How We Work

How We Work

New Era

The era of democratization of data and AI has begun. What does this mean? The democratization of data and AI refers to the process of making data and AI more accessible to a wider range of people and organizations. It is an attempt to make data and AI more democratic by making it available to everyone, regardless of their technical expertise. For companies, this trend brings not only new opportunities, but also responsibility. Now is the time to actively engage with this technology.

How Do We Apply Data Science?

Data science is a multidisciplinary field that involves the use of statistical and computational methods to extract insights from data. It encompasses a wide range of techniques, including data mining or descriptive analytics, predictive analytics, machine learning and artificial intelligence.

Descriptive Analytics

Descriptive analytics utilizes current and historical data with statistical methods to identify hidden patterns, relationships, and contexts. The aim is to extract business-relevant information from data and present it in a comprehensible manner. 

An example of how SIX utilizes descriptive analytics:

We carve out temporal changes in consumer payment behavior by analyzing historical data over consecutive months. See for more details our payment preferences paper.

Predictive Analytics

Predictive analytics is understood as a form of advanced analytics that uses both new and historical data in order to forecast future activity, behavior, and trends. It involves applying diverse statistical techniques, analytical queries, and automated machine learning algorithms to data sets to create predictive models. These models can then project, with a certain degree of accuracy, what may happen in the future. 

An example of how SIX utilizes predictive analytics:

At SIX, we use predictive analytics to anticipate the workload in our customer service centers. By analyzing historical call data and trends, we can predict peak call times and inform our recruiting strategy accordingly. This proactive approach ensures optimal productivity, reduces wait times for customers, and enhances overall service quality.

Machine Learning & AI

Machine learning and AI are two interrelated fields of computer science that aim to create systems that can perform complex tasks, such as analyzing, reasoning, and learning, in a way that mimics human intelligence. Machine learning is a subset of AI that uses data and algorithms to train models that can learn from data and make predictions or classifications. One of the applications of machine learning is pattern recognition, which refers to the ability of machines to identify patterns and regularities in data, such as images, text, or speech. By using prior knowledge or statistical information extracted from the patterns, machines can classify and categorize data into different groups or categories

An example of how SIX utilizes pattern recognition:

One way in which we leverage pattern recognition at SIX is in our Payment Enrichment Services (PES). Our PES enhances the transparency of payment transactions by adding additional information, making it easier to identify and categorize transactions. Thus, raw data is transformed into useful information to create a unique customer experience. Our PES are a testament to our commitment to using advanced technology to help businesses streamline their financial processes and generate compelling added value from their customers' transaction data.

How Does a Data Science Project Look Like?

The goal of data science projects is to find solutions to problems that can be addressed by using data and building data-driven products following a systematic process. During a data science project, several typical iterative steps are performed, such as defining the problem, collecting and preparing the data, building and evaluating models, as well as deploying and monitoring the solution. The figure below illustrates the different steps and corresponding effort.