Moving your data to the cloud is all well and good, but what comes after? Ed Thompson, CTO and co-founder of Matillion, explains what you should do next after moving to the cloud.
Enterprises that want to make decisions quicker are utilising cloud computing to store high-quality data and unlock hidden insights using the processing power of a cloud data warehouse. Using the performance engine of the cloud, companies can combine data sources to transform many raw silos of data into actionable insights. According to an IDG survey, speed is the main driver for companies to move their data into the cloud. However, moving data to the cloud is only half the journey.
Once your data is in the cloud, you need to think about two things:
How should I store my data?
When you begin to migrate data into the cloud, you can choose between putting it into a cloud data warehouse (CDW) or data lake, depending on your business needs. CDWs are built to work with incredibly large volumes of structured and semi-structured data; storage options scale along with a business’s complex data demands, all while simplifying the management of your storage layer. Well-known CDWs have different approaches to data storage. For instance, Snowflake has taken a unique approach by natively separating storage and compute, so that each can be managed independent of the other. Amazon Redshift Spectrum and External Data Sources for BigQuery add similar functionality to your cloud data warehouse, as an optional feature, giving you the flexibility to mix and match internal and external storage as you see fit.
Use your cloud data management strategy to guide your next steps as a CDW may warrant new design choices. For example, you might want to take advantage of columnar storage and bulk data loading. These subtle differences will impact how table structures are designed, loaded, and queried, which may require a learning curve for your team.
This is not to say that all data should be put into the cloud at once. To familiarise yourself with the new features of the CDW, you can load data incrementally and establish a process that works best for you. Take the opportunity to explore new design patterns, to clean up technical debt, or both.
How will I use it for my specific business case?
There are a number of ways to use the data inside of the cloud to quickly solve business challenges with accurate, transformed data. Normally, businesses have no issues collecting data, as it comes in large varieties and velocities inside every department. The struggle lies in the organisation’s ability to join together siloed data from different sources; add business logic, use metrics to take out raw ‘captured’ data and turn it into something useful. Simply ingesting data into a cloud data warehouse does not necessarily make that data useful or usable.
In order to provide the business with insights, data must be transformed from a raw, normalised state to data that is denormalised and ready for analysis. Transforming data is challenging but once it’s done, companies can benefit from an actual data warehouse model running on the cloud data warehouse engine.
Connect, normalise and transform data with cloud-native tools
Now that you’ve decided how to store your data and how you’d like to use it, you need to get your data into the right format. A cloud-based ETL tool can do everything that a traditional on-premise ETL tool does – and then some. In order to be valuable, the cloud ETL solution needs to take all kinds of data – structured, semi-structured, cloud or on-premise – from all kinds of data sources and join them together.
This can be achieved with a tool that uses the ELT (extract, load, and transform) approach, allowing data professionals to work directly and natively on data inside the warehouse. This approach offers some very significant benefits, you have all the data in one place for greater accuracy and higher fidelity. Pushing workloads down the cloud data warehouse means at development time you get faster productivity and when processes move to production you get increased scalability.
Get your team up to speed in the cloud
Some of the data professionals within your business may not have worked with a cloud-based ETL solution before. Using cloud-native tools helps your business move faster and affords more scalability to your data infrastructure. Modern technologies like most cloud-born products will use a graphic user interface and simple drag and drop features that allow for easy adoption and guide the user to build complex data transformations from simple, granular, easy to understand steps.
To help your team get up to speed with these new solutions, work on small projects to get comfortable using new tools. You can run a proof of concept (PoC) to prove out to test software options and see results within a few days.
Centralised data in the cloud can help you achieve more
Now that you have transformed data and a team running with cloud software, you can begin to expand the use cases inside your organisation. The first step in becoming a data-driven enterprise is ensuring that data is centralised and accessible in your business. Your cloud data warehouse and ETL solution can provide a single source of truth, with employees using transformed data to perform analytics, view multiple data sources, and run orchestration jobs.
With all of the data centralised in one place, you can explore new avenues of opportunities for your business like:
- Advanced analytics technology – Using data ingestion tools to load and centralise data, you can perform big data discovery, behavioral analytics, self-service data preparation, graph analytics, or web analytics, based on the needs of the business and advanced use cases like augmented analytics for data preparation, data discovery, and data science.
- Improved business programs and processes – Once access to new datasets is shared throughout the business, data-inclined business professionals can use this new information to test and challenge the old way of business. This can balance prioritisation of projects for business needs and ROI, help create more efficient day-to-day business operations and use customer data to create new policy or pricing.
- Better customer experiences – Empowering business users with more data on the customer experience can bring new perspectives into the fold and allow for opportunities for improvement to surface. If those insights are centralised and accessible to marketing, customer success, and finance, companies can easily detect early warning signs that could impact revenue and determine new ways to help customers and create new products based on customer insights.
The advantages of a modern data management strategy in the cloud are clear. And what you can do with the flexibility and scalability enabled by cloud-based solutions will help you not only make better business decisions, but also create a data-driven culture that can put you ahead of your competition. Only the cloud, and cloud-native tools, together with effective data transformation, provide the power, performance and efficiency needed to solve business challenges.