Interview with Mina Saidze
Dear Mina, you were on the panel at StackFuel’s Data Literacy Day 2021 and presented convincing arguments as to why democratizing data literacy is indispensable in today’s world. Are there exceptions here 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 a Porsche; a Volkswagen will do as long as we are transported from A to B. In this case, it 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 literate.
Does everyone need to be data literate? Not necessarily, as there are also people who choose not to get a driver’s license for financial, environmental or other reasons. It’s the same with acquiring data literacy: For example, if I work in construction or at a cash register, it doesn’t necessarily make sense.
It’s a different story if I work in finance or HR, where I deal with developing financial or HR metrics, often in Excel. And at some point, 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 fields of big data analytics and artificial intelligence are not geographically bound to one place.
Germany is an industrial 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 move up 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 how to program has a very hard 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, information systems, or science. In addition, informal qualifications such as bootcamps or online courses are still not recognized across the board.
As a result, the high requirements 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 labor market because companies often demand several years of work experience.
I therefore advocate a system of recognition of informal qualifications as well as more flexibility in hiring processes on the part of employers. Democratizing data literacy will help us to reform old beliefs.
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, at the beginning of 2021, the federal and state governments announced that they would invest 133 million euros to promote AI teaching at universities.
In this way, Germany is pursuing one goal in particular: to make artificial intelligence a major driver of innovation in Europe and ensure that we can compete – against countries such as China, India or the USA. While this is all welcome, I am still not really satisfied.
More measures need to be introduced in the area of education and labor policy, and the issue needs to 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 democratizing data and creating a data literate future?
I support the demand of the German AI Association to introduce data science as a compulsory school subject from the third grade.
At the moment, a lot is being said about robotics or website programming, or in computer science lessons: What is hardware and what is software? In data science, children and young people should rather be taught at what point we are confronted with data. For example, from the moment you use an app and click through, data is being collected in the background. I think it’s important that young people also understand: How does the online shopping website manage to show me exactly the clothes I like? Or how 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 people changing careers 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 lack of specialists that is preventing a large number of companies from implementing projects in the areas of AI and Big Data.
In an 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 companies do not have experts in this area.
Due to the technological change and shortage of skilled workers, we need more career changers* who think outside the box and react flexibly to changes.
Unfortunately, career changers still have a very hard time. It’s harder than it should be. Even if these people really want to work in the tech sector out of passion, they often don’t even get the chance for an interview because, for example, they don’t have a degree in computer science, business informatics or natural sciences. In addition, informal qualifications such as bootcamps or online courses are still not recognized by everyone.
That’s why I’d like to see companies not only hiring according to degrees and formal qualifications, but also giving a chance to people 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 digitalization, 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 insights did you take away from the conference?
The most important insight for me was also to see how the topic is put on the agenda and ultimately implemented by companies’ executives. 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 the DLD2021?
We should not only democratize data, but 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 the DLD 2021, to bring this mission closer to more people.
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Thank you Mina for sharing this interview on democratizing data literacy and for promoting this important topic!
Picture by Julia Steinigeweg