How to Instill a Data Culture in Your SaaS Team: Takeaways with Nybl

How to Instill a Data Culture in Your SaaS Team: Takeaways with Nybl

Data is everywhere, and for an entrepreneurial mindset, it is also everything: from session entries and visit frequencies, to profit margins and operational capacities, each activity carries its own data set across the multiple touch points of the value chain. Over the past decade, a core focus for businesses across the globe has been to properly source and leverage this extensive data in favor of their growth strategies and product engineering.

The evolution of data sourcing and analytics, hence, has been a core driver for the global entrepreneurial ecosystem, especially after breakthroughs in data decentralization, security management, and integration technologies, which brought forward the next generation of industry disruptors. 

While evolved in its intricacy and expansive in its potential, many facets of the world of data processing are still exciting and steep learning curves for businesses in MENA. This has put data-focused SaaS players at the forefront of industry-wide transformations, and today we take a deep dive into data culture with one of the region’s anticipated data pioneers. nybl has been designing AI-powered solutions, or “Knowledge-Driven Machines” as they refer to it, to unlock the potential of operations across a wide range of industries. The technology development house drives digital transformation using real-time machine learning on an IoT platform. The nybl-designed “Knowledge-Driven Machines” are built on a software platform that delivers real-time failure prediction, prescription, prevention, and optimization designed for multi-function and malleable for various types of operations. 

Earlier this year, the nybl team reached out to take their internal teams on a unique data transformation. For a data-heavy company, nybl’s relationship with data is driven beyond the ingenuine processing and effective development and into instilling a rooted data culture to steer the ship, and with it the need for a tailored data science program arose. Today we invite Hafsa Yazdani, Head of Technology at nybl for a reflective dive on designing your own data transformation and the greater impact it has on team dynamics and product design.

What is Data Culture?

The intricate application of data processing into various product offerings in MENA has been shifting market landscapes and business models across the board. On the mobility front, for example, AI/ML-driven courier and storage data analytics have allowed for Q-commerce (instant delivery) propositions; challenging infrastructural gaps in markets like Egypt and Iraq, and maximizing market penetration in markets like the UAE, KSA, and Kuwait. 

AI-powered mobility has also spilled over into e-commerce offerings, allowing for the rise of B2B e-commerce solutions that not only deliver on time but are now also integrating data-driven crediting and micro-crediting solutions for buyers and suppliers. Underwriting risk analysis coupled with the processing of social and behavioral data is also driving the next generation of fintech, insurtech, and open finance across the region. 

The data cycle prerequisite is being able to think of data both as an input and an outcome. In these terms, nurturing a data culture is adopting a continuous pipeline of knowledge generation through iterative processes, banking on an intricate interaction between human input and purposed automation. This means having the curiosity and the tools to transform all touch points into data, all data points into visualized and digestible insight, and all insight into actionable steps- in an all-hands-on-deck approach.

Tailored Data Training 

Data science, being a multi-faceted and multidisciplinary world, can be transmitted across many business units to assimilate the full team into harmonious data thinking, and that’s what the nybl team was looking for. Seeking complete harmony between the different internal departments grounded by a deep understanding of data, nybl and the AstroLabs program designers explored the option of splitting the program into two tracks of learning: the non-technical team, set to acquire the fundamentals of data, and the technical team, set to deep dive into the theoretical, the scientific, and the specialized. 

The key outcome was symbiosis: the seamless, knowledge-based interaction between technical and non-technical teams in building open-sourced innovative solutions. The program should enable the non-technical team (especially the client-facing ones like sales and account management) to understand and synchronize with the technical team faster. The company developers and engineers also required theoretical knowledge to complement and expand the practical knowledge they’ve built over the years. 

The tailoring of each training track was a focal point for this upskilling exercise. The non-technical team underwent a 4-week self-paced and live-learning training where they acquired the fundamentals of data science, learning the focal terminologies, data processing tools & techniques, as well as being introduced to data dashboards and modes of visualization. Soon enough, the team realized the potential impact of this training far beyond the day-to-day. While the company itself provided AI-powered solutions to customer challenges, the well-roundedness of the non-technical team will allow them to faster identify pain points, understand the modes of work required by the technical team, and have a knowledge-based vision of the next possible steps- optimizing and enhancing top of funnel solution building.

On the technical side, the 8-week data training program encompassed specialized topics including an exploration of data science, Advanced Python and syntax for programming, data collection & web scraping, advanced machine learning, data engineering as well as advanced mathematics for data science. By being heavily grounded in the scientific facets of data processing, team members are now able to complete vision and strategy planning autonomous of leads, allowing members to develop their own innovative solutions which will achieve a truly bottom-up approach to software development. 

Most remarkably, the data science program flows beyond the effectivity of communications and productivity of work, and into undergoing a real digital transformation. The real impact of the program was in unlocking the data agents so every team member can think, leverage, and develop the data at hand- turning the team into a data-driven learning machine.

Data Culture and Beyond 

Just as any touchpoint can be a valid data generator, adopting a data culture will transform every team member into a potential data processor. In doing so, every individual can confidently add their unique inputs and filters to the invaluable data bank of day-to-day operation.

A recent study finds that data-driven companies reallocate talent and capital 4x quicker than their peers, while 58% of businesses making data-driven decisions are more likely to make revenue targets than those that don’t. Here, the unique outlook on data, the knowledge-based intuition, and the technical ability to verify develop, and process intricate data become core drivers of effective decision-making. While that ensures the consistent meeting of targets, instilling a data culture in all teams becomes a vital exercise of growth and a core driver of digital transformation.