Driven by the desire to see businesses grow, Adebola Olabode pivoted into data science, where his keen interest in interpreting and analysing data, identifying trends and patterns have proven veritable. In this interview by NCHETACHI CHUKWUAJAH, the UK-based data scientist, who currently works with a research institute focused on renewable energy and a sustainable environment, speaks on the potential for businesses to scale up their operations by adopting a proper data culture, among other issues.
What is your journey as a data scientist like?
I am a data scientist who is passionate about demystifying the world of data and helping organisations harness their full potential. Data, for me, is more than just numbers and statistics — it is the key to unlocking insights, solving problems, and making informed choices. This motivated me to venture into the digital technology field with a focus on expertise in data science and artificial intelligence.
Participating in skill acquisition programmes during my national youth service year exposed me to online courses that helped me understand the connection between statistics and computer science. These online courses and tutorials equipped me with the necessary skills and expertise needed to work as a freelance data analyst. Working with these freelance platforms allowed me to work on a diverse range of projects across various industries, from healthcare and finance to e-commerce and telecommunications. This allowed me to hone my skills and deepen my understanding of data and technologies that can be used to extract insights and drive strategic decision-making.
Having such a strong foundation gave me the confidence to take on the role of an IT analyst at a shipping and logistics firm where I used my data skills to design and maintain dashboards for tracking key performance indicators (KPIs) such as procurement cycle, inventory turnover, and order rates. This dedicated effort increased company revenue and helped the company improve its inventory stocking according to customer demand.
Driven by my passion for aiding the development of a growing business, I moved to a startup that is dedicated to empowering young minds through the provision of relatively cheap digital technology solutions like laptops, monitors and other accessories. In this role, I used my data skills to spearhead the acquisition and retention of new market shares for the company. This was achieved through the development of a machine-learning web application capable of predicting prices based on customer preferences and a subscription platform that uses customer data to provide post-sale services.
My relentless pursuit of professional and personal development inspired my decision to further expand my horizon in data science by moving to the United Kingdom for a master’s degree in Artificial Intelligence and Data Science. I recently also became a certified cloud practitioner, further positioning me as a versatile and forward-thinking data scientist in today’s rapidly evolving digital landscape. This certification provides me with the knowledge and capabilities to harness the power of cloud infrastructure such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud platform to access scalable and cost-effective computing resources, enabling me to build, deploy, and manage data science applications and solutions more efficiently and effectively.
Presently, I am working as a data scientist at a research institute that is focused on how humanity can use renewable energy and environmental monitoring to achieve long-term sustainable development. The institute’s strategy revolves around using innovative thinking and technology to unlock the answers to the biggest challenge humanity has ever faced – achieving a net-zero carbon economy.
My journey as a data scientist has been filled with numerous ups and downs, but through continuous learning and upskilling, I have been able to overcome obstacles along the way. I also immersed myself in online courses, attended industry conferences and collaborated with peers and mentors. This has helped me stay abreast of new technologies, methodologies and tools within the field of data science. I hope to continue using my skills and experience to tackle real-world challenges, make a positive impact on businesses and society, and contribute to the exciting and rapidly evolving field of data science.
How has it been so far pivoting into data science with your background in logistics?
As I said earlier, transitioning into data science has required a commitment to continuous learning and self-improvement, but it has been an exciting and rewarding journey so far. Although pivoting into data science meant I had to learn new skills and technologies, my previous experience in logistics gave me a solid foundation that has proven vital in my endeavours. Constantly dealing with procurement and supply chain optimisation made me develop strong analytical skills, which are essential for data science. This has positively influenced my ability to interpret data, identify patterns and complete complex data science projects.
Additionally, the movement of goods from one place to another usually involves navigating intricate systems and proffering solutions to real-world problems amid unexpected circumstances. Doing this over and over again made me understand the importance of perseverance when faced with daunting challenges. This enhanced ability to solve problems has translated well into data science, where I am usually saddled with finding innovative solutions to challenging data-related issues. While there have been some difficult moments along the way, my background in logistics provided a strong foundation for my transition into data science. It has enabled me to leverage my existing skills and the importance of adapting to new technologies and methodologies to stay relevant in a rapidly evolving field.
How can businesses benefit from imbibing proper data culture?
Modern businesses have large quantities of information at their fingertips that can improve customer satisfaction, refine action plans and streamline operations. However, with so much information within reach, making sense of it and drawing insights that will add value is also fast becoming a problem. Only firms that understand how to process their data properly can take advantage of the situation. This is why companies should strive for a positive data culture.
Data culture can be loosely defined as an organisation that uses data to make decisions. That is, data is an important part of the company’s identity, mind set and operations. Having worked in a startup where I encouraged the organisation to rely on data-driven insights to increase revenue generation, I can boldly say using data for decision-making gives businesses a comparative edge.
