3 Key Data Lessons For Startups
Getting a startup off the ground can be a roller coaster ride! In the first couple of years – you convinced investors, built an amazing product and established a large customer base that absolutely loves your offerings. Now you’re wondering — what will trigger the next big inflexion point to achieve your growth ambitions? The answer is hidden within that data gold mine you’re sitting on!
It’s likely that you already have a backend database and various reporting functions spread across different tools. Now, it is time to step up your game. As an analytics consultancy with a focus on startups, we have gone through this journey many times and learnt the hard way. In this blog, we will share our top learnings and nuggets of wisdom — setting you up to scale!
Data Needs A Purpose
Your data strategy should be driven by your business growth objectives. Everyone in the team needs to be clear on what they are trying to achieve and how to measure success. Start with a bird’s eye view of your metrics, then repeat the above for each business function and product squad. Consider the following topics:
- What are your short and longer-term growth goals?
- Can you pinpoint a ‘North Star’ metric that measures the health of your growth?
- What are the contributing factors to your North Star metric? Define each precisely as this sets measurable KPIs.
How do you apply this? – Document your KPIs in a data dictionary. This acts as a glossary of business terms, their definitions, how they are calculated, and where that data sits. It provides a common data vocabulary and facilitates discussions across the board. This document does not need to be all-encompassing, it will naturally evolve as your business and analytics capabilities grow. With these definitions defined, you can build an analytic toolbox to help your team achieve their goals even faster.
Automate To Save Time
Automating today will save you time forever. The pursuit of efficiency is a key mindset in growing your analytics and automation is at the core. Our guiding principle is — if you are doing something for the second time, automate it, because it’s highly likely that you will do it again. Automation can save your team thousands of manual hours, which can be deployed elsewhere to fuel growth. It’s the equivalent of adding 10% to your headcount (a conservative estimate), without having to hire anyone!
Data automation comes in many layers. Firstly, you need to set up a solid infrastructure to ensure an automated, efficient data flow from various sources into your own centralised data warehouse (e.g. Snowflake or BigQuery). Raw data is then modelled based on your data dictionary definitions and transformed automatically into meaningful aggregations within your data warehouse. For maximum analytics efficiency we recommend all data processing logics to be captured within the modelling layer (one place doing the work, e.g. SAYN).
Why is this useful? — Modelled data can be used for all analytics purposes including; automated dashboards, deep-dive analysis, building data science algorithms or sending predictive data signals to various other applications (e.g. churn propensity to your App).
For example, knowing your user segmentation based on past behaviours allows you to customise their product journey to drive greater engagement and retention. This is how you personalise at scale!
In addition to time-saving, automation ensures data accuracy and insights consistency (less human error). Since all teams and applications are querying the same set of data models, you have achieved your single source of truth. Once you have centralised all data sources, you also have a complete 360 customer view capturing all touchpoints of the user journey!
Data Democratisation empowers everyone to make Informed Decisions. This is arguably the most important aspect of building a data-informed culture. So, what can you do to speed up data adoption?
Make data insights as easy as possible to digest. Most of your team will consume data via automated dashboards. Provide training and ongoing support to make the most of your reporting tool. Create layered reporting starting from a high level, which can be drilled down into specific areas. Finally, make sure key metric definitions are easy to find to avoid friction.
Appoint data champions in each business function. Find the most data-savvy person in each team and form a power user group. Run regular sessions to inform and train them on the latest analytics releases (I.e. answers to their burning questions). They will act as the go-to person within their business function for day-to-day data questions and help spread the power of data.
Continue to highlight the value and benefits of data. Like any product or service, make the value proposition clear and continue to add and communicate new features. Consider a monthly newsletter or commercial report that showcases the latest analytics milestones, successful use cases and how-to tips.
Make data fun! Data is much more than charts and trends and predictions. Data tells a story. A story about your customers and their behaviours. Dedicate some time to look beyond the quantitative “what happened” and into the qualitative “why it happened”.
How 173tech Can Help
Purpose, automation and democratisation — the three pillars to building your own analytics growth engine. We hope the ideas shared above help you accelerate your data journey as you scale! If you’re looking to get started on your data journey, don’t be afraid to get in touch with a friendly member of the 173tech team.