Ms Mordvinova, what is Data Engineering - and what does it promise for medium-sized companies?
Olga Mordvinova: Data engineering is primarily understood as the storage, processing and provision of data. It also includes the selection of the right systems for data storage, the optimization of algorithms for data handling, data quality, data protection and data security. The aim is to create a sustainable basis for making existing data quickly and purposefully usable with the help of intelligent systems. In other words: Data Engineering is a foundation without which neither intelligent analyses nor applications and business models based on them can be built.
A well thought-out strategy for data handling also provides concrete added value for SMEs and is one of the most important steps on the way to digitisation. By mapping the model of the company and its business processes, this semantics can be used for further intelligent analysis, including Machine Learning. For example, scenarios such as the monitoring of product quality or the predictive maintenance of a production machine can be realized with relatively small amounts of process data, if the manufacturing process is mapped semantically. Mass data from sensors - such as information about temperature deviations - can also be correctly expressed by process data and help to make decisions quickly and effectively in the context of production and maintenance. Good data engineering is valuable for optimizing business processes and can pave the way for new business models.
Which applications become possible only through intelligent data analysis?
Olga Mordvinova: The areas of application are diverse and range from assistance systems and customer segmentation to infrastructure monitoring and planning optimization. We at incontext.technology offer applications for the analysis and early detection of malfunctions and defects in machines, plants and industrial infrastructures. In the so-called predictive maintenance we use Machine Learning to optimize the effectiveness of the plants. By preventing unplanned machine downtime in production, plant utilization can be improved, productivity increased and delivery reliability to the customer guaranteed. Another example is the condition monitoring of critical infrastructure such as railway tracks, including the detection of deviations from standards and the preparation of forecasts regarding the service life.
How must an SME proceed in order to use Data Engineering successfully?
Olga Mordvinova: As with all business activities, it makes sense to define clear goals and expectations. Data collected without purpose has hardly any added value, and high, unfulfilled hopes can slow down digitization and especially the introduction of intelligent technologies. Another important point is clarity about in-house competencies and the future strategic direction of the company, which should best be in line with initial practical experience: To what extent does it make sense to obtain know-how from outside and which competencies should be built up within the company itself? Open Innovation could be particularly suitable for small companies. This can take place in cooperation with similar companies or in partnerships with suitable technology companies.