In an economy where data functions as a core business asset, translating numerical information into actionable strategy is a key differentiator for industry leaders. This shift requires professionals who can connect financial principles with advanced analytics. Daniel Akpan, a business analyst with consulting experience across Africa and the United Kingdom, operates at this intersection. With a background in accounting and an MBA from the University of Warwick, Akpan has focused his career on digital transformation and financial innovation. His work addresses the technical and cultural components of building data-centric organizations by implementing systems designed to produce measurable value. A Data-driven Career Path The transition into data analytics often stems from a foundational insight into its practical applications. Akpan’s career was shaped by an early understanding of the narrative power held within financial data, seeing numbers as a story about an organization's performance and potential. “My interest in data and analytics was sparked early in my career when I realised that behind every financial report or business strategy lies a story told by data,” Akpan says. “I became fascinated by how numbers, when analysed correctly, could uncover inefficiencies, predict trends, and influence critical decisions.” This perspective led him to pursue roles combining finance and technology. “This passion has shaped my career by steering me toward roles that combine finance, business analysis, and digital transformation—ultimately allowing me to champion data-driven cultures across organisations,” he adds. This skill set aligns with the core competencies outlined by the Business Analysis Body of Knowledge (BABOK®) and provides a roadmap for professionals transitioning into the field. competencies outlined by the Business Analysis Body of Knowledge (BABOK®) roadmap for professionals transitioning Improving Data Quality Data inconsistency is a common obstacle in large organizations, hindering reliable decision-making. Akpan addressed this challenge during a project focused on harmonizing reporting standards across several business units, demonstrating how disciplined data management can yield significant operational improvements. “The organisation faced inconsistent data sources and duplicated records that undermined decision-making,” Akpan explains. His team implemented a centralized analytics platform with embedded governance rules and validation checks. “For the business, this translated into faster month-end closes, more reliable forecasts, and stronger confidence in strategic decisions,” he notes, adding, “It was a powerful demonstration of how disciplined analytics can create real value.” This 63% improvement reflects better adherence to the core dimensions of data quality and shows a clear return on investment from such initiatives. dimensions of data quality return on investment Overcoming Data Adoption Barriers Successful digital transformation extends beyond technology implementation to address cultural and procedural challenges. Resistance to change, siloed information, and the absence of a clear strategy are often the most significant barriers to becoming a data-driven organization. “The biggest barriers are cultural resistance, siloed data, and a lack of a clear strategy,” Akpan states. “Many organisations invest in technology but overlook the human and process elements.” His approach is to foster data literacy, encourage cross-functional collaboration, and connect analytics projects to specific business goals. He adds, “By demonstrating quick wins—such as automating manual reports or improving KPIs—I help build trust and momentum toward a sustainable data-driven culture.” Such a strategy depends on clear objectives, which can be measured using established data governance metrics and KPIs and evaluated with frameworks like the PROFIT framework. data governance metrics and KPIs PROFIT framework Balancing Accuracy and Usability For data analysis to drive action, it must be both rigorous and accessible. Technical accuracy without a clear, practical presentation limits the impact of insights, as stakeholders may be unable to interpret or apply the findings effectively. “I always start with the end-user in mind,” Akpan says. “Technical accuracy is non-negotiable, but if insights cannot be understood or applied, they lose impact.” He designs solutions that are methodologically sound yet intuitive, using validation techniques alongside visualization tools. “This dual focus ensures that stakeholders not only trust the data but also act on it,” he says. This approach is critical for delivering value, a concept central to models like the Data ROI Pyramid, which emphasizes the importance of usable data products and avoiding the high cost of data downtime. Data ROI Pyramid cost of data downtime Democratizing Global Data Skills Observing a recurring skills gap among professionals, Akpan authored ebooks on SQL and data analysis to make technical knowledge more accessible. The resources aim to demystify complex concepts and provide practical guidance for those looking to build their data capabilities. “The motivation came from recognising a recurring skills gap,” he explains. “Many professionals wanted to leverage data but lacked accessible, practical learning resources.” The feedback has highlighted the global demand for these skills. “Readers from different continents have shared how these resources helped them secure new roles, complete projects with confidence, and transition into data-focused careers,” Akpan notes. Developing proficiency with tools like SQL and Python is a key component of the business analyst skills needed to thrive in the modern economy, with clear ROI frameworks for data analytics projects justifying the investment. business analyst skills needed to thrive ROI frameworks for data analytics projects Data Modeling Influences Strategy Precise data modeling can significantly influence strategic financial decisions. Akpan's work on a project for a multinational corporation involved modeling cash flow scenarios using harmonized data from multiple subsidiaries, which uncovered previously understated financial risks. “The enhanced accuracy revealed liquidity risks that had been previously understated,” Akpan recalls. This finding directly impacted executive decisions. “This insight influenced the leadership team to renegotiate supplier terms and adjust treasury strategies, ultimately safeguarding millions in working capital,” he explains. “It was a clear example of how robust data modelling can directly shape strategic business outcomes.” Such work often involves advanced methods like driver-based modeling and Monte Carlo simulations to manage financial forecasts effectively. driver-based modeling Monte Carlo simulations Evolving Business Analyst Role The function of a business analyst is expanding as data becomes more integral to organizational strategy. The role is shifting from a primary focus on requirements gathering to a more strategic position that leverages analytics to foster innovation. “The Business Analyst role is evolving from being primarily a requirements gatherer to becoming a strategic enabler of analytics and innovation,” Akpan says. He notes that analysts must now blend traditional skills with competencies in data literacy and predictive insights. “I see Business Analysts acting as translators between technical teams and decision-makers, ensuring that analytics not only answers business questions but also drives innovation and competitive advantage,” he concludes. This evolution requires mastery of both micro-econometric techniques for program evaluation and strategic liquidity management principles. micro-econometric techniques strategic liquidity management Harnessing Emerging Data Trends The convergence of artificial intelligence, machine learning, and real-time analytics continues to create new opportunities for businesses to manage risk and optimize operations. Adopting these technologies responsibly is key to harnessing their full potential. “I am particularly excited by the convergence of AI, machine learning, and real-time analytics,” Akpan states. “These technologies are redefining how businesses anticipate customer needs, manage risk, and optimise operations.” While acknowledging challenges like model hallucinations, a documented risk in generative AI, he advocates for a measured approach guided by frameworks like the EqualAI Algorithmic Impact Assessment (AIA). “In my future work, I plan to integrate these trends by building scalable analytics platforms, fostering AI adoption responsibly, and equipping professionals with the skills to harness these innovations effectively,” he adds. This requires a strong foundation in AI governance implementation. risk in generative AI EqualAI Algorithmic Impact Assessment (AIA) AI governance implementation Akpan’s career demonstrates how combining analytical expertise with business knowledge is crucial for organizational progress. By applying robust analytical frameworks and promoting data literacy, he helps businesses navigate digital complexities and find new avenues for innovation.

This story contains new, firsthand information uncovered by the writer.

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How Daniel Akpan Uses Data to Drive Business Transformation

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