It is a myth that only large, thriving companies can make use of big data. In truth, startups are in the greatest need of the information and insights provided by data collection and analytics — but few entrepreneurs understand how to set up a scalable data analytics system that will provide immediate advantages and continue to offer benefits into the future.
This guide should help entrepreneurs at every experience level develop a powerful data analytics framework that does more to push their organization forward.
Big data cannot do everything all at once, especially when manpower is limited — as is true in the startup environment. Entrepreneurs need to focus their data analytics efforts on specific objectives, which will guide the development of data analytics processes now and into the future. Some examples of worthwhile goals for startup data analytics include:
- Aggregating collected data in one location
- Making data accessible to various teams and staff members
- Automating the generation of data reporting
- Minimizing time spent on data administration
In the earliest stages, goals for data analytics should focus on setting up a functional system that can support more advanced goals as the business grows. Once the framework is created, goals might be more associated with utilizing data for decision-making, like understanding trends in consumer behavior.
Most startups have more than a single entrepreneur as a key decision-maker for the organization. If one leader is eager to create a data analytics framework but the rest have neglected this critical step in startup development, that one executive still needs to earn buy-in from the rest of the leadership team.
If not all decision-makers are on board with a unified data analytics strategy, the data team could receive conflicting messages that cause inconsistencies and inaccuracies in the entire data system. To ensure buy-in, entrepreneurs may need to generate an outline for their analytics plans, replete with goals, budgets, workloads, staff, and more.
Start With Basic Tools
Startups with limited budgets do not need to invest in the most cutting-edge analytics tools on the market — at least not yet. There are plenty of free analytics solutions that are powerful enough to manage the limited quantities of data available to a single startup, and many startups will already invest in other tools that can supplement data analytics endeavors.
As the framework develops, data science teams and executives can work together to identify more comprehensive solutions that suit the growing organization’s needs. Then, when the business eventually invests in advanced data analytics tools, it can be certain that those tools will cover all necessary features and functionality.
Too many novice business leaders make the mistake of assuming that more data is always better. In truth, an organization can easily be drowned in data; if too many resources are devoted to collecting too much data that does not provide valuable insights, the business will suffer.
Especially during the startup phase, it is imperative that leaders understand how much time and expertise they can devote to mining data. Usually, startups do not have the capacity for full data science teams, so they need to make the most of staff and solutions that efficiently collect and analyze only the most pertinent data.
All this work developing a data analysis framework will go to waste if that framework is not compatible with more sophisticated data analytics tools and systems.
Business leaders inexperienced with data analytics should work with data science experts, either in-house or in consultation, to ensure that any processes and practices established today will not disastrously conflict with what they hope to accomplish in the future. There needs to be a balance between short- and long-term goals for the analytics framework in order to achieve success with big data.
These simple steps should establish the foundation of data analytics within the startup. Then, as the startup grows, leaders should invest in their analytics framework further with staff, tools and budget.
Eventually, a startup should hire a full-time data analyst to manage the data that is accumulating daily. More staff can dig deeper into past and present data to provide insights that leaders can use to make better decisions in the future.