It is true that importance of data-driven decision-making in the workplace is accepted. However, it is not true that all the companies who have invested heavily in data have successfully become data-drive. Only Fortune 1000 companies could be counted in it. The trend is declining. Startups which are establishing a data strategy and analytics framework can be less challenging as they have less data debt as well as fewer established processes. It is easier for them to introduce new processes, standards and tools.
Former Shopify VP Solmaz Shahalizadeh has come up with a valuable advice to help startups optimize their early data investments. The data team of Shopify expanded from a few members to 500 and thereafter led the creation of its machine learning products.
Shahalizadeh has shared practical tips and techniques to help startups use their data effectively. Her insights are believed to benefit founders, functional leaders and data teams as well to ensure data is accurate, accessible and of course used to drive decisions as well as innovation.
A strong data infrastructure is the foundation of any effective data strategy. Advances in cloud data stacks have made complex data usage simple. Now, most startups set up their data infrastructure prior to hiring a data team. She advises against building data infrastructure from scratch. She recommends using of existing tools.
She adds that it is important to collect the right data from the beginning. It is suggested to think carefully about what data to gather. It is an ongoing and collaborative effort between engineering and data teams.
She suggests further to unify data from multiple sources into a single place. It prevents data silos and simultaneously enables comprehensive analyses. It provides a 360-degree view of company performance. Choosing an out-of-the-box tool that meets standard data privacy and security requirements is advisable.
She also adds that the teams should answer their own questions. She recommends purchasing or using open-source tools. The platforms allow non-technical users to interact with data independently. This helps the entire team move faster and also allow data scientists to focus on complex projects.
