Raphael Branger’s Post

Why are data products a long-term, sustainable investment in the future? In a recent article in BigData-Insider, I had the opportunity to talk about data products and the sweeping influence they have on a company’s overall success story. For the longest time, data management was a centralized affair, as evidenced by the rise of solutions such as data warehouses and data lakes. However, the sheer increase in data, data sources and data formats is leading to a risk of data being left unused (data warehouses) or becoming bogged down (data lakes), with departments failing to leverage the valuable knowledge it contains. This creates data silos with incompatible, inaccessible and redundant data of an unknown or insufficient quality. In turn, this hinders the go-live for data-driven solutions, while the use of the data itself falls well short of its potential to generate value. If we instead see data as a data product – as is the case when considering the data mesh concept – that is managed on a decentralized basis by the departments which create the data, this accelerates the tempo at which value is created and has a strong knock-on effect for the company’s success. The data mesh concept sets out exactly how a data product differs from “normal” data – something else I discuss in the article. Click the link to read the full article: https://bit.ly/3HDJdP2 #datamesh #data #dataproducts #dataasaproduct

Please have a look at my online article at Industry of Things about Data Contextualization. There is more to come!

What truly makes an organization data-driven? How are leading companies like Amazon, Bank of America, and Uber leveraging big data for growth and security? Marian Faryna, Big Data Competence Lead at Sigma Software Group, dissects the myths, realities, and opportunities of leveraging data in today's digital landscape. He emphasizes practical approaches for organizations that capitalize on data analytics for sustainable growth and innovation. Marian provides a thorough analysis, from the significance of embracing a data culture to the steps toward becoming a data-driven entity. Thanks to BigData-Insider for featuring this article. Read the full article here (German): https://lnkd.in/dzEDE3ri #BigData #DataDriven #AIinBanking #SigmaSoftware

Utilizing this could prove beneficial in efficiently overseeing your division, business unit, program, or project!

The world of data has changed in recent years. One of the reasons for this is the awareness of the relevance of data.🌍💻 Data Roles, the various positions and functions in the field of data management, play a decisive role in this. These are influenced by their dynamic environment in terms of their content, their relevance, their development and the demands placed on them. Our latest blog post deals with this dynamic development of Data Roles. The following topics will be examined in more detail: ✅Data Roles in the constantly changing market ✅Data Roles at a glance ✅Changing requirements and perspectives of Data Roles In addition to the challenge posed by constant change in the field, there are also opportunities to maximize the innovation and efficiency of data roles and thus ensure future success.🚀 So click on the link and learn more about the exciting field of Data Roles. 🔗https://lnkd.in/dj-jAPSJ

FSD Reflection: L'Oréal - Data Analysis Thanks to Mr. Huy Truong, I can gain valuable knowledge about Data Analysis. The data platform encompasses four essential processes: Data cleansing, Data integration, Data transformation, and Data reduction. Power Query emerges as an valuable tool for efficiently reshaping and organizing data into a structured format that is conducive to analysis. Its intuitive interface and expansive array of transformation capabilities facilitate the restructuring and reformatting of data. With features spanning from filtering rows and columns to creating calculated columns, Power Query empowers users to convert raw data into a refined and well-organized state. This tool underscores the significance of data transformation, portraying it as an equally critical stage to data analysis, while streamlining and expediting the process through its user-friendly functionalities. Additionally, Power Query excels in simplifying the integration of data from diverse sources. It enables the seamless combination of data from various files, databases, or web sources into a unified dataset. This integration saves time and ensures data accuracy and consistency across disparate sources. Moreover, Power Query facilitates the automation of data refreshing processes. Through the creation of queries and parameters, users can schedule routine data updates, guaranteeing that the analysis remains current and relevant. #FSD #FSDGEN4 #Ecommerce #DigitalMarketing

One of the biggest challenges in companies today is managing and handling information properly for an analysis that allows for accurate decision-making. This is why it is essential for anyone working with data to understand the structures and methods to optimize working with large data flows. Highly recommended course!https://lnkd.in/eQJbGjTP #análisisdedatos #analíticadedatos.

This week in #COMM231 class, we discussed "Choosing Methods and Collecting Data." There are roughly four different methods to collect data. These four types include Qualitative, Quantitative, Primary, and Secondary. https://lnkd.in/g7cD44zY goes a little more into the definition side of these terms. They also talk a little about the six methods of data collection. If you have time, take a few minutes to look at how businesses collect data on people to help their business.

𝗛𝗲𝗹𝗽 𝘂𝘀 𝘂𝗻𝗿𝗮𝘃𝗲𝗹 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗰𝘁’𝘀 𝗕𝗿𝗮𝗶𝗻𝘁𝗲𝗮𝘀𝗲𝗿𝘀 This is the second blog in our series exploring some thought-provoking interpretation challenges of the Data Act. 🧠   𝗛𝗲𝗿𝗲’𝘀 𝗕𝗿𝗮𝗶𝗻𝘁𝗲𝗮𝘀𝗲𝗿 𝗡𝗼. 𝟮! Let’s explore the definition of ‘𝗱𝗮𝘁𝗮 𝗿𝗲𝗰𝗶𝗽𝗶𝗲𝗻𝘁’. The Data Act puts users of connected products and related services in control over the data that is generated through their use. It enables users to direct a data holder to share the data with a party of their choice, qualifying such party as ‘data recipient’. This qualification comes with strings attached. Data recipients are restricted in their use of the data. Notably, they are prohibited from employing it to develop competing products and must abide by the contractual restrictions set by the data holder.   Interestingly, the Data Act does not seem to govern the direct provision of data by a user to a third party. These third parties are not considered data recipients and are not bound by the specific restrictions set by the Data Act, presenting a potential regulatory gap.  Data holders can mitigate this by contractually restricting users’ data sharing, ensuring consistent restrictions for all third-party recipients. This approach, however, relies on users to enforce these limitations with third parties. Moreover, it can pose challenges for data holders like manufacturers who lack direct engagement with end-users. 👇 𝗬𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀? 𝗦𝗵𝗮𝗿𝗲 𝘁𝗵𝗲𝗺 𝗯𝗲𝗹𝗼𝘄. 🔜 Stay tuned for our third Brainteaser on in-scope and out-of-scope data! 

Leverage potential of your data with an easy way to build data products.