In the instance of the startup where I worked, my team and I created a business intelligence database that gave us unbridled access to quality customer data. This made it possible to segment customers based on their purchase and analyse the entire customer life cycle. We identified opportunities for providing post-purchase support by creating a digital subscription platform. The subscription-based services provided ongoing value to users by sending them alerts for software updates, giving access to premium content and priority customer support services. Furthermore, the company was able to tailor marketing campaigns to increase the relevance of the subscription offerings. The startup achieved customer retention, implemented cost-effective strategies for promotion campaigns and created a recurring revenue stream.
From this real-life example, we can see that having a positive data culture will encourage sustainable improvement as it becomes much easier to implement best practices based on real evidence. Having a data-driven culture also ensures a transparent decision-making system that is fully visible to team members. This, in turn, encourages high levels of compliance and accountability by eliminating guesswork and uncertainty.
In summary, data culture is important for growth as it enables efficient and effective decision-making. Organisations that derive insights by analysing data are likely to achieve geometric growth through increased revenue, improved profitability and customer service, and enhanced operating efficiencies.
With your experience of working with a startup in the tech space, how can the startups in Nigeria scale up?
As a data scientist who has contributed to the growth of startups through innovative thinking, I believe that startups can withstand the unstable Nigerian economy by making a data-driven approach an integral part of their growth strategy. With many small and medium-sized enterprises (SMEs) folding up in recent years, having a proper data culture can help prevent business failure by enabling effective decision-making at a much faster pace.
However, having been in the system, I understand that the major issue faced by most startups when it comes to the incorporation of a data-driven culture is what I will refer to as a lack of knowledge about the importance of data and how it can be crucial to improving their productivity and service delivery. This problem has roots in the absence of proper data culture in Nigeria and the shortage of skilled data professionals who can help startups effectively collate, analyse, and interpret data. Thus, even if a startup founder understands the importance of data, the underlying poor perception of Nigerians regarding data and the shortage of data professionals directly impact how startups can collect accurate data that can be used for reliable analysis and decision-making.
To overcome this problem, business owners must be ready to invest in the proper data infrastructures as this is paramount for any company aspiring to become data-driven. A robust data infrastructure ensures that data is captured accurately, stored securely and made accessible to relevant stakeholders in a timely manner. With a well-designed data infrastructure in place, companies can leverage advanced analytics techniques, such as machine learning and predictive modeling to uncover hidden patterns, identify trends and gain actionable insights. A proper data infrastructure enables companies to effectively manage and govern their data assets, ensuring compliance with regulatory requirements and maintaining data quality and integrity. This can help startups demonstrate their value proposition to potential investors and secure funding for growth and expansion. By presenting compelling data-driven business cases, startups can attract investment capital and strategic partnerships to fuel their growth trajectory. Most investors are attracted to startups that can demonstrate a clear understanding of their market, target audience and growth potential based on concrete data.
Data-driven analyses can be used to identify gaps, trends and opportunities. Presenting data-backed information on consumer behaviour, market demand and competitive landscape can instill confidence in potential investors.
Ultimately, data can play a pivotal role in helping startups in Nigeria scale up by providing valuable insights, optimising business processes, enhancing customer experiences and mitigating risks. Investing in a proper data infrastructure is essential for unlocking the full potential of data-driven decision-making and driving sustainable growth and innovation within the organisation. By harnessing the power of data-driven strategies, startups can overcome challenges and capitalise on opportunities for growth and success in the dynamic and competitive business landscape
Are there new insights into renewable energy and sustainable environment you would like to share with your experience in this research institute?
At the research institute where I currently work, we are at the forefront of exploring innovative solutions to harness renewable energy effectively whilst ensuring environmental sustainability. The institute is focused on the crucial role of technology in maintaining a stable renewable energy supply. Renewable energy sources, particularly wind and solar, are inherently dependent on environmental conditions, making the optimisation of their production and distribution vital for ensuring a resilient power supply.
As a data scientist immersed in this field, I can attest that using big data for forecasting is rapidly emerging as a linchpin in the transition to renewable energy. Machine learning facilitates the seamless management of diverse data pipelines and the deployment of predictive models. The precise forecasting facilitated by machine learning ensures better integration of renewable energy into the grid, improves decision-making in energy trading and bolsters grid stability by anticipating and mitigating fluctuations in supply and demand. This underscores the transformative potential of big data and machine learning in advancing renewable energy and fostering a sustainable environment for future generations.
Beyond policy statements, how can Nigeria achieve environmental sustainability?
Man’s dependence on the environment in the pursuit of economic growth has led to environmental degradation, hence, the need for proper resource management through regulations to ensure a balance is maintained. Achieving environmental sustainability in Nigeria requires a multifaceted approach that addresses various environmental challenges while promoting economic development and social equity.
