The buzzword big data is still less strongly associated with small and medium-sized companies. The espionage and data scandals of the last decade have placed big data primarily in relation to multinational corporations such as Google, Facebook (now Meta) and Co. However, big data has become increasingly differentiated in recent years. This is because the implementation of digital tools, digital processes and digital technologies in all areas of society that goes hand in hand with digitalization means that more and more data is being generated, which is being combined to form big data and specialized AI-supported and machine learning-based analysis methods and processes.

From big to bigger data

Big data is a data complex that is generated by data in a wide variety of contexts. Big data includes various forms of data, including, for example, unstructured film, image, video and sound files, semi-structured data that is evaluated in the form of data visualizations and structured data in the form of tables, maps and lists.

Big data is generated by bringing together a wide variety of data. These originate from systems that are usually used in large companies, such as:

• Enterprise Resource Planning (ERP)
• Customer Relationship Management (CRM)
• Supply Chain Management (SCM) and other systematized forms of data collection in relation to production, distribution and sales processes
• communication via social networks such as Meta, Twitter, YouTube or WeChat
• the constant use of mobile devices, with cell phones being particularly important - for a global view of the big data complex - as they are the only way for people in countries in the Global South to access the Internet and the World Wide Web
• the IoT (the Internet of Things), whereby things communicate with each other and with people using sensor-based ecosystems
• through open data on weather, traffic, through the provision of maps in open source format

The coronavirus pandemic has also meant that virtual forms of communication via video conferencing or digital applications such as health apps are now an integral part of our everyday lives. As all these data-producing devices and services become firmly anchored, more and more data is being generated in different formats. Scientists are therefore already talking about the future of big data: bigger data. Even more than Big Data, Bigger Data poses the much more urgent question of how knowledge can be generated from the mass of data produced in the context of Big Data.  

This question requires science, business and politics to engage intensively with big data analysis methods. The new concepts and approaches that are already being tested here in order to move from big data to “smart data” are particularly evident in the SME sector.  

Big data doesn't just mean huge amounts of data

Big data concerns the question of how value is created from data. As a 2021 study commissioned by DELL Technologies shows, the biggest challenge in the field of big data is to generate economically usable (data) value from the data volumes in the first place. This is why the use of evaluation, analysis, visualization and visualization and interpretation methods has become increasingly important. Especially as more and more SMEs have large amounts of data on internal and external company processes. As a result, the demand for and importance of big data analysis methods, IoT analytics, data sharing practices and the use of specialists such as data scientists and big data engineers is also growing significantly in SMEs.

SICOS BW GmbH, which has been based in Stuttgart since 2011, has taken on this task for Baden-Württemberg. It specializes in the use of big data analysis processes and the use of AI methods in SMEs. SICOS BW GmbH was founded by the Karlsruhe Institute of Technology (KIT) and the University of Stuttgart to provide SMEs with access to simulation technologies and high-performance computing systems and thus support the transformation process from big to smart data.  

SICOS BW GmbH works closely with the Smart Data Solution Center Baden-Württemberg (https://www.sdsc-bw.de/) to strengthen the competitiveness of SMEs based in Baden-Württemberg through the use of data analysis. To improve the implementation of big data analysis processes and tools in SMEs, sdsc-bw offers potential analyses, arranges partner networks and further training formats, provides advice on applying for funding projects and sample analyses or creates individual offers. 

How can and do SMEs want to make decisions in the future?

The main focus for SMEs is currently on providing and deepening skills in the use of big data analysis and its benefits for employees. Against this backdrop, sdcs-bw serves as an ideal example because the focus here is primarily on the exchange of expertise and networking activities between providers and potential partners from business and research.

As there are numerous data analysis practices that work on the basis of artificial intelligence, machine learning, cognitive computing or the use of neural networks, it is also clear that big data has above all also initiated a transformation: It is significantly changing the way decisions are made in companies. This also means that the basis for these decisions has become much more complex and difficult, as it is more data-driven and therefore more technology-, competence- and analysis-intensive.  

If big data analysis methods are integrated into the everyday life, processes and strategy of SMEs, the way in which SMEs produce knowledge, evaluate information and ultimately make and defend decisions based on this information will change.  And in every respect - in relation to partners, service providers, clients, potential investors and their own customers. 

The advantages of this form of decision-making based on big data are numerous: big data promises added value in the areas of resource planning, accelerating decision-making processes, increasing sales and developing new, innovative and sustainable business models. The use of data analysis methods also leads to positive cumulative effects. Above all, the analyses increase the transparency of decision-making processes, making it possible to identify correlations as well as positive or negative dependencies within and outside the company.

Big-Data-as-a-Service as the presence of Big Data

A notable trend in the field of big data and SMEs is currently the use of as-a-service models (aaS models) - and specifically the use of big data as a service platforms and services. Many companies are turning to them because processing data requires high storage capacities and storing data is costly and infrastructure-intensive. As four fifths of all companies do not have this capacity and no suitable computer and data centers are available, more and more companies are outsourcing the storage, processing and analysis of their data to aaS models.  

This is also the conclusion of the study “Big Data – Big Chance für klein- und mittelständische Unternehmen” published in May 2022. Although it confirms that the potential of big data is primarily used in companies with more than 500 employees, it also shows that the use of big data as a service improves the analysis of available data for 76% of the SMEs surveyed as a first step. Without having to provide all the necessary capacities and resources for processing (including storage, classification and formatting, calculation, analysis and evaluation through interpretation) themselves, SMEs can use big data for themselves thanks to a-a-S offerings. 

The shift to big data as a service models is also linked to the idea of increasing the agility, scalability and efficiency of companies. However, the outsourcing of analysis capacities to improve data quality also goes hand in hand with the realization that more needs to be done to develop the data skills of all employees. The results of multi-cloud and as-a-service models can only be coordinated and applied in relation to the respective contexts and environments of SMEs if the focus is on building the expertise of all those involved.  

Differentiated data analysis methods that meet the needs of SMEs

In addition to the integration of advanced analytics thinking, data analyses for sustainability purposes and the full-scale production of metadata analyses, the agenda for the coming years will continue to focus on increasing the data skills of all employees in the company.  

With regard to big data, for example, it is important to reflect on the data sets generated in SMEs in the context of more comprehensive logics when evaluating data and to learn to specifically assess the usefulness and suitability of the analysis methods used. Data-competent work, technology, administration, marketing and management services are required - a goal that will not be possible without further differentiation of the analysis processes, techniques and methods used to date.  

Particularly as big data also encompasses many different types of data, it is particularly important to find out whether the applications provided really fit the use cases. As it is also known that decisions are both a cornerstone for success, growth and innovation as well as a factor for risk and uncertainty, it is important for SMEs to monitor decision-making parameters and check the extent to which big data improves the basis for decision-making and makes it more future-oriented, more cost-effective, more transparent, less hierarchical, less discriminatory, more comprehensible or more fact-based.