Every Startup Needs a First Party Data Warehouse
In the Model-as-a-Service AI era there is a single strategic decision that will let your company take full advantage of the rapid advancements in AI: having a first party data warehouse.
Consider two startups:
Company A: Has a first party collected, central repository of cross joined data across all their services
Company B: Relies on third party data collection and reporting through siloed apps
Company A will be able to wield custom tailored insights, recommendations, and warnings from the multitude of AI plugins, packages, and tooling available today. Company B will be left with siloed, third party reporting that misses out on the most important insights and can’t connect the data directly to AI platforms in a meaningful way. Luckily, becoming company A is easier and faster than ever for startups and growth stage companies today.
Data has become the next competitive frontier for every company whether they are aware of it or not. Over the past decade the data landscape has witnessed an explosion in strategies, tools, and techniques. Many growing companies view this space as the arena of larger, established companies who have large data teams filled with expensive data scientists. With the development of the modern data stack the capabilities historically reserved for the fortune 500 are within reach of any organization. It is now possible to have a data warehouse with data from the most critical billing, support, and CRM tools up and accessible to anyone who knows SQL in just a few hours.
Here are the pain points that indicate that a warehouse first data strategy will unlock the next level of growth by improving the customers’ experience and systems agility:
Spending a lot of time generating ARR/Revenue reports with low confidence that it captures an accurate view of the business that is a sign that the company has outgrown automatic reporting and need to start catering automated reports to actual business needs.
The company's CRM holds partial user data for some contacts and customers, and it is unable to track the user journey of anonymous users. This limitation can make it challenging to create accurate and insightful target audiences for communication purposes. Believing that a CRM can serve as a data warehouse is an illusion that can quickly become a hindrance to growth strategies due to space, cost, reporting, and compute restraints that can set the company back by several quarters. The reason for this is that CRM tools are not designed to handle or integrate with the vast amount of event stream data that modern companies generate, which is essential for building precise reports.
If the company is slow at or not able to answer questions using more than one data source including: CRM, product/app usage, advertising/marketing platforms, social media, finance, and accounting. Some of these questions include: Which product usage correlates with the highest LTV customers? What are the demographics of the company's most valuable customers? What product events precede churn or cancellation?
Only a few people in the company can answer questions about customers with data and the rest are relying on experience, intuition, or siloed third party apps. This leads to missed opportunities that hurt the bottom line through inaccurate marketing, sales, and customer experiences.
The team spends important hours formatting Google Sheets that are widely debated and questioned. Not only does this take up valuable time and resources, but it can also increase the risk of errors and inaccuracies in the reports. A data warehouse alleviates this burden by automating and streamlining the reporting process so fixes occur once and the source of truth is trusted and shared by everyone.
Reliance on Third Party Attribution: If the marketing spend is being directed by the reports that the advertising platforms themselves provide then the company is wasting valuable spend and marketing allocation without knowing which campaigns are driving the most top/bottom of the funnel impact. Ad platforms will all claim credit for the same users who pass through their systems so the company will be spending multiple times to acquire the same customers without knowing it.
If every meeting presents new conflicting data from the last one it is likely time to invest in centralizing the data and codifying metric definitions so the team can spend less time scrutinizing and more time building with confidence.
Modern data warehouses and a warehouse first strategy makes it quick to setup and maintain a central data repository to unlock advanced analytics and AI tooling rapidly. A proper data collection and processing strategy allows these tools scale infinitely with the company to stay informed, efficient, and confident in where the business stands.
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Thanks for the sharing the pain points!
I am curious to understand how you see the layer after the DWH?
Why are there no BI-Tools only LLMs? And what do you think about custom agents like Dot compared to LLMs? https://getdot.ai/ (I am a co-founder, we build Dot with OpenAI's GPT)
Maybe a good topic for another post?