Data completeness plays a pivotal role in the accuracy and reliability of insights derived from data, that ultimately guide strategic decision-making. This term encompasses having all the data, ensuring access to the right data in its entirety, to avoid biased or misinformed choices. Even a single missing or inaccurate data point can skew results, leading to misguided conclusions, potentially leading to losses or missed opportunities.

It was lovely to see so many of the community and hear about the latest data streaming initiatives at Kafka Summit this year. We always try to distill the sea of content from the industry’s premier event into a digestible blog post. This time we’ll do it slightly differently and summarize some broader learnings, not only from the sessions we saw, but the conversations we had across the two days.

Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL. Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”.

We tested a product-led go-to-market motion last year. Here are the exciting results and learnings.

New legislation affirms the governance and security responsibilities of all organizations doing business in the EU.

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.

The rise of AI-powered features in today’s analytics solutions has increasingly displaced our reliance on dashboards by making it easier for business people to analyze their data. With this rising paradigm shift, a new label has emerged: The augmented consumer. On a surface-level, the word ‘augmented’ may conjure up a variety of different interpretations. AI-based dashboard assistants? Natural language-led reporting?