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Cloud data platforms make businesses more scalable and flexible and more cost-efficient
The need for actionable data is nothing new. Enterprises spend time, money and resources working to get their data into a format that is usable for analytics. But most are still struggling to do just that. More than 90% of enterprises say that making data available for insights is a key challenge in their analytics programs. The journey to modernizing your data infrastructure is challenging, especially if performing data transformation quickly is holding back your business.
To compete effectively with data, you need “analytics-ready” data. This requires scalable data transformation—the ability to join together siloed data, denormalize, enrich and apply business logic. This process is imperative for getting to the insights that will help you solve business challenges at pace. So how much can data transformation benefit your business? It can rapidly improve your data preparation process, consolidate your data sources, save money, make accurate forecasts and predictions and most importantly, provide you with the insights you need for business growth.
Cloud-native Solutions and the Cloud Data Warehouse
A modern data infrastructure works best with cloud technology. Legacy data warehouses hold businesses back from scaling alongside data volumes and bringing data together into a single view. Cloud data warehouses help companies move quickly, and cloud-native solutions use the economics and power of the cloud to handle big and varied data volumes. This is especially helpful with a cloud ELT (extract, load, transform) solution. Traditionally ETL (extract, transform, load) is used to perform data transformations. But the modern ELT approach allows you to move data directly into your cloud data warehouse and then perform the necessary transformation. When enterprises employ a modern data environment and cloud-native tools, it is easier to proactively use data to glean insights—and save on provisioning costs.
Take, for instance, a large cloud security organization that needed a centralized data repository that was modeled to handle diverse data sources and increasing data storage and reporting needs. This company moved off of its legacy system onto a cloud data platform with a cloud data transformation tool, allowing them to reduce application spends by 84%. Now with a single view of data, they can easily run unified reports to analyze data quickly, opening up greater possibilities to extract insights.
Preparing and Transforming Data Quickly for Faster Time to Insights
The act of wrangling data is time-consuming. In a recent survey by IDG, nearly half of data professionals reported that it takes more than a week to prepare data for analytics. ETL, ELT and data preparation workloads are bogging down precious data engineering resources and wasting valuable time that could be better spent innovating with data. Data preparation is a challenge for organizations because the work is code-heavy and manual. Cloud-native ELT solutions can reduce the burden of data wrangling and provide a much faster, low-code process to prep data.
Data transformation tools that take advantage of the power of the cloud, have intuitive and automated data preparation processes that eliminate the need for coding.
This enables businesses to transform data for advanced use cases such as machine learning. For example, a global logistics company that 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 their cloud data warehouse helped them to 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%.
High-quality, Accurate, Timely Data Makes the Difference
New data sources are proving beneficial—and challenging—for businesses to integrate into their organization. As the pace of data increases, the need for recent and accurate data rises along with it. An analytics platform built in the cloud can provide a much-needed pipeline of high-quality data that can be used in a timely manner, with insights provided quickly.
As another example, a software company that wants to improve its data-driven culture by relying on fast data integrations using a cloud analytics platform to ingest information. Instead of analyzing data that was days old, the software company used a cloud data transformation tool to load data in real-time from all external sources in the business. By taking advantage of reusable components built in the cloud, the team reduced their data ingestion process from as many as 10 workflows down to just one. This allowed them to reduce their data warehouse and ETL administration, freeing up developer resources to innovate within the business.
Scaling Resources and Saving Costs With a Cloud Data Platform
The results of cloud data transformation speak for themselves, as enterprises from different industries see accelerated time to value, scalability and cost savings that validate their investment in modern solutions.
Data transformation, performed in the cloud, can help you rapidly improve data management and existing processes. Analytics-ready data can help you solve critical business problems and ultimately generate revenue. As external factors contribute to pressure on budgets, resources and road maps, transforming data with an ELT solution will help reduce development time and accelerate innovation with new insights. Cloud ELT and data platforms make businesses more scalable and flexible, cost-efficient and better-prepared to leverage data in advanced use cases involving IoT data, machine learning and artificial intelligence that can predict what comes next and what to do about it.
