Data analytics methods

Data – the oil of the new millennium?

Data are often described as the raw material of the future. However, in contrast to natural resources such as crude oil, there is no prospect of scarcity where data are concerned: rather, new technologies and methods for sensor-assisted data collection, transmission of data to the Internet of Things, data storage, data analysis and process integration in the corresponding systems mean that our ability to collect this raw material is growing by the day. As a result, data repositories and streams are multiplying in both size and number – in business environments and beyond.

The challenge of big data

Because it has become so easy, most data today are collected and stored with no particular problem or analysis question in mind. Moreover, there is often uncertainty among companies over strategic questions about the economic value of data, in particular:

Which data are of relevance to future analyses, and which will actually be required for concrete applications? and
Which data does the company have at its disposal, and in what form?

Accordingly, companies prefer to store too much information rather than too little.

Consequently, a sustainable and efficient approach to the raw material data is not just a matter of economic importance, it is also a social issue – just like the sparing use of other resources.

This indiscriminate accumulation of data is becoming a real economic and social challenge. The problem is not just that many companies do not know how to deal with the seemingly unmanageable flood of data. Storing and processing all this information is also expensive, besides causing unnecessarily high energy consumption and associated carbon emissions – a waste of resources with consequences for the economy, the environment and, therefore, future generations.

Making the right decisions with the right data

In an increasingly digital world, companies will be able to achieve ever greater efficiency in processing the raw material data in pursuit of their goals. To this end, they need:

the right mathematical, statistical, management and economic methods,
increased automation in processing
strategic knowledge of which data are relevant to the company now and in the future.

The goal must no longer be to simply collect the largest possible amount of data for potential future processing. Instead, it must be to single out only the most important data sources, which offer real benefits and generate economic value – both now and in the future. Besides process-related and domain-specific technical aspects, the statistical and mathematical relevance of the data is an important factor in this decision.

Data analytics is key

Distilling extensive and unstructured datasets into fewer but higher-quality data of strategic, tactical and operational relevance to companies, offering concrete long-term value and benefits, does not depend solely on improved technology or sensors for the collection, transmission and integration of data. In order for the information collected to be leveraged in concrete applications – i.e., to turn big data into smart data – it must be processed, enriched with additional relevant information (in some cases from external sources), and analyzed.

This significant field – data analytics – holds enormous potential. Advances in algorithmics in recent decades have surpassed those in the field of hardware by an order of magnitude as far as solving times are concerned. As a result, mathematical problems that would have required 75 years of processing time a little over two decades ago can now be solved in a single second.

Analytics research at the boundary of what is currently possible

Accordingly, our research seeks to continually improve analytics methods and processes in the field of artificial intelligence, pushing the boundaries of what is currently possible. As a result, appropriate filters and continually refined automated analytics methods will, in future, make it possible to analyze, optimize and act upon larger volumes of relevant data faster.

In this way, seemingly unmanageable amounts of data and related material will be transformed into manageable, high-quality data and information; in other words, the right data, with concrete economic benefits.

How we extract the assets in your data

Data holds assets that are of immense value to companies. These assets must be extracted and given shape – with the help of cutting-edge data analytics methods. With this goal in mind, we continually select, develop and improve the necessary tools to equip companies to deal properly with their internal and external data.

The guiding framework for our research is the SCS Reference Process for Digital Transformation, which reflects the life cycle of data. Based on this framework, we provide our clients with customized methods and data that we find in markets and processes, and then filter, interpret, and analyze. The data are then fed into forecasting, simulation and decision-making models and optimized, in some cases in collaboration with software companies and service providers.

In methodological terms, data analytics comprises three hierarchically related areas: descriptive analytics, predictive analytics and prescriptive analytics. Visit our department website to find our more, and see which methods and processes we can use to identify your internal process data and analytics applications, enrich them with external market data, and support your decision-making with the help of data science and mathematical optimization.

The life cycle of data

The guiding framework for our research is our Reference Process for Digital Transformation, which reflects the life cycle of data.

Data analytics in the data-driven business processes

Data are created throughout the entire business process, and can therefore be used for a wide variety of applications – from strategic issues to improvement of processes. However, for data analytics to yield meaningful results it must always be anchored in a concrete application, i.e., an analysis question informed by a specific goal. Only then can knowledge be generated in the form of data, applied and integrated into the decision-making process as guidelines. You can find out more about how the life cycle of data interacts with that of the business process in our Reference Process for Digital Transformation.

To the reference process