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Reading Time: 3 minutes One of the biggest frustrations across the board in healthcare and clinical research is poor quality data. High quality data is essential for clinical trials, as well as for improving healthcare for patients. But all too often, clinical researchers are…
Reading Time: 5 minutes How we generate, manage, and consume energy is changing, especially in this volatile post-pandemic world. A sustainable utility future requires improved business resilience with the ability to quickly address new energy-transition requirements as well as increasing regulatory and political demands….
Reading Time: 2 minutes Fast, intelligent decision making relies on real-time access to the right data. However, the pain is real when it comes to data integration and data management, but today’s enterprise architects are racing to build modern data infrastructures using data fabric,…
Reading Time: 4 minutes Organizations are increasingly turning to the cloud to take advantage of scalable yet elastic computing and storage resources. The availability and economics of the cloud for flexible and scalable high-performance environments has radically changed the way information architects envision the…
Reading Time: 2 minutes If we look at a typical data science lifecycle, many of its stages have more to do with data than science. Before data scientists can begin their work regarding data science, they often must begin by: Finding the right data…
Reading Time: 3 minutes If you’ve decided to implement a data lake, you might want to keep Gartner’s assessment in mind, which is that about 80% of all data lakes projects will actually fail. Obviously, you want to be in that 20% that succeed….
Reading Time: 3 minutes Including external data in many forms of analytics and reporting can provide significant enrichment. New business insights can be discovered that would be impossible to unearth by working exclusively with internal data. The good news is that external data is…
Reading Time: 5 minutes In recent years, we have seen wide adoption of data analytics. However, most organizations continue to find it challenging to quickly yield actionable insights. Some issues that have been most often cited for this include: Poor data quality: While preparing…
Reading Time: 3 minutes Having been privileged to witness the evolution of the data science and artificial intelligence (AI) scene in the Middle East for the past 10 years and having spoken at one of the first big data events in Dubai back in…
Reading Time: 3 minutes The business value of applying data science in organizations is incontestable. Prescriptive and descriptive models can help improve business and decision making processes. Data science work can be divided into analytical and data preparation work. Examples of data preparation activities…
