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Summary: German computer scientist Michael Berthold and his colleagues built an open source data processing platform called KNIME after seeing organizations struggle to adopt data analytics practices. The platform has now evolved into a VC-backed startup with a strong portfolio of clients, and has recently received a $30 million investment. Key takeaways: Despite the aspiration of many organizations to become data-driven, only half of data and analytics leaders say their teams are effective in providing value to their organizations. KNIME's visual, no-code workflows and ability to integrate with an org's systems of record have made it a top choice for businesses looking to transform data, create reports and visualizations, and compare different data sets. With the recent investment, KNIME plans to expand its team, develop new products, and target small- and medium-sized businesses. Counter arguments: KNIME faces competition from other data analytics companies such as Dataiku, Alteryx, IBM, and SAS. The technology industry is currently experiencing a slowdown, which may affect sales cycles and negotiations for KNIME. #venturecapital #vc #venture #startups

Exciting changes are happening in the field of Information Technology and Data Visualization! Are real-time data integration systems the future? The brilliant minds at Feldera seem to think so. Feldera, co-founded by Ben Pfaff, Gerd Zellweger, Lalith Suresh, Leonid Ryzhyk, Mihai Budiu and Mohsin Beg, have developed a revolutionary system that integrates, captures, and analyzes real-time data, delivering prompt results as new information emerges. Based in Foster City, California, this trailblazing startup is driving innovation in Data Integration, Data Visualization and Real Time Information Technology. Their startup journey is nothing short of inspirational, and that’s exactly what we delve into in our new blog post. Dive into the full story here: https://lnkd.in/eDf8Z9RD Stay informed. Stay updated. And most importantly, stay curious! #Innovation #DataVisualization #RealTimeData #InformationTechnology #StartUp #DataIntegration Thanks, Feldera, for leading the way towards future!

What blocks your science innovation? Could improving #datamanagement hold a solution? A few simple practices hold promise... 👉 Standardize parameter and unit names for cross-functional teams and departments 👉 Resolve for simple, powerful tools that won't cost a fortune 👉 Use knowledge from past work to solve new problems. Leave recreating wheels to others 🙅♂️ https://lnkd.in/erjGyC3Y ----- Was this helpful? Like and follow CoBaseKRM! 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 - CoBaseKRM | #CoBaseKRM | #KRM 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 - youtube.com/@cobasekrm CoBaseKRM is a product of Predictum Inc. 🔔 Follow us to learn how Predictum empowers scientists, engineers, and analysts. #dataanalyst #database #dataaccuracy #dataanalytics #statisticalanalysis #statistician #processimprovement #researchmethods

What blocks your science innovation? Could improving #datamanagement hold a solution? A few findings hold promise... 👉 Standardize parameter and unit names for all your team's work 👉 Resolve for simple, powerful tools that won't cost a fortune 👉 Use knowledge from past work to solve new problems. Leave wheel re-creation to others. ----- Was this helpful? Like and follow CoBaseKRM! 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 - CoBaseKRM | #CoBaseKRM | #KRM 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 - www.youtube.com/@cobasekrm CoBaseKRM is a product of Predictum Inc. Learn how Predictum empowers scientists, engineers, and analysts at predictum.com. https://lnkd.in/e97e2UFN

We're excited that the Washington Business Journal ran a story about the formation of the Data Society Group, which offers data science and AI workforce training to fill cultural and skills gaps that prevent the effective use of data within organizations. Article highlights: Data Society, a D.C. data science and workforce startup, has been acquired by private equity group Growth Catalyst Partners and merged with two portfolio companies, The Data Lodge and CDO Magazine. Now known as Data Society Group, the combined entity is set to deepen its impact in both private and public sectors, empowering clients to harness the power of their data effectively. Under the new ownership, Data Society Group aims to expand its reach further and enhance its offerings. Plans include developing assessments to measure client progress in data utilization and exploring potential acquisitions to bolster data science training initiatives. This strategic move signifies an exciting chapter for Data Society Group. We're thrilled to be a part of their journey toward continued growth and innovation. Note this article is subscription-based. #DataSocietyGroup

