Opinion — University failed me badly to prepare me for my IT and Data Science Career

How much worth is a Bachelor’s Degree in IT and Data Science?

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In my current career, I am dealing with several trending topics regarding digitalization such as the renewal of IT through the cloud, agility for more efficiency or customer & employee satisfaction. In addition, I encounter topics within the data science environment on a regular basis. However, I have to say that I taught myself most of the therefore needed skills during my first jobs or learned at least the theory about it later on in my master’s degree. I often asked myself why the university, where I studied for my bachelor’s degree, wasn’t properly preparing me for my upcoming professional…

How to do Data Analytics in Areas like Internal Audit, Financial & Accounting or Controlling

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Software like SAP handle all business processes of a company such as bookkeeping, controlling, sales, etc. It also hosts a lot of data, especially financial data that could lead to significant insights and need to be controlled by departments like business intelligence, accounting or internal audit. The following overview of common checks should provide a useful list of hands on analytic use cases. In this article, it is illustrated with the example of SAP FiCo, but other systems tick similarly.

Suspicious Changes

With the help of the tables CDHDR and CDPOS you can analyze changes in tables and also identify suspicious processes…

How to build meaningful KPIs and Dashboards

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KPI is the abbreviation for Key Performance Indicator. The term refers to key figures that can be used to determine the performance of activities in companies. Which KPIs should be considered to measure success or failure depends on the company objectives [1]. These KPIs can be displayed together with other facts and figures on a so-called dashboard. State-of-the-art (self-service) BI tools such as Microsoft Power BI, Google Data Studio or Tableau can be used for this purpose. …

What are the Key Elements that are used to meet the Challenges?

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This article is intended to clarify the phenomenon of Cloud and the implications for the topics like Data Science, Big Data and other related fields. Nowadays, even Data Warehouses and Data Lakes can be made available via SaaS (e.g. Google’s Big Query or Amazones Redshift). A lot of services can be activated and used with the click of a button. They can also often be used more cheaply and without much effort. The cloud also usually offers significantly more performance and computing power than you could realize yourself in a data center.

Cloud vs. On-Premise

Cloud computing refers to the provision of software…

Use Google Analytics to monitor important Dashboards Metrics

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Do you perhaps also have the Google Cloud with BigQuery as a Data Warehouse in use and as a frontend Data Studio? As a BI or IT department it would be interesting to know if your reports are really used by the users, right? Which reports are particularly popular, up to when data was downloaded. With Google Analytics you can do exactly that.

Step 1: Set up Google Analytics

The first step is to set up Google Analytics if you haven’t. The cool thing here, is that you only need a free Google Account. Afterwards, just click on Start measuring.

What you have to know for your Cloud and Data Projects

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The CLOUD Act (Clarifying Lawful Overseas Use of Data Act) is a U.S. law in place since 2018 regarding the U.S. government’s access to stored data on the Internet [1]. The CLOUD Act from the USA requires the surrender of data regardless of whether it is located in the United States or elsewhere. This conflicts with the law of the EU and its member states. Without a mutual legal assistance agreement, personal data may not be handed over to US authorities simply because of the GDPR. Affected companies must therefore decide which law they are violating.

Status Quo

There is still no…

How the new and simple Features of Jira can support Data Analytic Teams

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Jira Work Management is designed for marketing, HR, finance and other business teams. So you can work and collaborate with others according to your requirements [1]. What I really like about the new feature is the simple design and the fact that tools such as the calendar, lists and the timeline-feature that were previously unavailable, can now also be accessed. If you don’t have an account, you can simply create an one in the Jira Cloud for testing and try it yourself with up to ten colleagues.


Especially for bug fixes or Go-Live-Appointments the calendar view is super handy. …

How to make your Data compliant in Data Science Projects

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Personal data is the core concept of data protection. Data protection law only applies when data relates to individuals. The GDPR for example increases fines to up to 20 million EUR or, in the case of large companies and groups, up to 4% of the global group turnover of the previous year [1]. When working in the field of Big Data, Data Science or related fields it is essential to know about these laws and how anonymization and pseudonymization give the possibility of still using the data for your use cases.

What is Personal Data?

This is any information relating to an identified or…

How to set up stable Data Pipelines

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During my work in the field of data engineering and analytics, I have identified 5 best practices that are essential for stable data processes. Hopefully, these can also help you to safely and correctly realize data transfers— because only with correct data, solid analyses be built.

Prevent Errors

In case of failure a rollback should be done — similar to SQL: If a job aborts with errors, then all changes should be rolled back. Otherwise only X% of the transaction is transmitted and a part is missing. It will be very hard for you to find out what that missing data is.

Fair Processing Times

Terms, Definitions & Future Outcomes

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Big Data and Data Science are real buzzwords at the present time. However, what are the differences between both terms and how are the fields related to each other? Can they even be considered as competitors?

Terms & Definitions

Big Data refers to large amounts of data from areas such as the internet, mobile telephony, the financial industry, the energy sector, healthcare etc.. Big Data can also extract figure sets from sources such as intelligent agents, social media, smart metering systems, vehicles etc. which are stored, processed and evaluated by using special solutions [1].

Data Science is about to generate knowledge from data…


Big Data Enthusiast based in Hamburg and Kiel.

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