Tech Talk with a Data Scientist: Tech-telmechtel with BI [Interview]

Yes, data scientist is an official professional title. Data science can be studied and, as today’s interview guest Christoph shows, it is certainly a wise choice to do so. After all, data scientist is not only the sexiest job of the 21st century according to Harvard Business Review, but also something that every company that wants to keep up with the competition in the digital age needs.

Tech Talk with Data Scientist Christoph

Christoph, how did you get into data science?

I studied data science and wrote my master’s thesis on »Graph-based analyses with big data frameworks«. Before joining the data science team at dotSource, I worked in software development, specialising in Hybris and Java.

So you are now doing exactly what you studied?

Yes, but finally in practice.

What does this mean in concrete terms? What does a data scientist do?

Christoph lists one buzzword after another and chuckles.

We carry out smart data analysis. We use data engineering to collect data from different systems and sources and then store it in a data warehouse, where we combine the data to generate insights.

Easy, right? ?

Tech Talk – From Data Engineering and ETL to Data Warehouses

OK, so data engineering is the act of collecting and processing data, and a data warehouse is the place where it happens? Can you give me an example?

Exactly. We use ETL (Extract, Transform, Load) processes to store data, for example from a CRM system, from Google Analytics or simple Excel and CSV files, in a data warehouse (the database) and then apply various data science methods, for example clustering.

There are customers who only make a purchase once a year, but spend a lot of money on it. They’ll come back. You should thus address these customers differently than customers who are fickle or notorious shopping cart dropouts ?

Based on the data we collect and evaluate, it’s possible not only to cluster these customers, but also to draw conclusions about other, new or potential customers and their behaviour – and to adjust one’s marketing activities accordingly.

So what you do is the basis for optimising my CRM and marketing automation processes?

Yes, for example.

Cooperation with the respective departments is extremely important here – whether it’s in the field of CRM (as in the customer clustering example), in the field of PIM and MDM when it comes to product data or in the commerce segment when specific shop systems are involved.

We analyse data, gain insights and make forecasts that enable our departments to optimise processes and activities together with the client.

Tech Talk: BI Tools and Connectors

And what is it all about in terms of technology? Can you simply bring together systems and tools?

Yes, by using BI tools that offer connectors, for example. This way, you can connect a CRM, PIM or shop system. There are also third-party connectors or APIs provided by the various systems themselves. These can be used as well.

What tools do you work with?

Some of the tools we use are

• Power BI by Microsoft
• SAP Analytics Cloud and
• Google Data Studio

Tech Talk: SAP Analytics Cloud

Speaking of SAP Analytics Cloud: you are our expert when it comes to this BI tool. What do you use it for?

We use it to create our dashboards (for dotSource internally) – and of course to support our clients in their analyses.

For example, we look at sales data and find »the only truth«.

What do you mean by this?

Well, it’s often the case that those responsible in e-commerce refer to different sources when they want to provide information about their sales figures, for example. One of them says: »I’ll check Google Analytics«, another one relies on data from the ERP system, yet another one looks at the figures provided by the shop system – and the result is different every time. We bring together the data from all sources in the data warehouse and, as I already said, find out what is really true ?

That sounds quite plausible. And extremely exciting.

Doesn’t it?