Vuonna 2023 Cloud1 sai arvostetun Future Workplaces -sertifikaatin, tunnustuksena erinomaisesta yrityskulttuurista ja työntekijäymmärryksestä. Cloud1:n eNPS-indeksi oli erinomainen 59.
Have you ever noticed that when you ask about data strategy, you sometimes receive a response about digitalization strategy instead? This can be confusing and frustrating, as it may not address your original question. Misunderstandings can easily happen when discussing complex concepts, even among professionals in the same field. Communication is challenging, and it's crucial to establish a shared language to ensure clarity. As data professionals, we often need to clarify terminology with business partners, even if we have experience in their industry. Organizations may use different terms to refer to common concepts, adding to the confusion. This is particularly true when it comes to data and digitalization strategies. To avoid further misunderstandings, it would be helpful to have concise descriptions of both mentioned strategies to guide the conversation in the desired direction.
Data profiling is one of my all-time favorite data development tools. A few years ago, I got to know the Pandas Profiling Python library, which does so much of the work that I previously had to do manually, mainly using SQL and Python. Data profiling can catch a wide variety of problems, but if the cause-and-effect relationship is not simple, it is not useful for a deeper investigation. The cause of the problem often has to be dug up more or less manually after profiling. So, I set out to investigate whether ML and the technologies used for its development, could somehow help me in finding the causes of quality problems.
I don't recall such hype from a single technology during my career that Open AI’s Chat GPT has made. As a regular person, I am just as into that hype as the next person. But for it to revolutionize the data industry. Well, for that we might still have a bit way to go. On that path, however, the Azure Open AI is a hefty step in the right direction. Here is a story of my first impressions trying to utilize the new service offering. I wanted to do a text analysis that would, instead of just picking up words, use some kind of intelligence to categorize the inputs. So, I started to think and search for a data set that would be relatable and would make a simple use case. Song lyrics would be perfect, right? How little did I know...
In Power BI, like in quite a few other analytics tools as well, there have always been challenges when moving to really large amounts of data. Like now, for example, analyzing a table with a billion rows. This kind of problem has usually been attempted to be answered by storing part of the data in the memory of the analytics tool, which is usually limited, and then directing detailed queries to a database. A few years back, while testing how Databricks would perform against Power BI's direct queries, I was disappointed. The speed was not as expected. And I also heard a similar comment like this last week from a report developer. But surely things must have improved somewhat in last couple of years?
Press release Embargo 27/12/2022
Tuomo Riihentupa, the new CEO of Cloud1
Industrial IoT is one of the fastest-growing trends in the data field and platforms and it is discussed in Cloud1's podcast Azure AamuCast