Data professionals play a pivotal role in leveraging information to enhance decision-making processes. Data scientists work in a diverse range of projects, focusing on mathematical modelling, machine learning, and artificial intelligence (AI).
The data analysis phase is where data scientists embark on exploratory data analysis, scrutinizing biases, patterns, ranges, and distributions within the dataset. This exploration forms the basis for hypothesis generation and a/b testing, guiding the relevance of the data for predictive analytics, machine learning, and deep learning models. The accuracy of these models establishes their reliability for driving scalable insights and influencing business decision-making.
Communication is the final crucial step, where insights are transformed into reports and data visualisations. Leveraging data science programming languages like R or Python, or dedicated visualisation tools, data scientists present findings in a format that resonates with business analysts and decision-makers. This streamlined process ensures that the impact of data-driven insights is clearly communicated, fostering a comprehensive understanding among stakeholders.
Advian’s Senior Data Scientist Matti Karppanen acknowledges the necessity of abandoning outdated methods and swiftly embracing new paradigms and approaches. This flexibility ensures that data scientists stay ahead of the curve and continue to deliver optimal solutions to evolving challenges. Successful data scientists have strong technical prowess but also for their commitment to continuous learning and adaptability keep them busy.
Case 1: Predictive Maintenance in Heavy-Duty Machinery
One of Matti's long-term projects involves heavy-duty machinery equipped with sensors collecting IoT data at a staggering rate of every ten seconds. With 100 sensors per machine, the goal is to predict when a part might break down. This predictive maintenance strategy not only minimises downtime but also allows for forecasting warranty costs for different machine parts, optimising maintenance budgets for manufacturers.
Case 2: Customer Churn Predictions
In another intriguing application, Matti applies his expertise to customer churn predictions. By analysing customer data, he identifies potential churn risks, aiding businesses in proactive customer retention strategies. This involves understanding patterns and behaviours to predict which customers are at risk of leaving, ultimately empowering businesses to take preventive measures.