DE 
Dear Mina, you have made Data Literacy Day 2021 from StackFuel participated in the panel discussion and presented convincing arguments as to why data literacy is indispensable in today's world. Are there exceptions or is "data literacy for all" unstoppable?
Data literacy is not much different from acquiring a driver's license: The knowledge of how to analyze, interpret and communicate data can in principle be transferred to all vehicles or data technologies.
It doesn't always have to be the Porsche, the Volkswagen will do as long as we are transported from A to B. In this case, that means that not every person in the organization needs to be capable of SQL, Python, or R for data analysis. An easy-to-use no-coding platform can make it easier for non-technical employees to be data-informed.
Does every:r have to be data literate? Not necessarily, since there are also people who decide against acquiring a driver's license for financial, environmental or other reasons. It's the same with acquiring data literacy: If I work in construction or at the cash register, for example, it is not obvious for me to deal with it.
The situation is different when I work in the area of finance or HR, where I often deal with the development of financial or HR key figures in Excel. And at some point I reach a point where the Excel spreadsheet takes me forever or even crashes due to the amount of data - and I then ask myself the question: How can I deal with large amounts of data more efficiently? That's when data literacy, upskilling and reskilling come into play.
If we look to the future, data and data skills will become increasingly important, here in Germany as well. What is your vision of a German data culture in 15 years?
I don't believe in a German data culture, because the field of Big Data analytics and artificial intelligence are not geographically bound to one place.
Germany is an industrialized nation where the classic career is always viewed in a linear progression. You are expected to simply move up one stage at a time and only rise within corporate hierarchies if you bring the right degrees from the right universities.
A person with a liberal arts or humanities degree who teaches themselves to program has a very difficult time. Even if these individuals are truly passionate about working in tech, they often don't even get a chance to interview because they don't have a degree in computer science, business information systems, or science. In addition, informal qualifications such as bootcamps or online courses are still not recognized across the board.
The high demands thus inhibit entry, which we are also seeing now in the pandemic. It is particularly difficult for the younger generation to gain a foothold in the job market because companies often demand several years of work experience.
Therefore, I advocate a recognition system for informal qualifications as well as more flexibility in hiring processes on the part of employers.
How must the new federal government respond to the changing need for data skills to support this evolution?
The German government is already slowly moving in the right direction. I think it is important and right that even more is being invested in artificial intelligence (AI) and that education policy is being expanded to this end. For example, the German government increased the federal government's investment in AI from three to five billion euros by 2025. In addition, the federal and state governments announced at the beginning of 2021 that they would invest 133 million euros to promote teaching about and with AI at universities.
In this way, Germany is pursuing one goal above all: artificial intelligence is to make us a major driver of innovation in Europe and ensure that we can hold our own in competition - against countries like China, India or the USA. That's all welcome, but I'm still not really satisfied.
More measures must be introduced in the area of education and labor policy, and the topic must continue to be prioritized. For example, teaching at educational institutions must change for the digital future. For example, mathematics classes should not only teach statistics, but also how to use statistical tools. Universities should also use programming languages and software in courses that are relevant to the labor market.
How must schooling and corporate education change today to lay the important first foundations for a data literate future?
I support the Demand of the KI Federal Association introduce the school subject "data science" as a compulsory subject from the third grade onwards.
Currently, a lot is being said about robotics or the programming of websites, or it is being explained in computer science lessons: What is hardware and what is software? In data science, children and young people should rather be taught when we are confronted with data. For example, from the moment you use an app and click through, data is collected in the background.
I think it's important that young people also understand: How does the shopping site manage to show me exactly the clothes I like? Or why can a dating app possibly find the love of my life? Students should understand the massive influence that data and algorithms have on their everyday lives.
Why does the tech and data industry need to rely more on lateral hires now and in the future, and how open is the industry to a breath of fresh air?
Currently, Germany still has a shortage of AI specialists. And it is precisely this shortage of specialists that is preventing a large number of companies from launching projects in the fields of AI and big data.
In a IDC study from 2018, more than half of the companies stated that they were unable to implement projects due to a lack of experts. More than 80 percent of the companies have no experts in this area.
Due to technological change and a shortage of skilled workers, we need more lateral entrants who think outside the box and react flexibly to changes.
Unfortunately, a lateral entrant still has a very difficult time. More difficult than he or she should have it. Even if these people are really passionate about work in the tech sector want, they often don't even get the chance for an interview because they don't have a degree in computer science, business informatics or natural sciences, for example. In addition, informal qualifications such as bootcamps or online courses are still not recognized by everyone.
I therefore wish that companies would not only hire according to degrees and formal qualifications, but rather also give people a chance who don't have a "perfect" resume, but who dare to take the plunge. Because these people are willing to take new paths and have the intrinsic motivation to teach themselves new skills. Especially in the age of digitization, we need more people who really live the principle of lifelong learning.
What do you think is important about an interdisciplinary exchange like the DLD and what special learnings did you take away from the conference?
The most important learning for me was also to see how the topic is put on the agenda and ultimately implemented in the executive suites of companies. And also that technological change is always accompanied by emotions - fear of the unknown or anticipation of the new. And these emotions must be addressed by decision-makers in politics, business and society. Without the right culture, communication and collaboration, we will not achieve a successful digital transformation.
Why was it important for you and your mission to be at DLD2021?
We should democratize the understanding of data and AI because it is crucial for the future and the future belongs to all of us. And that's exactly why it was so important for me to be at DLD 2021, to bring this mission closer to more people.
Thank you Mina for sharing the interview and for taking the time!
(Pictures by Julia Steinigeweg)
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