Data lakes are expansive repositories that store vast amounts of raw and structured data, offering a versatile approach to data management. Unlike traditional data warehouses with rigid structures, data lakes accommodate diverse data types, formats, and sources, enabling organizations to ingest, store, and analyze data at scale. They provide a centralized hub for data exploration, processing, and collaboration, supporting advanced analytics, machine learning, and AI applications. By leveraging data lakes, businesses can unlock valuable insights, enhance decision-making, and drive innovation across various domains, from marketing and sales to operations and customer experience. However, effective data lake management requires robust governance, security measures, data quality controls, and skilled data professionals to ensure data reliability, accessibility, and relevance for actionable intelligence and strategic outcomes. #datalake #datalakes #datalakestorage #structureddata #unstructureddata #deeplance #finland #helsinki #canada #usa

 Watch this video interview by CDO Magazine in which Sangeeta Edwin, VP of Data and Analytics at U.S. Venture, Inc., speaks with Mike Woods, VP of Sales at Denodo about having the right #datafoundation before embarking on the #AI journey, building infrastructure, the collaborative role of a #data and #analytics team, running PoCs, and prioritizing foundations, processes, and people https://buff.ly/4bVEXXE

🚀 Transforming Data Chaos into Business Insights with Cube 🚀 📊 In the dynamic world of startups and small businesses, data management can often become overwhelming. Luckily, innovations like Cube's semantic layer are transforming the landscape. This next-gen solution connects data sources and consumers, streamlining data access and utility across a company's ecosystem. 🔍 Key Highlights: - 🛠️ Universal Applicability: Cube’s semantic layer integrates with various BI tools, AI chatbots, and more, ensuring uniform data understanding and handling. - 💡 Enhanced Security: With role-based access controls, automated query adjustments, and performance insights, Cube ensures that data access is secure and efficient. - 👥 Broad Adoption: Already serving over 200 Fortune 1000 companies and nearing 5 million users, Cube is a testament to robust, scalable data solutions. 💥 This development not only simplifies data handling but also empowers decisions and operations, making it crucial for business growth in today's tech-driven market. For startups striving to streamline their data operations, embracing such a platform could be a game-changer. 🔗 For a deeper dive into how Cube is redesigning business intelligence through its innovative semantic layer, check out the complete article: https://lnkd.in/g5zwX2A4 #DataManagement #BusinessIntelligence #Startups This post was generated by an AI. Please contact us for more information.

Watch this video interview by @CDO Magazine in which @Sangeeta Edwin, VP Data and Analytics at @U.S. Venture, Inc., speaks with @Mike Woods, VP Sales at @Denodo about having the right #datafoundation before embarking on the #AI journey, building infrastructure, the collaborative role of a #data and #analytics team, running PoCs, and prioritizing foundations, processes, and people https://buff.ly/4bVEXXE

Interesting report commissioned by one of our global tech alliance partners Weka, regarding AI adoption based on a series of interviews with key executives across a broad range of industries. A 28-page read so grab a cupper! Snapshot from the exec findings to whet the appetite! #Weka #HitachiVantara #AIDataAnalytics 2. Many AI projects fail to scale — legacy data architectures are the culprit. AI projects are challenged by weak data foundations. Legacy data architectures are impeding broader deployment. – Achieving scale remains a challenge: Organizations are facing significant challenges in achieving the desired reach of their AI projects. The average organization has 10 projects in the pilot phase and 16 in limited deployment, but only six deployed at scale. – Availability of quality data is a major obstacle: Data quality is the greatest challenge to moving AI projects into production. The challenge for project teams is not so much about identifying relevant data, but its availability; organizations are struggling to build a consistent, integrated data foundation for projects. – Modernizing data architectures is critical to success: Given this, it is unsurprising that the greatest proportion of respondents (35%) cite storage and data management as the primary infrastructure issues hindering AI deployments — significantly greater than compute (26%), security (23%) and networking (15%).