There is no bigger asset to your enterprise than your data. However, when your datasets are poorly managed and kept in silos, it becomes mind-bogglingly complex to squeeze actionable insights out of them. Today, less than 0.5% of data is actually being analyzed and used, and companies lose over $15 million a year because of bad data. To append, scrub, and properly organize big datasets, you need to store them someplace that has the power, scale, and flexibility needed to get the job done – a data warehouse.
A data warehouse is a place that stores historical and current data from an organization’s operational databases as well as third-party sources. Their purpose is to house your data in a structured manner and provide companies with a unified foundation to model their data and generate reports. The analytics stemming from a data warehouse usually offer a more comprehensive view of your business processes rather than a snapshot of the current state of your company.
Why A Data Warehouse Deserves a Place in Your Customer Data Technology Stack
In this modern age, delivering a holistic customer experience requires you to have a customer data technology stack that is intentionally designed to achieve that goal. Generally, the first step is to capture and store the behavioral and transactional data of your customers in different schemas, formats, and tools. Once you have usable data to work with, it must be unified and stored into consistent formats.
After all, systems, formats, and data sources in modern enterprises are disparate, meaning there is a dire need to integrate all that data in a central repository so it can be appropriately analyzed. This is why thinking about the data warehouse you’ll need is a reasonable jumping off point for designing your customer data technology stack. Data warehouses enable you to set up a unified, central repository that contains all of your data in one place for easy and efficient analysis, reporting, and data visualization.
For the purposes of this blog post, the data warehouse technology we’re referring to here is cloud-based relational databases, meaning that they feature a processor component that presents the datasets in relational formats when they’re queried. One thing to note that there are are data lakes like Amazon S3 that are built to aid a similar objective, but house your data in an unstructured manner, which changes the needs of your stack. Also, data lakes require other technologies for support when data needs to be processed or analyzed.
Ideally, enterprises should focus on cloud data warehouses as they’re considered hallmark for building a cloud-native, modern customer data tech stack. These include cloud-based, relational, and columnar store offerings. Smart data warehouses like these let you easily and quickly consolidate data from your cloud applications, databases, and tools into a unified data management repository – without the hassle of data modeling, schema, or configuration.
How to Choose the Right Data Warehouse For You
It is no straightforward task to pinpoint a solution that can outperform your existing platform or other legacy data warehouse offerings based on flexibility, scalability and performance. As you explore which data warehouse option is right for you, there are several questions that we’d recommend you ask yourself:
1. What Is My Preference for A Cloud Provider, Or Am I Provider-Agonistic?
If you prefer Google Cloud Platform, AWS, or Microsoft Azure, know that each platform works best with their native warehouse solutions. Moving data from the cloud solution to the provider’s data warehouse is straightforward and hassle-free if it’s from the same provider, but requires an additional step of using an ETL tool if it’s moving to a separate (external) warehouse. In addition, as Azure, AWS, and GCP data warehouses charge for data egress, there’s a non-trivial expense involved with transferring data from your cloud provider to another provider’s data warehouse.
This consideration won’t have a significant impact on your final decision if you’re provider-agnostic.
2. How Exactly Will I Use My Warehouse?
The different notable data warehouse providers all use different pricing schemes. Some solutions cost per cluster, while others cost per query or separate out storage and compute. If you’ll be storing a lot of data but won’t be querying it often, certain tools will be more affordable for you than if you won’t be storing that much data but query it frequently. Pricing is not standard for data warehouses, so having a good understanding of your usage patterns and likely needs will help you identify which solution will deliver the best cost-to-performance ratio for you.
3. How Technical and Skilled Is My Enterprise Tech Team?
Different tools require or provide different levels of configuration. For companies with smaller engineering teams or fewer technical resources available to them, a provider that handles the majority of the configuration is likely to fit better. For those with a strong technical staff dedicated to the warehouse, a solution that gives you more control over configuration could suit your needs better.
4. Is There A Significant Difference In Performance For My Situation?
Typically, the performance of the primary data warehouse solutions is roughly similar in most instances, although a few providers are better at handling specific workloads (e.g. consistent workloads). Hence, it is worth analyzing your usage needs, as your organization may fall into a bracket where you should make a choice based on the performance differences and certain edge case requirements. Generally, however, we don’t advise on making minor differences in performance the leading factor for your choice of a data warehouse.
Choosing the right data warehouse for your needs is a crucial step in upleveling your data maturity and becoming more data-driven as a business. Ask yourself these questions before going on to pick a data warehouse provider. Generally, cloud-based warehouses will be the most suitable for the majority of enterprises outside of large organizations (and even then), but sometimes the factors of specific unique circumstances or sectors may merit the requirement of a data lake-like option or an on-premise solution for querying and storing your data.
If you’re interested in more, we wrote an ebook that goes into more depth on building your customer data technology stack, including an overview of the notable solutions in each layer. Check out How to Build your Customer Data Technology Stack to learn more.