Insights & news
During the implementation of Data Science Projects, we always face cases where we have to decide on the best method of implementation in order for it to be integrated with the pipeline smoothly. The goal is to achieve the most simplistic implementation as the overall design is always complex. We focus on to simplifying our approaches as much as possible so we can keep track of all the steps and modify them easily with minimum implementation/modification time.
Corporater BMP (Business Management Platform) allows for the cascading of KPIs in order to facilitate the creation of detailed and insightful dashboards across all organisational levels and reporting periods. In doing so, this solution creates a detailed strategy model where executives and their employees will be able to assess performance at various levels of operation.
Another week, another trip, another award for Football!
We just made it back from the Aspire Summit in Doha and here we are again at another great event in Milan for the SAP NOW – SAP Quality Awards. On the heels of being told that Clariba was among the winners of the Innovation category, we are excited to announce that we received the top level - SAP Quality Award Gold it is!
Our journey in Sports Data Analytics started back in 2015 and what a ride it has been! Back in 2015, in the offices of Aspire Academy, we were invited to brainstorm on a performance platform that could serve professional football on one side and individual sports on the other. Thanks to the trust of Aspire Academy an amazing project was brought to life, a new experience for the Clariba team was initiated and my personal journey in sports performance analytics began!
SAP Analytics Cloud is just for Analytics. This is how we usually misunderstand the true potential of SAP Analytics Cloud. In this post, we explain key features that SAP Analytics Cloud provides for successfully planning, budgeting and analyzing data. This blog comes from our expertise working in different scenarios we have satisfactorily developed for our customers.
Some tools can be more productive than others. Throughout our experience in implementing an optimal machine-learning pipeline in production, we have learned to appreciate the raw strength of the combination of SAP HANA with SAP Data Services. The amount of time that can be saved by reformulating the approach and optimizing it to use this combination is significant, compared to a vanilla approach involving usage of Python for data wrangling, cleaning, discovery, and normalization, which are significant aspects of machine learning pipeline development.
By general rule, every 6 months we can expect a new version of the traditional on-premise SAP BI Platform, which comes with enhanced functionalities. In a world where everything moves to the cloud, for those customers that still want to leverage their current on-premise investments and aren't eager to see their data flying through the internet, a basic upgrade could be the ideal solution.
In today’s post, we will narrate you our journey navigating through the ocean of NULLs. This is the story of how we moved forward from the mystical, the initial expectations and assumption, to the practical, an actual problem-solving methodology that became an integral and reusable approach of our data science framework.
