The Insurance industry is in uncharted waters and COVID-19 has taken us where no algorithm has gone before. Today’s models, norms, and averages are being re-written on the fly, with insurers forced to cope with the inevitable conflict between old standards and the new normal.
The only certainty in today’s world is change. And nowhere is that more apparent than in the way organizations consume data. A typical company might have thousands of analysts and business users accessing dashboards daily, hundreds of data scientists building and training models, and a large team of data engineers designing and running data pipelines. Each of these workloads has distinct compute and storage needs, and those needs can change significantly from hour to hour and day to day.
It’s time, again, to look at a chart that you might not be using, but that you definitively should consider using when doing data visualization. The waterfall chart is great at visualizing the cumulative effect from positive and negative changes, as, for example, you would see in a Profit and Loss (P&L) report.
Today we are introducing a new series of blog posts that will take a look at recent enhancements to Apache Impala. Many of these are performance improvements, such as the feature described below which will give anywhere from a 2x to 7x performance improvement by taking better advantage of all the CPU cores. In addition, a lot of work has also been put into ensuring that Impala runs optimally in decoupled compute scenarios, where the data lives in object storage or remote HDFS.
Today, companies can measure every aspect of business health, except the health of their data which drives business decisions. Data is vital to inform critical decisions such as identifying new routes to market, systems to support business agility, and more resilient supply chains. As Harvard Business Review puts it, “Your organization’s data is the source of both the opportunity and the challenges to your innovation.
At Google Cloud, we’re invested in building data analytics products with a customer-first mindset. Our engineering team is thrilled to share recent feature enhancements and product updates that we’ve made to help you get even more value out of BigQuery, Google Cloud’s enterprise data warehouse.
We live in a Fourth Industrial Revolution, where data is the lifeblood of business. Those of us who harness the power of artificial intelligence, machine learning and augmented analytics to uncover insights from data are the ones who will be able to find better ways of driving efficiency, productivity and superior business outcomes.
The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.
Every professional has used Microsoft products in their work life. The ubiquity of Microsoft Office in the enterprise is one of the main reasons many analytics users are familiar with Power BI, given how the company bundles its business intelligence software with other licenses in the same way Excel and PowerPoint comes with Office.
