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Modern business intelligence (BI) relies on data warehouses as the foundation for effective data analysis and decision-making. A data warehouse serves as a centralized system where vast amounts of structured data are collected, stored, and optimized for querying, enabling businesses to extract valuable insights efficiently. Without this structured approach to data management, organizations would struggle to analyze historical trends, track performance, and make data-driven decisions at scale. As global data generation accelerates—expected to reach 175 zettabytes by 2025—businesses need robust systems to process and organize this influx of information. Data warehouses not only facilitate the integration of diverse data sources but also ensure that information is accessible, accurate, and ready for strategic use. By powering BI tools and dashboards, data warehouses transform raw data into actionable intelligence, helping businesses stay competitive in an increasingly data-driven world.
A data warehouse is a centralized repository designed to store, manage, and analyze large volumes of structured data from multiple sources. It enables organizations to consolidate historical and real-time data, providing a unified view for reporting and business intelligence. Unlike traditional databases, which handle day-to-day transactions, a data warehouse is optimized for analytical queries, supporting complex data processing, trend analysis, and decision-making. It operates through ETL (Extract, Transform, Load) processes that integrate data from various systems, ensuring consistency and accuracy. By organizing information in a structured format, data warehouses empower businesses to gain insights, improve forecasting, and drive strategic decisions.
Many companies could reasonably benefit from adopting a data warehouse.
That is, if it were free.
Historically, a data warehouse was the only option for businesses looking to implement any business intelligence—which is why only enterprise businesses could really accomplish it. Affordability has always been the main barrier to entry.
The building process alone costs an average of $500,000 in startup costs. And once that’s done, you still have to set up a team of dedicated analysts and IT experts to manage it perpetually.
Here are Cooldata’s cost estimates for even a mid-size warehouse:
Data warehouses are incredibly useful, but they’re also incredibly complex. To anyone other than a well-versed data analyst, the slow, inner machinations of your data warehouse are hard to understand. What isn’t hard to understand is that there’s always going to be a wait period for a newly-requested report.
Slow-pending data warehouse requests are not uncommon. There are a few reasons for this delay: the number of data sources you have, your ETL process, the computing ability of your data warehouse, and the size of the request are just a few.
Your data resides in locations across various systems across each team and department in your organization. A data warehouse typically connects to four types of data sources:
Each of your data sources presents raw data in different formats. At this point, this data will need to be extracted from your data sources, transformed, and loaded into your data warehouse before it can be viewed comprehensively.
After your data is extracted from your data sources and before it’s loaded into your data warehouse, it’s sent to a staging environment. In staging, your data is held and prepared for storing and consumption. It will be checked for any issues that affect data quality, including incomplete data, missing fields, or duplicates.
Once any issues are identified and fixed, your data can be cleaned and transformed to ensure it’s formatted correctly for storage and further analysis.
New and historic data will be stored and arranged into various groups and dimensions that make up the schemas used to pull the most frequent and standard reports your organization needs.
This is the point, now that the data has been carefully prepared when your analyst begins to generate the reports that you will view. They could do this directly, writing and executing queries that the data warehouse processes, but more often than not this is handled through some kind of business intelligence software.
The business intelligence stage is where you, the business user, are finally able to view your data.
Data warehouses and business intelligence (BI) platforms work together to significantly speed up report requests by organizing and centralizing vast amounts of data in a structured, query-optimized format. Instead of pulling information from multiple disconnected sources, a data warehouse stores cleaned, transformed data in one place—ready for analysis. BI tools then access this centralized data to generate BI reports quickly, often with the help of pre-built dashboards and real-time data visualization. This streamlined process eliminates manual data gathering, reduces wait times, and enables faster, more informed decision-making across the organization.
This is all to say that while a data warehouse plays a major role in your business intelligence strategy, it’s not an end-to-end solution. In order for a data warehouse to work, you need to coordinate additional ETL, analytics, and visualization software. The benefit of a data warehouse is the availability of in-depth insights supported by years of data. The downside is time.
