Employees at startups face fast-paced work environments and typically wear multiple hats. Given the data-oriented nature of businesses these days, almost every startup hires at least one business data analyst, whether a data science professional or a corporate FP&A analyst, to help them make sense of customer and market data.
Generally speaking, however, data analysis doesn’t fit with the traditional startup work model that prioritizes quick results and explosive growth. Datasets can be complex to decipher, and the pressure for quick insights leads to a pressure-packed environment that data scientists must navigate.
Here are four challenges that data analysts at early-stage startups routinely face.
Data analytics are only as good as the datasets that underlie them. Ensuring data integrity typically falls under a data analyst’s purview, and at a startup, this task can be tough.
Typically, early-stage startups lack the deep resources that make a data analyst’s job easier. For instance, installing analysts find it easier to derive insights from data when data quality, extraction, and loading process have been previously defined.
In a startup, data analysts have to play the role of data governance officers as well. Donning this hat involves diving into data sources and examining extraction and data transformation procedures. For instance, one data source might provide bulk outputs in CSV files, while a second source might offer high-level data with low granularity.
In such situations, data analysts must discover whether alternative data fields exist, plan to import them into their systems, and consider data integrity and update processes at all times. Executing these tasks is challenging for a team, even when startups enter growth mode. A solitary data analyst working in a lean startup’s data team will undoubtedly find these tasks far more challenging to solve, which makes coordinating between relevant stakeholders a mission-critical skill for financial analysts and other data professionals.
Ensuring ongoing data governance is also a challenge since the company’s data sources will grow as its revenues increase. Thanks to a lack of time, data analysts might neglect to document their procedures, leading to issues down the road.
Growth can be a double-edged sword. Growth in data sources offers potentially greater insights. However, if left unchecked or without backing from sound processes, increasing data sources can lead to silos. Many growing corporations encounter this issue. For instance, a company billing its clients via multiple platforms and sales channels might struggle to integrate data from app stores and PoS terminals. When dealing with importing data from numerous data sources, a data import tool that allows you to take data from any source and transform it to match your system is recommended.
In such cases, analysts will stage revenue data on Excel files and manually merge datasets when running analytics. These ad-hoc procedures work if a business is small and a team is nimble. However, they are not repeatable processes and compromise a company’s long-term growth.
For instance, going public while depending on such processes is impossible for a growing startup. Compliance requirements such as SOX reporting are impossible using manual processes.
Data silos also cordon off a majority of the organization from data-driven insight. Often, data analysts lack the deep knowledge that a business professional has. For instance, a sales professional will understand the startup’s customers better than an analyst. Giving the sales team member access to customer data and allowing them to run reports will provide the business with deeper customer insights.
However, data silos make this impossible since the salesperson can never trust the data they see. As long as data exists outside their analytics platform, they cannot derive insights, leading to lengthy report workflow requests that don’t solve issues. This situation is an issue for data analysts as well since they’ll have to deal with data they’re unfamiliar with.
The majority of startups fail, and most CEOs’ intense focus on short-term results is often to blame. Startups must display growth to impress their investors, and many companies make the mistake of using data analytics as a means to boost short-term results.
As a result, data analysts lack time to examine data context and dig deeper into their datasets. Analysis reports then become superficial, lacking insights that power a business’ growth.
Ideally, companies must prioritize a balance between short-term needs and long-term focus. This principle holds true in data reporting as in any other aspect of startup life, and a CFO armed with the right scenario analyses, based on the right financial models, can help steer the ship to success.
Data analysts in such environments can contribute insights that offer both short and long-term benefits. An intense short-term focus will lead to faulty models that decrease revenue, compensate employees based on bad KPIs, and increasing biases in models that fail to account for new market trends.
Startups often pursue growth above all else, and this can lead to confusion in analytics. Given the executive priority of chasing growth, data analysts are incentivized to model scenarios that might lead to poor long-term competitiveness.
For instance, expanding a company into new markets might look great on paper and attract VC attention. However, if a company cannot adequately service those markets, that “growth” is useless and will turn into a cost center. Only savvy financial analytics can reveal the correct strategy that will unlock genuine and sustainable growth.
Data analysts often find themselves pioneering data science at their startups due to a lack of precedent. Thus, instead of focusing solely on analysis and modeling, data analysts must define organizational goals via analytics and install governance processes that support those goals.
These tasks are challenging for established professionals, let alone data analysts just getting started with their careers.
Working at startups offers data analysts many challenges. However, the fruits of overcoming them are immense. Data analysts end up dealing with every aspect of data science, leading to well-rounded skills that serve them well in the long run.