Why Every Early-Stage Start-Up Needs a Modern Data Stack

Every successful start-up will eventually set up a modern data stack, but it oftens happens later than it should. Most will struggle along with managing increasingly complex and large volumes of data in spreadsheets, asking engineering to pull data, and stringing together brittle integrations. This leads to inconsistent data, biases against making data-informed decisions, and can impact your database performance.

By having a modern data stack in the early stages of your business, you’ll avoid these current and future pain points. You’ll stay on top of important metrics, get ahead of competitors by having a deeper understanding of your customers, create a data-driven culture from the start, and easily keep your investors informed.

Here’s what you need to know about setting up a modern data stack early on:

How a modern data stack helps early-stage start-ups

Automated reporting for you and your current, prospective, and future investors

Reporting on some key metrics doesn’t require a data stack. You can simply query your database to know how many signups you’ve gotten or the number of daily active users you have. But having a data stack enables you to automate this reporting in a BI tool. Instead of pulling data and juggling spreadsheets every time you need to share updates with your investors, you can automate your reports to refresh on a specified cadence and be emailed to individuals.

Optimize ad spend

Ad platforms do a great job of telling you which ad performs best. But to assess which channel is most effective in producing customers and revenue, you can’t just look at the data that these platforms provide. This is because they often provide misleading attribution and make it difficult to compare across platforms. Instead, you have to combine your ad data with your customer data, and that’s really hard to do if you don’t have a data stack.

Let’s say you’re able to achieve a low cost per acquisition (CPA) by targeting lower cost audiences. A low CPA suggests your advertising has been successful. But if you combine your ad data with your customer data and find that cohort churns quickly or never upgrades, your advertising hasn’t actually been effective. Instead of optimizing for CPA, you should’ve been optimizing for cost per revenue. And by optimizing for cost per revenue, you would’ve taken a different approach.

Mitigate customer churn

Data isn’t a substitute for having good account managers, but it can surface things that are indicators of a customer potentially churning.

When customers say they’re interested in turning off your product or canceling their subscription, it’s too late. Preventing customers from leaving at the moment of churn is a mistake. What you want to do instead is to mitigate churn by having a deep understanding of your customers and the actions that might indicate they’re about to churn.

When customers are accessing your product or inputting information less frequently, it’s a good indication they’re not getting a lot of value out of your product. You can only see this happening by combining your product data with your customer data.

Get a deep understanding of your customers

When you have a large number of users or when your users take a rich set of actions, you’ll no longer be able to manage all this information in a spreadsheet or product analytics tool. This is when you’ll need to set up a data stack so that you can effectively work with all this data (and exclude non-relevant segments) to not only understand your customers, but also their future behavior.

Build a culture of data from the start

Having a modern data stack enables a data-driven culture. This is because it’s easier for everyone to access, understand, work with, and operationalize the data. Less technical people aren’t reliant on engineering to pull data, so they’re able to quickly run experiments, measure results, iterate, and double down on what’s working. You’ll encourage arguments that are settled with data, rather than restricting it to a chosen few who have the skillset to access it.

If you’re trying to recruit data-minded employees, a modern data stack that’s built on the best tools can help you attract the right candidates. Having the right tools and data infrastructure is an indicator that you care about empowering anyone at the company to use and own their data. And these candidates will be glad there’s tooling and a data-driven-culture in place already.

Easily calculate complicated metrics

You’ll need to start reporting on more complicated metrics that require more than counting values. An example is retention. Calculating retention is more complicated because it requires a numerator (the number of users you had at the end of the time period, minus the number of users you acquired during that same time) and a denominator (the number of users you had at the beginning of the time period).

While many product analytics tools provide retention rate as a built-in feature, calculating it yourself gives you much more control over defining the numerator and denominator. This is important because you should exclude cohorts that aren’t reflective of the general population. Otherwise, they’ll skew your calculation. But it’s difficult to do this if you don’t have the ability to query your data in a more sophisticated way.

As your business grows, you and your investors will want to understand more complicated metrics, like customer lifetime value, customer acquisition cost, and payback period. Your ability to pull these metrics and answer these types of questions will give you a massive advantage in building a long-term business and fundraising.

Putting off a modern data stack slows down analysis, negatively impacts your user experience, and makes the transition more painful in the future

As an early-stage start-up, your time and resources are precious. It’s tempting to put off implementing a modern data stack, but here’s the impact of doing so:

  • You’ll get inconsistencies and multiple answers, since you’re not transforming your data. When you’re driving toward a north star metric, this creates uncertainty in whether you’ve achieved your goal and how you’re progressing toward it. Plus, people will define the metric in the way that works best for them.

  • Querying your database directly will negatively impact its performance. Your website and product will slow down, which harms your user experience and can lead to lost sales.

  • When you finally set up a data stack, it’ll be painful and time-consuming. Without a data stack, adding more data sources and building more reports will result in clunky ad hoc processes. You’ll create a large web that needs to be untangled and a lot of reports that need to be rebuilt when you transition to a data stack. The earlier you put in the right infrastructure, the less challenging it is to make the transition. You’ll have the foundation in place to incrementally layer more data sources and reports without it turning into a mess. It’s also easier to practice good data practices, such as creating table descriptions and adding data validation, when you only have a small amount of data to manage.

Why you need the full stack and not just a data warehouse

Within the data stack, there’s a lot of focus on the data warehouse. Most people have heard of data warehouses like Snowflake, Big Query, and Redshift. Many companies will look at implementing a data warehouse first before implementing other components of a modern data stack. While a data warehouse by itself is powerful, you need to have the other components of the data stack in order to get the most value out of a warehouse.

First, you need an extract, transform, and load (ETL) tool to take the data in all your different systems and load it into the data warehouse. While you can manually set up extracting and loading data through APIs, using an ETL tool makes the set-up much easier, reduces the amount of engineering needed, and takes care of maintaining the APIs for you.

You also need a transformation layer. Data transformation is all about understanding and cleaning your data effectively before it goes into a BI tool or spreadsheet. With a transform layer, you can dedupe, clean, and manipulate your data before it reaches the end-user. You want to transform your data before it gets analyzed because this ensures everyone’s pulling from the same data and everyone at the company will arrive at the same answer.

Not transforming your data results in inconsistent answers. For instance, your product team and operations team are both trying to find out how many monthly active users you have. In writing their queries, they define what it means to be “active” differently. As a result, they arrive at different answers. If you transformed your data upstream to define what an active user is, both teams would come to the same conclusion. Establishing data consistency in the early days of your start-up is extremely important because it speeds up analysis, reduces confusion, and scales as your team grows and more people are working with data.

How to set up a modern data stack in an hour

It used to take a few weeks to set up a modern data stack, but there are now options that cut implementation time by 99% and don’t require engineering. The barriers to scalable data infrastructure have largely been removed, making it more accessible to early-stage start-ups.

Mozart Data provides an out-of-the-box modern data stack that can be set up in an hour. We offer best-in-class tools, including a Snowflake data warehouse and over 200 data connectors, in an easy to use, all-in-one platform. Join dozens of data-driven start-ups that are already using Mozart Data to easily access and prepare data for analysis. Contact us now for a demo.

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