So, what’s the alternative? While there isn’t a self-service tool that can completely replace the capability of a data warehouse, there are end-to-end business intelligence systems that do provide—to a certain extent—the data management, storage, visualization, and analytics capability of a custom technology stack.
As you already know, data warehouses are complex systems that pull data from a variety of sources to be housed and analyzed in one central location. These analyses are then used to fuel the majority of high-impact business decisions. However, while data warehouse tools are considered to be a core component of business intelligence for many companies, they also come at a steep cost, both in price and valuable resources.
As recently as ten years ago, data warehouses weren’t simply the best option for data collection and analysis—they were the only option. Luckily that’s not the case anymore. In many instances, business intelligence platforms are equipped with enough features to meet (and even surpass) the majority of your growing company’s data needs.
In fact, depending on the stage of your company, you may have the option to forgo a traditional data warehouse altogether in favor of BI tools, helping you to save both money and resources.
Simply put, a robust, end-to-end, BI platform can provide an effective solution for data management while you’re still growing.
It’s pretty easy to figure out if you need actually need data warehouse, or if a BI platform will function as an adequate solution.
To determine this, the first thing you’ll need to do is measure your company’s stage of growth, to help growing businesses answer this question, Grow’s BI customer success managers developed a unique data maturity model.
The Grow Maturity Model is designed to help you track your data maturity through six stages: Pre-Data, Data Familiar, Early, Moderate, Advanced, and Expert.
Below is a brief overview of each of these stages, to help you gain a better understanding of your current position.
Ask yourself, which stage sounds the most like your current situation? If your answer is anywhere from pre-data to moderate, you likely don’t need a data warehouse at this point. And that’s not necessarily a bad thing. Thoughtful handling of your data can help you progress your maturity, but in the meantime, you should be able to effectively use BI for your data needs.
If you’re going to tackle a custom data warehouse, it’s just going to be a single layer in your business intelligence technology stack. You’re also going to need an ETL system and a system for data analysis (at the minimum).
The chief challenge in setting up a workable data warehouse is ensuring that all the systems and processes that the data warehouse depends on are set up and managed correctly. You have two options when it comes to setting up your data warehouse. You can either host it on your own servers, or you can host it through a cloud service.
Some businesses prefer to host everything on their own servers for a few reasons:
That being said, maintaining your custom data warehouse on your own servers means that you are fully responsible for maintenance and optimization—there is no other line of support to fall back on (self-hosted business intelligence solutions are also called on-premise business intelligence).
This is one of the most popular choices for setting up a custom data warehouse, especially now when there are more cloud-hosting services than there were even a few years ago. Essentially, you pay on a subscription basis to rent server space. You can keep costs down, and rely on the support of the cloud service to protect your data warehouse.
Examples of these services include Amazon Redshift and Snowflake.
In either case, in a custom data warehouse-based approach you will face significant hardware, software, and human resource costs. At minimum, you should expect the building process alone to take you six months. This doesn’t include the time you’ll have to spend planning and designing your data warehouse, or the time allotted to training your team to use and maintain a custom data warehouse.
The design and development of your data warehouse are critical to its success, and more than 60% of data warehouse projects ultimately fail due to poor design.
Whether you build your entire data warehouse using in-house talent, or bring in expert consultants—this is recommended due simply to the depth of your investment in the data warehouse’s success—you will need to ultimately staff at least four ongoing positions:
Each staffing position will require its own management processes, although there can be some crossover. For example, data analysts work in the ETL layer cleaning and prepping data, as well as in the dashboard layer building data visualizations.
But one of the most significant differences between staffing your team for an end-to-end business intelligence system and for a custom data warehouse is the role of IT. Generally speaking, your IT team will need to support your data warehouse at each layer.
Generally, an end-to-end business intelligence system is faster to set up because it:
The trade-off is the loss of customization capability. While you have total control over your entire business intelligence flow with a custom build, you’re going to be sacrificing some of that with an end-to-end system.
