Data is an incredibly valuable asset for businesses of all shapes and sizes. And, with technology powering everything from sales to operational workflows, the amount of data generated by even small to midsize companies is substantial.
What’s the best way to collect, analyze, and use all of this data?
Data warehouses have emerged as a viable option. This article explains what a data warehouse is, how to decide if you need one, and considerations for making an informed buying decision.
What is a Data Warehouse?
The term “data warehouse” refers to a type of database that is used for analytical purposes. Data warehouses are designed to aggregate massive amounts of data, handle complex queries, and support numerous data-driven use cases for an organization. When used in tandem with a modern business intelligence (BI) tool, a data warehouse can help stakeholders across an organization answer important questions pertaining to sales, marketing, operations, finance, supply chain, compliance, and other key functions.
Data warehouses differ from transactional databases (i.e. “traditional” databases) in how they store data. Imagine a very large spreadsheet containing multiple rows and columns. Transactional databases are configured in a similar way—each row represents one transaction and each column represents a relevant piece of information about the transaction. Empty columns still take up disk space, and therein lies a major challenge of running analytics on a transactional database. It’s possible but certainly not ideal from a performance standpoint. Without getting too technical, data warehouses solve this challenge by storing data differently, enabling you to bypass the storage of non-existent data. In addition, columns in a data warehouse tend to store similar types of information, thereby simplifying compression and delivering additional efficiency.
Data Warehouse use cases & benefits
Successfully implementing a data warehouse can alleviate many technical headaches for data teams looking to support the following use cases:
BI & Reporting: Enterprise reporting is incredibly time-consuming and tedious when data is not centralized. Copying and pasting from spreadsheets requires manual effort and opens the door to unreliable data. Bringing together an organization’s data in a data warehouse makes it easier to govern data and utilize it for BI and reporting.
Machine Learning & Artificial Intelligence: Machine learning engineers and data scientists should spend most of their time on data modeling and experimentation, not looking for data. Storing, verifying, monitoring, and observing data in a data warehouse can simplify life for your ML and AI experts.
In addition to providing a solid technical foundation for BI and ML/AI initiatives, a data warehouse can yield numerous other business benefits. At Proxet, we’ve witnessed firsthand how data warehouse technology can deliver tangible value for our clients. Examples include:
Gaining Faster Answers To Difficult Questions
Comparing last month’s sales revenue to the previous month, quarter, or year should not involve hours of waiting for reports to load. Data warehouses help enable faster load times, so business users can get the answers they need with minimal waiting and frustration. And, as cloud-based data warehouse vendors continue advancing their capabilities, the time to insight—even on previously unanswerable questions—will keep getting better.
Outsourcing Of Infrastructure & Support
The cloud has transformed virtually every aspect of modern business, and data warehousing is no exception. Data warehouse vendors are leveraging cloud technology to achieve amazing things, especially when it comes to data storage and compute. That’s good news for an organization thinking about implementing a data warehouse. Large, in-house engineering teams and upfront infrastructure purchases are no longer necessary to gain value from a data warehouse. That being said, configuring and supporting even the most intuitive data warehouse still requires some level of technical expertise.
Centralizing Data & Reducing Data Silos
Purely from a risk management and data governance standpoint, managing one source of truth for data seems like the ideal. Storing data in a data warehouse aligns with this vision and makes it easier to monitor, observe, and track data.
Deciding if your organization needs a Data Warehouse
Does your organization actually need a data warehouse? The answer depends on a number of factors, including your existing technology, technical capabilities, and data requirements. At Proxet, we believe in taking a gradual approach and avoid pushing clients to adopt unneeded technologies. Here are three things to look at when evaluating your own needs:
- Your Existing Technology: How does your company currently power its reports and dashboards? If you’re mostly reliant on spreadsheets and presentation decks, then a data warehouse might be a future-state goal rather than an immediate priority.
- Your Technical Capabilities: Do you have the right in-house team to take on a data warehouse project? Although the cloud has made data warehouses easier to implement and support, you’ll still need a competent technical team to ensure a smooth transition, build the right processes, and oversee supporting technology. If in-house resources do not exist, contracting with a data engineering company could be wise.
- Your Data Requirements: Last, but certainly not least, the decision to implement a data warehouse should go back to your organization’s data requirements. Are business users already able to get necessary insights but simply need faster load times on reports? If so, optimizing your existing database might be more beneficial than tackling a data warehouse project.
Planning the right team, processes, and tools
Let’s assume that after reviewing your tech stack, capabilities, and data requirements, it’s clear that implementing a data warehouse is the best path forward. What next?
For starters, you need a skilled data team that consists of experienced technical staff (preferably data engineers) who can own the project, guide technology decisions, and provide ongoing support before, during, and after go-live. A big part of the team’s ongoing responsibilities will involve overseeing processes that ensure data governance, data quality and reliability, data security and compliance, and other critical considerations.
To maximize the value of your data warehouse, you may need to surround it with additional third-party tools that streamline:
- Ingestion – getting data into your data warehouse
- Transformation – building models that transform raw data into something usable
- Monitoring – ensuring data is correct, clean, and verified
- Governance – controlling who has access to what
- Business intelligence – visualizing your data as charts, dashboards, and reports
Evaluating Data Warehouse solutions
Which data warehouse vendor is the best fit for your organization’s needs? Again, it depends on a variety of factors that are specific to your unique situation, including:
- Existing infrastructure
- Data requirements
- Size of your data
- Cloud provider
- Governance requirements
At Proxet, we do not typically recommend on-prem data warehouses to clients. Instead, we prefer cloud solutions like Snowflake, which offers a scalable, modern architecture and solid governance model. Databricks is another popular option that we’ve worked with. Leading cloud providers (Amazon, Google, and Microsoft) also offer their own data warehouse solutions, which are Amazon Redshift, BigQuery, and Azure Synapse Analytics respectively.
Your next move
At the end of the day, the decision to implement a data warehouse depends on one big factor: your company’s actual needs. Start a conversation with your technical team to begin assessing your needs and exploring next steps. If you get stuck at any point along the way, feel free to drop us a note.
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