- The significant organizational and technical constraints startups face, dissecting the ‘build vs buy’ dilemma in the context of these limitations.
- Drawing upon real-world examples, we share how Brazen uses Ascend to overcome these constraints and maximize efficiency.
- We provide actionable insights and offer a roadmap for startups navigating the intricate landscape of technology decisions.
Company and Technical Constraints for Startup Data Teams
Startup Company Constraints
- Tight Budgets: In a startup setting, the scarcity of financial resources leads to a high degree of scrutiny for every expenditure. Each dollar spent must yield maximum value and every investment has an opportunity cost. Funding one project often means that another project goes underfunded.
- Lean Staff: For many startups, there is no dedicated team for data projects. At best, there might be one dedicated individual or a handful of team members able to contribute fractions of their time. The lack of human resources adds complexity to the initiation and execution of data projects.
- Competing Priorities: In the rapid-fire environment of a startup, priorities shift quickly. Team members could be called to address an urgent customer issue, or to push a new initiative. Projects evolve, timelines change, and this fast-paced environment can make it difficult to kickstart and maintain momentum for data projects.
- Large Datasets: Startups generate a significant volume of data, from product features to log data. The availability of cheap storage makes it easy to stockpile this information. However, a sudden increase in customers can lead to exponential growth in data, which may outpace the capability of traditional analysis tools.
- Data from Multiple Sources: As a startup grows, it typically starts to use a range of third-party tools for different business functions, each generating its own data. This leads to complex business questions that span multiple data sources, complicating data management and analysis.
- No Single Source of Truth: The challenge of multiple data sources is compounded by the lack of a unified data repository. Analyzing data from disparate sources often means exporting it into spreadsheets, where maintaining consistency and up-to-dateness becomes a Herculean task.
- Inconsistent Calculations: With multiple analyses taking place across different spreadsheets, inconsistencies are likely to creep in. The lack of a single source of truth leads to a situation where the validity of the data is frequently called into question.
The Build vs Buy Dilemma
Building your solution can offer customization potential, allowing you to tailor your tools to your specific use case. However, this aspect should be weighed against the fact that a built solution may not scale well or may require constant tweaking and maintenance as your startup grows and changes.
Time to Market
Brazen's Use Case: How Ascend Maximizes Efficiency
The Ascend Solution
- Speed and Agility: With Ascend, Brazen can access new data from their databases within minutes, vastly improving their response time to business questions.
- The Learning Curve: Brazen recognized that while Ascend came with a learning curve, mastering advanced functionalities like data ingestion and partitioning ultimately offered more flexibility. With Ascend’s help, Brazen found the onboarding process not just fruitful but also engaging.
- Data Variety: Ascend’s ability to handle a wide range of data types and sources proved invaluable. By defining transformations and schemas on data as it’s ingested, Ascend ensures data consistency and quality.
Ascend’s Role in Startup Success
Read More: What Is Data Pipeline Automation?