Nina Perälä from the Nordic Fashion Week Association interviews Dirk Hofmann (Co-founder and CEO of DAIN Studios in Germany) and Majella Clarke (Analytics Strategist). DAIN Studios started up in March 2016 and was founded by Ulla Kruhse-Lehtonen, Saara Hyvönen and Dirk Hofmann. DAIN stands for Digitalization, Artificial Intelligence and INsights.
How was DAIN Studios conceived and what makes DAIN so special?
Dirk Hofmann: DAIN studios was born from the founders’ idea that digitalization is rapidly changing the way we live and there is enormous future potential working with data and developing Artificial Intelligence (AI) to enhance creativity, improve efficiency and support data-driven decision making in business. The three founders, Ulla Kruhse-Lehtonen, Dirk Hofmann and Saara Hyvönen had a collective vision of helping multiple industries discover an emerging dimension of business. At the same time, the founders are also committed to cultivating an internal working culture that fosters diversity, openness and most of all – curiosity.
Majella Clarke: I recently joined DAIN Studios and could see very quickly that this is a company that is responding to the fourth industrial revolution, where digitalization is changing the way we live and do business. It’s a dynamic work environment where you need to learn every day.
What does it mean to be data-driven?
Dirk Hofmann: There are several aspects and impacts of becoming a data-driven organization and making data-driven business decisions. Firstly, being data-driven in decision processes can reduce human bias in decisions – it helps you do things systematically. Secondly, data-driven processes and operations can create transparency within organizations, and that can be used to foster a trusting work environment. Thirdly, when an organization decides to become data-driven, it needs to be supported from the top leadership and management and become a company priority. This requires a well thought out approach to change management, as becoming a data-driven organization is a big change that will eventually affect everyone in that organization.
Majella Clarke: New hardware and software capacities are improving the potential for big data to reshape and change the business environment. For a data-driven organization, we are just at the beginning of an exponential curve of opportunity.
How can data be used to improve efficiency and improve business performance?
Dirk Hofmann: Improving efficiency within a business requires making decisions based on facts, and then being able to systematically reproduce and scale those decisions. When we use human judgement to make decisions in our production and operations processes, we might not be considering all the different factors that are affecting efficiency, or we may have a pre-conceived bias that affects our decision making. In those cases, the actual performance of the decision can be good, bad or variable. You can see many people make good decisions with their intuition, but is it enough when you want to have the same good decisions every day? I think that there is much more potential for efficiency if those decisions are informed by quality data, analytics and insights. And then there is also all the other factors and variables that one human or a team might overlook – and we now have the capacity to use data to complement that.
Let’s take the example of marketing – you want to reach a person with the right message. How can you leverage what you know about their preferences and personalize the content to tailor an offer? Now think about 100,000 or a million people you need to personalize content and tailor an offer. By using data, marketing managers can tailor communication on mass and create personalized content that is designed for more than a million individuals instantaneously. If you can leverage the knowledge and data that you have, it can make a major difference in the success of your sales. We see lots of companies doing just this in their marketing, and this is where the value of data can be easily realized. Of course, we also see ethics and regulations, such as the European Union’s General Data Protection Regulation (GDPR) gain support. This is because data can present enormous opportunity to businesses and needs to be managed like other assets in an organization – with prudence.
Majella Clarke: From an economist’s point of view, I think it is interesting that data has now joined the economic factors of production. For over a century, economists have allocated resources based on land, capital and labor – data was recently added to these core factors of production. This is probably in response to how the fourth industrial revolution is transforming our business models, and those business models are being fed by data – with efficiency and business performance at the core.
Dirk Hofmann: Building on that point, if we take the fashion industry as an example. The conventional business model would play out as follows: a designer creates a shirt, which is produced somewhere in Asia – let’s say 100,000 shirts, and then delivered and distributed around the world to different stores and warehouses. It could be that the shirt is popular in a certain part of the world, and then in another part there is excess supply in shops and they are selling shirts at discounts trying to clear the stock. In the near future, I foresee that type of manufacturing and retail model being highly disrupted.
I think of the case of Adidas, who are one of our partners – they have already operationalized “Speedfactory”. This concept is such that a shoe is co-created and customized. The Speedfactory is automated and capable of efficiently producing limited runs of a customizable product – it can also get shoes to market three times faster than conventional means – and these factories are data-driven, responding to digitalization, and located in Europe and the US. These types of data-driven innovations are going to create immense opportunities for efficiency improvements along the value chain, and the great point about the Adidas SpeedFactory is that they are using parley ocean plastic as input material – it’s great for sustainability too.
What are the pros and cons when transforming into a data-driven organisation?
Dirk Hofmann: The big advantage is that we know the future is digital and data-driven, and therefore understanding how data can drive your business will redefine your competitive advantage. Another advantage is that data can support businesses to effectively and efficiently implement their strategy – data can create a business environment in which you can apply build-measure-learn loops to product development and innovation. By being data-driven, you can quickly understand what works and what does not. The big draw-back comes if you are not innovative or have misconceptions on what data can do for you.
Majella Clarke: Many companies new to the digital transformation process have concerns coming from the cultural change that has to accompany a data-driven process and transformation in a company. There is a famous saying “culture eats strategy for breakfast”. If your company culture does not transform towards a data-driven process, then it will be difficult to meet objectives and targets within a strategy.
What type of transformation do you get to see in your clients?
Dirk Hofmann: It starts with a realization from the client’s side – digitalization is impacting their industry and redrawing the business competition. Typically, the digital transformation process commences with the formulation of a digital strategy. It is fundamental that the strategy is tied closely to the company vision, and therefore the strategy process is often led by senior management and the CEO.
Transformation processes can also be catalyzed when companies ask: what is the value of our data? How can we use it to improve performance? How can analytics help us drive better business decisions? At DAIN, we start by understanding the analytics maturity within a company and map the current skills, data capacities and data enablers. Also, at the moment, privacy and security of personal data are big topics, and we help our clients meet the expectations of both customers and regulators.
Naturally, when you can add value to your client’s business there is a sense of achievement and partnership.
What are the main challenges your clients are facing when implementing transformation?
Dirk Hofmann: The fabric of decision making is transformed when a company moves from relying on judgement in decision making, to using data to inform decisions. So, the first challenge is change management. Changing human behavior is usually a long process inside a large organization, so the urgency for change needs to be communicated with clarity.
Majella Clarke: Industry 4.0 and its new business models require new skill sets within an organization. The main questions many companies are asking at the moment are what type of skillsets are required? and where do we get these skillsets from? At the moment, there is a deficiency of skillsets that are unique to the fourth industrial revolution – data scientists are probably the best example.
Data Scientists typically have programming skills, math skills, hacking skills and statistical skills – a lot of skill in one person. This is creating a massive challenge for companies as a whole, because on one hand, the people coming out of universities, the millennials, have grown up with digital technology, so they have quite a lot of these skills. On the other hand, companies need to quickly invest in further developing the skill sets of their Science, Technology, Engineering and Math (STEM) professionals, so that they too can continue to contribute to the evolving needs of the labor market. The challenge lies in human resource management to acquire and nurture talent at the same time fostering a company culture of life-long learning.
What is KAMU AI and how is it special?
Dirk Hofmann: As founders of DAIN, we were always keen to start a company that would not only be service driven, but also develop and innovate our own products. KAMU, which means “buddy” in Finnish, is our first AI product. KAMU is an artificial intelligence platform used for the delivery of travel recommendations. KAMU uses multiple sources of data such as reviews, facts on destinations and consumer preferences to generate hotel recommendations that focus on price and location.
What is the biggest opportunity for data in fashion and what is the best way to apply it into fashion?
Dirk Hofmann: Coming from a background in innovation, I get excited about the possibilities in augmented creativity when using AI in the design process. I know that there are people that question the role of AI in creative and design processes, but I see AI as something that can be used by designers to enhance the creative process. Data and AI can be used to make fashion accessible and personalized, like the Adidas case we discussed above.
Majella Clarke: Digitalization within the circular economy will provide lots of opportunities to improve the sustainability of the fashion industry. In the last two years a number of circular economy initiatives have been launched and directed towards making fashion sustainable. For example, the Circular Fiber Initiative was launched at Copenhagen’s fashion summit last year in 2017. The Make Fashion Circular initiative has serious buy-in from brand conscious companies and designers such as Nike, Stella McCartney, H&M and Burberry.
We see momentum taking shape in Finland on this front as well with Stadin Ammattiopisto vocational college, commencing a new program in August creating an environment for textile and fashion design students to learn to produce products that can be part of the circular economy.
Dirk Hofmann: In addition to that, data can play a key role in being able to improve efficiency, predict sales and resource needs along the value chain. If we are able to move towards a circular economy using data to make our businesses efficient, there is a huge opportunity in the fashion industry and for designers.