(Fortunately, unless you’re a huge, enterprise-level business this isn’t going to be an issue).
The short answer? Absolutely.
However, if your company is completely dependent on data for both macro and micro-decision-making, a data warehouse may still be your best bet.
If you’re a data newbie, or a moderately data mature company, business intelligence applications could be an ideal fit.
Of course, all companies should be aware of their basic data needs: to access their multiple data sources, extract, load and transform their data, and then see their data. However, there’s no reason to over-exert your budget by paying for services you don’t actually need.
Data warehouses are commonly used primarily for combining data from one or more sources, reducing load on operational systems, tracking historical changes in data and providing a single source of truth.
Today’s BI applications can provide solutions for almost all of those uses by:
Sounds pretty comprehensive, right? The truth is that although BI may not cater to every data scenario, it can certainly meet the needs of most growing companies.
There was a time when the concepts of BI and data warehouse were nearly synonymous. However, end-to-end BI platforms have changed this. And, in some cases, eliminated the need for a traditional data warehouse altogether.
A data warehouse functions as a single layer in your BI stack. Data is processed by an ETL, then stored in your data warehouse, and finally organized and visualized in your BI layer.
This method is perfect for storing petabytes of data and handling massive transforms or querying years of historical data. But from the standpoint of a single business user, it limits your control.
Working with a data warehouse is a less user-friendly method for managing your data. If you’ve been working directly with your data sources in the past and are familiar with the way they report, a data warehouse creates a kind of smokescreen between your raw data and data that’s undergone ETL (Extract, Transform, Load) in your warehouse.
Because of this, you can only execute very specific types of analysis. If you need to ask new questions or process new types of data, it can take significant time to update your data warehouse and make new queries or data visualizations possible.
With an end-to-end tool, there’s no divide imposed between your raw data and normalized data by a data warehouse. You can perform ETL tasks and visualize data all in one platform, allowing you to pivot quickly to create new datasets if needs be.
And instead of having to use a coding language to access your data, end-to-end BI solutions often have more user-friendly point-and-click tools, allowing even less technical roles on your team to work with the data they need to better inform their strategies.
In any system, whether a data warehouse or an end-to-end BI solution, you should have an experienced data analyst managing transforms, creating datasets, building metrics, and gaining insights.
Your data analyst shouldn’t be responsible for coding your platform to give you access to dashboards—you should have the ability.
BI solutions that require a data warehouse are much more complex. They’re intended to handle a large amount of data in a variety of formats, and inform complicated queries in a custom BI visualization tool. Navigating a custom system will require some level of coding, which creates a technical expertise barrier. When non-technical business users need to query or access the data, they’ll need to request reports through your analysts.
You don’t want to have to rely on your data analyst to connect systems or design custom data visualizations or dashboards—you want a system that makes it easy for you to do those things directly. You want to spend your time actually looking at the data instead of trying to figure out how to access it and present it.
In terms of access to real-time data, an end-to-end BI system is also more friendly than a system that depends on a data warehouse. You don’t have to wait for data to go through the pipeline—by connecting your data sources directly to your BI solution your visualizations will be updated as soon as your data sources are.
The last thing you want is for the system that’s supposed to make your job easier to, instead, introduce a new level of dependency.
With a BI solution, you can set up a system that can allow employees to access and utilize the data without jeopardizing your analyst’s or your IT team’s time.
An end-to-end BI platform allows you to connect all your structured and unstructured data sources, transform your data, create reports and visualizations and share data across your teams from a single point without requiring extensive coding or technical skills.
But how do you centralize, store, analyze, and visualize the enormous amount of data your organization is collecting?
Generally, there are two options:
Let’s clarify this up front: neither option is inherently bad (although one will take a lot more time than the other). For either option, there is always a use-case that makes sense. So which one should you choose?
Here’s a few questions that can help you decide:
The simple truth is that while both data warehouses and end-to-end business intelligence systems can be effective ways to wrangle all of your company’s data, each option has different initial setup and configuration processes.
