Scale Your Start-Up by Refining Your Data Strategy
Once you are ready for accelerated growth, continuing with the same data strategies can hold back your start-up.
- By Piyanka Jain
- October 23, 2023
When your start-up is at the brink of scaling, it is tempting to do more of what’s already working. However, continuing with the same data strategy that helped you come this far will actually hold you back from the exponential growth you want. During the early stage, a start-up’s data strategy prioritizes validation of the business model, refinement of the value proposition, and establishment of product-market fit. This strategy will no longer serve a start-up that is ready to prioritize expansion and optimization. Although this sounds right in theory, many start-ups continue using the strategies they’ve long outgrown in practice.
Where an Early-Stage Start-Up’s Data Strategy Falls Short
Let’s say a healthcare technology start-up making wearable fitness trackers spent its first two years collecting usage metrics directly from its product such as steps taken, heart rate, and sleep patterns. They used this data, quite successfully, to tweak and refine both their product and messaging which led to increased user adoption, engagement, and retention.
Delighted with their success, the start-up decides it’s ready to go global and distributes their wearable in new continents, but soon they find that their adoption, engagement, and retention rates begin to decline. Worse still, the same metrics decline in their origin country.
The plan was that once they expanded into new markets, they would continue improving the product to fit the needs of the new users based on the same data and that this continuous improvement would keep attracting and engaging users the way it did before. This process had worked, so “why fix what isn’t broken?” Unfortunately:
- The large volume of data gave conflicting insights about the wearable’s usage, leading to product decisions that alienated the initial customer base.
- The focus on metrics for product refinement instead of revenue generation led to missed monetization opportunities.
- Problem-solving focused on improving features of the product, instead of adapting their message to resonate with different cultures.
- The volume of data surpassed what their existing data infrastructure could handle, leading to incomplete insights.
In with the New: The Growth-Stage Start-Up’s Data Strategy
Many start-ups experience the same story, not realizing that the answer lies in upgrading their data strategy to fit the realities and complexities of achieving greater scale. Yet many start-ups continue to use the strategies that were made to succeed in the early stage. The following data strategy will help you if your start-up is about to enter the growth stage, a stage where maximizing time, money, and efficiency is key.
Expand Your Data Infrastructure
Prepare for a massive surge in the volume, velocity, and variety of data by investing in tools that can comfortably process large amounts now and in the future. If you are using a manual input process, replace it with an automatic one, and expand the number of tracked metrics to encompass scaling-specific business goals, particularly those of revenue generation.
To simplify the use of this updated infrastructure, you must also create a single reference point that allows you to access all your data from one place because during the scaling phase, you will need to cut down on time and effort spent searching for data and spend more time actually leading with insights. According to McKinsey, 19% of working hours are spent searching for information. That number may be much higher in growth-stage start-ups due to the absence of a fully established data infrastructure in such businesses. Therefore, for your start-up’s data strategy to operate at its highest potential, having this point of reference will unlock more time to find insights at a pace that can keep up with rapid scaling.
Invest in a Strong Data Culture
Start-ups in the growth stage often prioritize hiring product managers and marketing professionals. Build more features. Attract more customers. Data analysts are then hired to help these people track their work. Great. However, in the absence of a strong data culture that’s bent on value creation without hierarchy, these analysts simply pull data, clean it up, and deliver unactionable reports. According to Gartner, lack of data culture is one of the top reasons why only 20% of insights provide actual business outcomes. A framework that can streamline the creation of insights that lead to profitable outcomes can be very useful here.
In the case of the wearable start-up, the CEO might demand an analyst report on wearable sales across countries. The resulting bar graph would reveal no insight. In a strong data culture, however, the analyst asks questions to get to the “how” and “why” behind the CEO’s request. They find that because sales have been dropping, the CEO wants to know which region they should invest in to generate more revenue. With this business question, the analyst can develop hypotheses to test, analyze this data, and present insights that are immediately useful.
Notice how a lack of hierarchy enabled the analyst to get the intent and business consideration behind the task straight from the decision-makers. This ability to drill down to the real business question is only step one. BADIR (a product of Aryng, the company I lead) is an example of a framework that supports this kind of culture. In such a culture, step two would be for the analyst to prepare an analysis plan for testing their hypotheses and measuring results. An early-stage start-up’s analysis is more simplistic, usually limited to correlation and trend lines, which serve their purpose well enough. However, this won’t maximize results during the growth stage. With a structured, hypothesis-driven approach, growth-stage start-ups can avoid losing time and funds to analyses that yield no real insights or making decisions based on incomplete inferences.
Only after establishing a data culture should you collect, clean, and verify your data and then analyze it for insights. Early-stage start-up data strategies tend to “boil the ocean” for those aha moments to appear, which is easier to do with limited early adoption usage data. The more focused growth-stage data culture described, however, allows a fast-scaling business to focus on solving complex problems methodically and converting them into usable recommendations.
Enable Cross-Silo Collaboration
Research conducted by Adaptive Insights (since acquired by Workday) found that 69% of CFOs believed that one of their biggest mistakes was keeping data siloed. During the growth stage, as teams become bigger and more independent, data becomes pigeonholed within silos. Eliminate these blocks from the get-go by ensuring every team can access all the data they need without extra points of friction, emails, and follow-ups. The growth stage will require instant and transparent access as teams work together to roll out new launches with the rapidity of scale.
This includes creating a central repository of data, establishing data governance policies that define ownership, control, and access to data, and creating integrated systems that blend different kinds of data so you can derive comprehensive insights. Standardize the format of the data you create and conduct regular audits to ensure nothing falls through the cracks.
Upgrade to Advanced Analytics
The simple analysis of focused engagement metrics is useful during an early-stage start-up’s continuous iteration process. During the growth stage, a start-up’s data scenarios become more complex. You will receive information on a diverse array of user journeys and purchasing behaviors, in multiple regions, toward a broader range of products and features. More complex data sets will require more complex analytics and modeling to create high-quality insights.
In the growth phase, start-ups will also face more competitors who are already using such data as currency. Only by leveraging more advanced analytics can growth-stage start-ups gain a competitive edge and optimize their revenue streams. These techniques include predictive analysis, A/B testing and experimentation, customer lifetime value analysis, and segmentation.
Empower the Head of Data
As the early-stage start-up transitions its data process, infrastructure, culture, collaboration, and governance into those of a growth-stage start-up, the role of the head of data, or chief data officer, will become more important than ever.
The CDO will be responsible for metrics about such issues as the number of risk events caused by data, number of issues caused by bad data or friction in its access, and the ROI on outcomes created by the data team’s insights. CDOs assess, establish, and drive the data landscape of the growth start-up, ensuring that executives can make decisions with greater speed and confidence to generate long-term value.
Through all of this, it is important for the growth-stage start-up to keep its data strategy flexible. Although the product may be refined and the market fit established, the salient feature of successful start-ups is their ability to pivot with ease and speed when needed. A growth-stage data strategy therefore, while more advanced than an early-stage strategy, must be open to evolve even further with the demands of an ever-changing world.