It’s hard to believe that enterprises are still struggling to make use of the vast amount of data within their organization but for most, accessing and analyzing data remains an elusive goal. The cloud and cloud data warehouses can help them centralize the information they need from across their organization to perform analytics, forecasting, predictive modeling, machine learning, and other advanced use cases that will get them the insights they need. The cloud is a scalable, high-performance platform that can help organizations achieve faster time to insights. But simply moving data into the cloud alone won’t actually make it actionable.

Get Started with the Cloud for Better Insights

Veronica Kupetz
Veronica Kupetz is a Solution Architect at Matillion, a leading provider of data transformation for cloud data warehouses. She is a database professional with 10 years of experience working with MySQL, PostgreSQL and Microsoft SQL databases. Her expertise includes customer service, data management and project/event creation and implementation.

It’s important to understand that where your data resides does not address how it is used. For example, 90% of data professionals say it is challenging to make data available in a format usable for analytics. When you choose a cloud data warehouse to store your data, you’ll need to migrate those data sources first. After that, you will need to figure out how to use that data for analytics and reporting to derive valuable insight from it.

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To build a data analytics platform in the cloud, you need to first design and set up the data infrastructure and complementary cloud-based solutions to make it happen.

Here are a few general do’s and don’ts when building out a cloud data analytics platform.

DO View CDWs and Data Lakes as Combined Data Stores

The line between a cloud data warehouse and a data lake is beginning to blur as these two technologies appeal to data professionals looking to store data in a central location. A data lake is not a direct replacement for a cloud data warehouse. They are supplemental technologies that serve different use cases with some similar capabilities. Most organizations that have a data lake will also have a data warehouse.

Some cloud service providers combine data lake and data warehouse technologies into one platform to support better analytics. Whether you want to call it a data warehouse or a data lake matters less: The data is centralized in a form that is useful, and that’s the key. Breaking down data silos helps you access the data you collect and keep that data synchronized and up to date.

DON’T Skip Data Transformation

Simply loading data is not enough to get the insights you need for analytics and reporting. Data transformation, the joining together and embellishment of data from different sources, produces analytics-ready data by taking it from a raw, normalized state to data that is denormalized. Traditional ETL (extract-transform-load) processes and manual coding, along with failure to plan and test data before running an ETL job, can introduce errors such as duplicates, missing data, and other issues. A modern ETL or ELT (extract-load-transform) tool can reduce the need for hand-coding and help cut down on errors. Data transformation plays an integral role in using data for advanced use cases and reporting. Data transformation solutions not only load data into the cloud data warehouse but also manipulate it into the format required by analytic and business intelligence software.

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DON’T Migrate All of Your Data at Once

It can be tempting to extract and load all of your data into the cloud data storage platform of your choice. But instead, identify a small use case with clear metrics to familiarize yourself with cloud data management. To gain support for cloud data initiatives, you need to quickly prove business value to stakeholders. Start with an analytics use case then figure out the metrics or KPIs you need and the data that will yield that insight. Now you can load the necessary data into the cloud to transform it for analysis. Better yet, choose data from sources that have well-defined KPIs, like click-through rate to purchases made on an eCommerce website. In this instance, you can provide data from both the email marketing system and the eCommerce platform to your cloud ETL solution and show how quickly you can report those numbers. The next steps might be to extract that campaign data — bounce rates, open rates, clickthroughs, etc. — one data point at a time.

DO Think Long-Term When Buying Software

The cloud provides flexibility and scalability for data management and the software you choose should leverage both of those attributes. Choose solutions that can grow as your business does, not ones that you will outgrow after only a few projects. Determine if your vendors can grow with you, throughout the cloud data journey, as you begin to progress from small projects to complex use cases. This will help you become comfortable with the solutions you use. When you are ready for more robust features, you’ll be working within a tool that you trust, without having to start from scratch.

DON’T Forget Your Security Requirements

Security in the cloud is a continually growing need for all organizations. As always, make sure that the data analytics platform and architecture you build meets the security requirements of your business. When you select a cloud service model, you need to understand the security responsibility that the model entails and where the responsibility lies for certain deployment models.

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Assess Your Cloud Data Maturity

Cloud data transformation will help you unlock the insights in your organization to make data-driven decisions quickly. But first, you’ll need to assess how proficient your organization is with data efforts in the cloud. Are you and your team ready to set up a scalable infrastructure and build a future-proof platform for analytics?

If you are unsure, take this cloud data maturity assessment to help you understand where you are on your cloud data journey and how to move toward mastering cloud data management.

Feature image via Pixabay.