Top 5 reasons most data warehouse projects fail

Top 5 reasons most data warehouse projects fail
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The Hidden Costs of Bulk Data Licensing

Data is like oxygen for a SaaS company–especially in the real estate industry. Proptech companies have become experts at using property data to design customer journeys that are smooth as glass. Home services companies use the data to examine the age and stage of a house to figure out which consumers are likely to need their services.

More than 50% of data warehouses fail to make it to user acceptance.
- Gartner Group

A product manager with visions of manipulating housing data in new ways might be tempted to build a data warehouse in order to have all that sweet sweet data on premise. Bringing raw bulk data in-house means you can tinker and experiment with product ideas without restrictions. However, that level of control comes at great cost.

Pursuing a data warehousing strategy is a massive undertaking that involves significant investment in technical expertise and infrastructure. It impacts both the business and tech sides of the organization. And to quote a highly respected data architect with hundreds of DW implementations under his belt, “A data warehouse project has no end date…it is a living thing, requiring ongoing care and feeding…”

This blog will lay out the top four reasons why most data warehouse projects fail. Some of them have to do with cost and complexity. But the #1 reason may surprise you. Keep reading to learn…

#5 - Storage

Building a data lake or data warehouse to store and manage large amounts of data, such as property information, is a complex process. It involves several steps:

  • Data Ingestion: Data must be collected from various sources, cleaned, and loaded into the warehouse. This often requires ETL (Extract, Transform, Load) processes, which can be time-consuming and require specialized tools like Apache Nifi, Informatica, or Talend.
  • Data Storage: Holding large volumes of data requires significant storage capacity. Cloud-based solutions like Amazon S3, Google Cloud Storage, or Azure Blob Storage are often used, but these come with recurring costs based on the amount of data stored.
  • Data Organization: Data must be organized in a way that it can be easily accessed and analyzed. This often involves creating a schema or structure, and implementing data cataloging tools.

#4 - Maintenance

Keeping the data up-to-date is another challenge. Data pipelines must be created to regularly collect, clean, and load new data into the warehouse. For the uninitiated, a data pipeline is not a physical structure, but a set of processes that move data from one place to another. Practitioners lean on tools like Apache Beam, Airflow and Luigi. These pipelines must be monitored and maintained to ensure they are functioning correctly. Failure to keep pipelines updated can result in stale data creeping into your application, which can throw off calculations that are mission critical.

#3 Optimizing for Performance

Storing and updating data is just one part of the equation. To be useful, data must be retrievable quickly and efficiently. This requires optimization techniques such as indexing, partitioning, and caching. As the volume of data grows, it can strain the system, leading to slower query response times and reduced user satisfaction. Properly scaling and optimizing a data warehouse is a complex task that requires specialized skills. Technologies like Elasticsearch for search optimization, and Redis or Memcached for caching, are often employed. But these tools have a steep learning curve and should be wielded by highly experienced professionals only.

#2 Personnel and Labor Costs

Managing a data warehouse and keeping it up-to-date requires a team of skilled professionals, including data engineers, data scientists, and system administrators. According to Glassdoor, the average salary for a data engineer in the U.S. is around $102,864 per year, and this doesn't include benefits, taxes, and other associated costs.

As important as their level of skill is the degree of domain expertise of the technical team that will be wrangling the data. Without proper context for the end use of the data, the learning curve can be extraordinarily steep. Updates and new requirements to the project will require Herculean effort to translate from the business line making the request  to those who will need to implement the change in the warehouse.

#1 - The “why” isn’t strong enough

Before embarking on building a data warehouse or a data lake, your organization should assess the necessity of such a project in the first place. Questions founders should ask themselves before saddling their organization with such a demanding project:

  • “What is the business outcome we hope to empower with this project?”
  • “Is building and maintaining a data warehouse  the highest and best use of our resources to achieve that outcome?”

If the answer is not clear and compelling, don’t do it.

The fact that peer companies have chosen this path is not a good reason. Tradition is rarely a good reason to adopt a technology. Furthermore, without a strong business case, the project is more likely to lead to wandering scope and unnecessary expense, not to mention frustrated product managers and biz dev team.

The Economical Alternative:

In contrast to the expense and labor-intensive nature of managing a data warehouse, using an API solution like is a more economical and efficient alternative. The Property Search API allows users to perform nuanced searches across 150M+ properties, while the Property Detail API provides detailed physical and financial information about each property. The system handles all the complexities of data management and optimization, delivering data with response times below 400 ms, even for bulk requests.That is enterprise grade performance for a fraction of the cost to build the infrastructure required to deliver such performance.

For a flat fee of $10k per month, users get nearly unlimited access to a vast property database through a powerful and expressive API system.On the surface, this is a savings of over $100k annually ($120k/yr vs $250k/yr) . But a closer look reveals the savings are substantially higher. The typical return on ad spend (ROAS) in the SaaS sector is nearly 3x. So, for every dollar redeployed from data procurement to marketing, it’s reasonable to expect 3 dollars to come back into the business as revenue. The ability to free up cash flow that can be directed to revenue generating activities is perhaps the most powerful argument for opting not to build your own data warehouse.


While bulk data licensing might seem like a good solution for accessing large volumes of property data, the hidden costs and complexities make it a less-than-ideal choice for many businesses. offers a more economical and efficient alternative, handling the complexities of data management and delivering fast, reliable access to a vast property database. Freed from the burden of data wrangling, product managers are able to ship products faster and focus on the features that matter most to end users.