Top 5 Misconceptions of Data Warehousing Projects
Data warehouses are instrumental in breaking down data silos—serving as a complete collection of a company’s data from across multiple sources. But how do you know when it’s time to invest in one?
I recently had the pleasure of contributing a chapter to the ebook The Essential Guide to Revenue Operations, released by our friends at CloudKettle. Give it a read if you’re interested in the ins and outs of when an organization is ready to adopt their own warehouse, as well as some of the common misconceptions marketing leaders have around the implementation of this type of project.
Data Warehousing for Revenue Intelligence
So, how do you know you’re ready to invest in a data warehouse? Here are two tell-tale signs:
It takes weeks to get answers to simple business questions. Best-in-class companies can answer these questions in days, if not hours. If you’re operating in weeks or months, you’ve got a problem.
Your analysts struggle to understand and identify basic trends in marketing and sales performance. This is very important for companies in a growth phase. Many companies rely on a superficial understanding of business performance. In order to be able to diagnose problems and/or opportunities, you have to be able to go deeper.
Sound like your organization? You’re not alone. But before you embark on implementation, be sure to manage your team’s expectations by keeping these top five misconceptions in mind.
Top 5 Misconceptions
1) A Data Scientist will Solve all my Data Problems.
There has been a lot of hype around data science and machine learning lately, however, we believe that a data scientist is not the best hire to build or maintain a data warehouse.
Rather, a more effective first hire to address your data problems is a Data Engineer. A good Data Engineer will provide clean data schemas that can be reused for reporting and leveraged to help push business objectives forward.
2) The Engineering Team Can Build a Data Warehouse as a Side Project.
Don’t make the mistake of tasking your product or engineering team with building a data warehouse as a side project. Even if someone on the engineering team is able to build it, consider whether or not they’ll also be able to maintain and upgrade the data warehouse over time.
For most organizations, a successful data warehouse implementation is largely dependent on having a dedicated resource.
3) Something is Better Than Nothing.
A mistake fast-growing organizations often make is only partially investing in building a data warehouse. In some cases, it gets deprioritized or the resources get re-allocated to save time or money and they justify it by saying, “something is good enough.”
In reality, the opposite is often true. Incomplete or inaccurate data leads to making poor decisions and restarting or rework will cost the organization more in the long run.
4) Data Warehousing is a Sexy Project.
A data warehouse isn’t a product that sees the light of day.
It can be difficult to find technical hires who are passionate about building an internal (and essentially invisible) tool. But finding the right person is paramount to the project’s success—you want someone who understands the business use case of the project, as well as being able to execute on the technical requirements.
5) It’s Fast & Easy.
When done correctly, establishing a data warehouse can be a lengthy process. This is especially true if a company has a lot of technical debt, like legacy code or faulty work-arounds, which most companies do. In our experience, it takes at least six months to a year to build a strong, reliable foundation and many months of tweaking the reporting requirements for key stakeholders in the organization.
This is a condensed version of the original post. Head over to CloudKettle’s article for a detailed breakdown of the top five misconceptions of data warehousing and how you can lay the proper foundation for data architecture in your organization.
Blog post by Saadat Qadri, analytics practice lead at Outshine.