implementing cloud-native ETL, Uberflip reduced data preparation time from five weeks to just one day helping their product, marketing, and sales teams to rapidly deliver better business value for customers. Matillion ETL delivered repeatable and scalable processes and models for data orchestration and decreased required development time, freeing up valuable engineering resources.
Learn how they made it happen with this webinar on-demand: