Teams-meeting with Samet Boyaci – Part I

Friday morning – after a busy week – we were happy to have a Teams meeting with Samet Boyaci – Data Warehouse Manager at NA-KD. 

Samet is obviously properly “branded” in a cool NA-KD black t-shirt. Should I have worn a Knowit hoodie? 

Hi there! Who are you? 

Starting from the beginning, originally from Istanbul, Samet has his university degree in industrial engineering – but found it challenging to choose what field to work in after university. 

He decided to start working within IT in a large bank and quickly handled large amounts of data. The data was initially within research and development, and later on, he moved on to bigger projects with AI. It was fun and helped him realize how important it is for a large company to have an excellent in-house data structure as a base. 

He continued in the field as a business analyst within the data warehouse department. He stayed with the bank for several years in an inspiring and great learning experience – until it was time to move on and learn new stuff. 

E-commerce turned out to be the next stop to try out the fun work with data and analysis in a new context. He points out that within e-commerce – the entire business is based on data and the interaction between the data architecture, the data engineering, and the actual business and marketing. 

With new input and understanding, Samet moved newfound insights abroad and found NA-KD in Gothenburg. First time in Sweden, he looks forward to more exploration of the Scandinavian countries once covid19 allows for it. 

So – with this experience: what is your take on data-driven work? 

The core is to build confidence with customers – whether business to business or business to consumer. The data-driven business allows the user to make decisions based on actual facts, not feelings or thoughts, but data and logic. It will enable the end-user to make a correct decision – checking up on what they ordered last and the actual delivery time – for an informed decision – a data-driven decision moving forward. 

What makes NA-KD successful within data-driven work? 

Larger corporations such as banks are often divided into different sectors where each area is complicated enough to specialize within that sector. Banks have vast amounts of data that need to be built and handled in proper order and structure. The amounts of data are impressive, and the experts within each field are very sophisticated, but it also makes it more challenging to see the “bigger picture”. The overall interaction is unfortunately limited. 

At more minor corporations with a modern structure such as NA-KD the data-driven work is integrated into the entire company – cross-borders. It gives a much better understanding of why the data is required, how it is used and where it comes from, but it also allows for efficient communication between IT and business. Moreover - the project teams consist of all parts. 

NA-KD realized early on that they needed to decide about investments for solving long-term data handling as a basis for their business. 

Data and business together – key to success! 

According to Samet, the key to success seems relatively straightforward and “easy”. Regardless of the data quality, the structure, organization, cleaning, and analysis will not aspire to anything unless the business is involved in the creation. 

People are generally hesitant to amend their daily routines; you want to continue with the work you are comfortable with. Then there is a pedagogic question from the product manager to explain the benefit of new development and communicate what will be better and why. 

It requires a new model. The realization that success is based on business. 

Even if you have rocket science level in your data handling – it doesn’t help unless you actually know why you have it, why you need it and what you want to use it for. 

Samet concludes wisely: the key is simple as always – we need to understand both sides. A good product owner and business analyst should help create efficient communication with both sides – qualified technical input and business need combining. 

To be continued – stayed tuned for more on: 
Agile sprints in the datadriven procedure and Challenges ahead 

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