Old industry, like metallurgy and mining, are not the typical examples of successful digital transformation because the related business models are extremely stable, even in the era of hyper-innovation. At least this is what some CEOs believe, and it’s partly true, because for some branches, there is no burning platform for digitisation, and hence, the change process is inert. Then, the level of digitisation is stagnates, together with data strategy development and implementation. Data scientists can only generate value added for the company, if there is enough good quality data for the projects. Therefore, it is also the job of data scientist to contribute to the digital transformation in her/his company and provide successful high impact projects. “Low hanging fruits”, e.g. KPI, reports, explorative analysis, are inevitable to build up trust, to establish and promote data science.
Key Takeaways
- Data science is not possible without successful digital transformation but Digital transformation has to be encouraged and supported by data science
- The availability of data, especially availability of data in acceptable quality is the biggest challenge. Therefore the old industry has to catch up and adopt new technology
- “Low hanging fruits” (e.g. KPI, reports, explorative analysis) are inevitable to build up trust, to establish and promote data science
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