Personally, I believe Nigeria can achieve environmental sustainability by investing in renewable energy sources and proper waste management. Given Nigeria’s abundant solar resources, investing in solar energy infrastructure can help diversify the country’s energy source, reduce reliance on fossil fuels and mitigate greenhouse gas emissions. Investment in renewable solar energy can involve both large-scale utility projects and decentralised solar solutions to improve access to electricity across the country. Enhancing waste management infrastructure, including recycling and composting facilities, can reduce waste, mitigate pollution and create job opportunities and economic development.
Stakeholders need to focus on public campaigns to educate the masses about waste reduction and segregation in a bid to promote behavioural change among Nigerians. Achieving environmental sustainability in Nigeria requires the joint efforts of the government, civil society, the private sector and individuals. By implementing these strategies and embracing other sustainable development practices, Nigeria can build a more resilient and sustainable future for the coming generation.
With incessant power cuts, epileptic power supply and calls to reduce fossil fuels, do you think it is time for Nigeria to embrace renewable energy?
Certainly. With Nigeria facing incessant power cuts and an unreliable power supply, coupled with global calls to reduce reliance on fossil fuels, embracing renewable energy is not just advisable but imperative. Renewable energy sources offer the promise of decentralised energy production, which could significantly improve access to electricity across the country. Unlike fossil fuels, which are finite and depleting, renewable energy sources are naturally replenished by the environment, ensuring their long-term availability.
Given Nigeria’s predominantly tropical climate with abundant sunlight, solar energy stands out as a particularly promising option for alleviating the country’s power woes. Tropical regions like Nigeria receive consistent and ample sunlight throughout the year, making solar energy a viable and sustainable solution. While Nigeria has made strides in investing in renewable energy, there is a pressing need to prioritise these sources to address the longstanding issue of inadequate power supply. Currently, the country heavily relies on fossil fuels, primarily natural gas, for over 80 percent of its electricity generation, with the remainder coming from hydropower sources.
Shifting towards renewable energy not only reduces dependence on fossil fuels but also contributes to mitigating climate change and promoting environmental sustainability. Moreover, embracing renewable energy can lead to long-term cost savings. With renewable energy technologies, there are few or no moving parts leading to minimal need for maintenance costs, resulting in reduced operating expenses over time. Given Nigeria’s energy challenges, transitioning to renewable energy presents a viable solution that can enhance energy security, mitigate environmental impact and foster sustainable development. It is time for Nigeria to seize the opportunity and embrace renewable energy wholeheartedly to meet its energy needs and build a cleaner future.
Can you walk us through the workings of data science, big data and machine learning?
Data science, like any type of science, includes the use of different techniques and methodologies to deduce knowledge, only this time we are getting the information from data. Simply put, a data scientist can be likened to a detective who sifts through evidence (data) to uncover hidden truths. And, big data, as the name implies, refers to a vast amount of data in different raw formats. Big data serves as the foundational material upon which data scientists apply different computational techniques to extract valuable insights and meaningful information. Machine learning is a fundamental component of data science as it provides the different computation techniques a data scientist can use to uncover information hidden in big data.
To succinctly explain the relationship between these concepts, data scientists use machine learning to make sense of big data. Just as humans can learn through experience, machines can also learn and execute typical commands such as classifying the data, identifying patterns within the data and developing web applications that can make predictions based on historical data.
In some quarters, both machine learning and artificial intelligence are sometimes used interchangeably. But machine learning and artificial intelligence are closely related, but different concepts. First, artificial intelligence is the umbrella term that describes the mechanical simulation of human intelligence processes, machine learning, on the other hand, is only a part of the complex AI system. Machine learning allows computers to learn from data and make predictions without human intervention. AI is the technology that allows machines to mirror different human attributes while machine learning is an artificial intelligence subset that allows machines to be trained using existing data.
In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. Data science, big data and machine learning are intertwined concepts that work together to unlock the value of data, derive insights and drive innovation in various domains. Data science leverages big data and machine learning techniques to analyse large datasets, uncover patterns and make informed decisions, ultimately contributing to the advancement of artificial intelligence and data-driven decision-making.
Did your background in logistics prepare you in any way for your data science journey?
I must mention that all of my previous experiences both in academics and work environment have shaped my ability to interpret and analyse data effectively, identify trends and patterns, and derive actionable insights – all of which are essential components of data science. So, yes, my background in logistics played a crucial role in my decision to venture into data science.
Experience is the best teacher and I can say using data to improve operational efficiency propelled my decision to delve deeper into data science. This made me appreciate the insightful nature of data and realise the immense potential of using data-driven approaches to find solutions to recurring business problems and improve service delivery.
Working in logistics contributed to my professional development as it exposed me to different data analysis techniques and tools, which laid the foundation for my interest in data science. It also instilled in me a curiosity and desire to understand the field of data analytics and explore how data can be used to make informed decisions. For me, I believe learning is a continuous process; I always try to learn and develop myself in any position I find myself and this has proven to be beneficial in my data science journey.
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