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Blog2020-03-30T10:26:09+02:00

The Pillars of Data Science Readiness

Success stories about the benefits of machine learning and data science prompt companies from all industries to adopt some level of automated data analysis for themselves. In doing so they often encounter problems early on, or, even worse, late into the process. In this article I will give you an overview of the main pillars of Data Science readiness, as well as how you can achieve them. Data Availability No matter the method or goal, the basis for any machine learning endeavor is data. Any machine learning expert (or hobbyist [...]

By |October 30th, 2020|

Data Quality Disasters

Data quality is an innocuous term. Upon first encounter, the association is usually big tables filled with numbers, some of which erroneous, math, and complex statistics. The consequences, however, can be very real. In my previous article “Data Cleaning: Pitfalls and solutions” I  shed some light on some of the shapes data quality issues can take. I also talked about a few approaches towards improving data quality and shared some insight on the business impact of inadequate data quality. Today, I would like to approach the topic from a more [...]

By |October 6th, 2020|

Data Warehouse in the Cloud VS On-Premises: A calculation example.

Data is the new oil, the new gold... Headlines like these are a dime a dozen these days. Yes, data is important. We are all aware of that by now. Data is produced. Every day, every minute, in masses. By private individuals and also by companies. Every company has the most diverse data generating systems: CRM, ERP, BDE and many more three-digit acronyms. Often there is no historical storage of data in these systems, which means that the data is lost when the records are overwritten. Another problem is the [...]

By |September 8th, 2020|

Data Cleaning: Pitfalls and Solutions

As the interest in machine learning and artificial intelligence grows, companies regularly find themselves confronted with the dissatisfying quality of their data. This discovery is either made early-on with a structured approach, or a lot later, when poor data quality is identified as the root-cause of poorly performing models. In either case, the next step should be a methodical exploration of the available data, followed by a series of steps to remedy the identified issues. In this article, I will give you an overview of common data quality issues and [...]

By |May 26th, 2020|

Early detection of supply chain bottlenecks due to Covid-19 with data analytics

Especially in the current crisis, it can be enormously important for companies to recognize supply chain bottlenecks early on in order to be able to act directly. The coronavirus crisis is putting the economy to the test. Increasingly, supply chain bottlenecks are occurring because suppliers may fail, and the prices for replacement deliveries are rising sharply. The question of how to maintain an overview and create transparency in procurement during uncertain supply situations is therefore of concern to many companies. In addition, the demands on data analytics are increasing, evaluations [...]

By |May 13th, 2020|

What Opportunities Does Data Warehouse Automation Offer Companies?

Many companies are increasingly beginning to understand the great importance of data, its visualization and use for decision-making. However, the first steps towards making these data-driven decisions and preparing the data for successful use, still cause many companies problems. After all, work in the area of data warehousing is often very time-consuming. What is data warehousing used for? Basically, data warehousing offers the possibility to: preserve data, create a single source of truth, increase the productivity to the end users, reduce dependency on key users and react flexibly to changing [...]

By |March 13th, 2020|
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