How to apply data science in business?

Data science in practice

In the age of information, the field of data science has emerged as a powerhouse, seamlessly blending mathematical expertise, statistical analysis, specialized programming, and cutting-edge technologies such as artificial intelligence (AI) and machine learning. This multidisciplinary approach aims to unveil hidden gems within vast datasets, providing organizations with actionable insights that drive informed decision-making and strategic planning. Data science stands as the key to unlocking actionable intelligence, making it an integral component for organisations seeking to thrive in the data-driven future.

Data science has rapidly become one of the fastest-growing fields across industries, fuelled by the ever-accelerating volume of data from diverse sources. The role of a data scientist, coined the "sexiest job of the 21st century" by Harvard Business Review, has become indispensable. Organizations now rely on these professionals to interpret complex data sets and deliver recommendations that enhance business outcomes.

The data science lifecycle navigates through various stages, each essential for extracting actionable insights. The journey begins with data ingestion, encompassing the collection of raw structured and unstructured data from diverse sources using methods such as manual entry, web scraping, and real-time streaming from systems and devices. The sources range from structured customer data to unstructured elements like log files, video, audio, images, the Internet of Things (IoT), and social media.

The work of data scientist involves a lot of coding to manipulate raw data. This transformative process sets the stage for creating predictions, classifications, and other solutions using machine learning models. The hands-on approach to data manipulation forms the foundation for generating valuable insights.

Applications of data science

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.

5 essential skills for data scientist

Matti shares his insights on five essential skills for data scientist.

Be ready to take the leap into the unknown

Matti emphasizes the need for data scientists to shed old habits and swiftly embrace new paradigms that offer superior solutions. Describing it as a "leap into the unknown," he stresses the importance of moving quickly from outdated methodologies to contemporary approaches, a mindset that characterizes his approach to the ever-evolving field of data science.
Stay curious – and stay hungry for continuous learning and skill development

Matti has advice against getting stuck when faced with unfamiliar tasks, encouraging self-study as a means to bridge knowledge gaps. According to him, the key lies in not just relying on existing know-how but actively building new skills on top of it, creating a dynamic and adaptable skill set.
Do not forget to the basics – keep your essential hard skills up to date

Matti identifies mathematics, programming, and statistics as the foundational hard skills crucial for success in data science.
Apply proven practices and follow software development principles

Drawing parallels between software development and data science, Matti underscores the significance of applying proven practices from the former to the latter. Acknowledging the maturity of software development as a field, he advocates incorporating its principles into data science workflows, emphasizing the importance of good software development practices in coding for effective and scalable data solutions.
Keep your customer on the same page - communication is the bridge to success

Matti recognizes effective communication as a cornerstone of success for data scientists. He stresses the importance of aligning with customers, emphasizing the need to establish a clear consensus on project objectives, methodologies, and expected benefits. This collaborative approach ensures that the end results not only meet technical standards but also align with the broader goals and expectations of the stakeholders.

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Automating receipts accounting with Machine Learning. We streamlined the accounting of Ukko’s customers’ receipts using Machine Learning. The automatisation resulted in time and cost savings, as well as freed a team of accountants for other essential tasks.

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Reducing tractors warranty costs with Machine Learning. As a major part of their product development process, Valtra the leading tractor manufacturer in the Nordics, wanted to cut field costs related to tractor warranty claims. We at Advian responded with a Machine Learning solution, that gives Valtra a part-by-part prediction of warranty costs.

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Advian helped us to in matter of couple of months turn a manual process into automated and thereby save us significant costs.

We succeeded with the project by working together.

We were pleased that Advian had the expertise and vision to take overall responsibility for controlling the data conversion.

Working together with Advian has been smooth and straightforward, leaving the customer feeling good.

We received valuable information about new hidden soft grounds and knowledge of the use of satellite data.

We would not have had any chance to get such a large number of skilled people for the project just with our resources.

Advian’s team complemented our own experts by providing their own experience in the energy markets KPI development and more technical areas like MS BI development, data engineering, and quality assurance.

Advian was able to find the right people and experts quickly.

Cooperation and assembling the team with Advian was the best option for us.

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Advian understood the district heating customer onboarding processes and was able to build a quick solution that ensures that the process is now structured and works smoothly.

Flexibility according to the customer's needs has been a good characteristic of this team.

Advian's leak analytics has proven very useful and has enabled us to find, fix and prevent leaks in our district heating network. Sustainability and keeping our network well maintained are priorities for us.

Looking for a place to grow as a data professional?

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If you want to grow, develop, and enjoy your work, we at Advian are committed to making it possible – together.

Typically, we introduce ourselves with the following titles: Data Advisor, Data Architect, Data Engineer, Data Scientist tai Machine Learning Engineer. We are always welcoming new talent to learn and grow with us.

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Data Science

How to apply data science in business?

Data science in practice

What is data science in practice?

Applications of data science

How to succeed as a data scientist?

5 essential skills for data scientist

Read more about our machine learning and advanced analytics projects

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