How do you deliver more insights out to more people? Operationalizing BI and analytics – that is, putting the power of data in the hands of everyone across the enterprise, not just analysts and data scientists – has always been the mantra for Birst co-founder Brad Peters.
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According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%? Despite the efforts of Cloud BI vendors making it easier for users to acquire, explore, and analyze data sources without IT dependency, lack of data literacy and analytic skills still hinder widespread adoption for data-driven decision making. But the industry is undergoing a fundamental transformation. The mainstream arrival of Artificial Intelligence (AI) brings with it the potential to finally meet the demand for actionable, enterprise-wide, fact-based decision making.
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When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn’t help reduce the confusion when every tech vendor rebrands their products as AI.
The post Inside the Mind and Methodology of a Data Scientist appeared first on Birst.
In Part 1 of this blog, I gave a high-level comparison of traditional extract, transfer and load (ETL) tools, desktop data preparation tools and Birst’s modern, built-for-the-cloud ETL tools for data analytics. In this blog, I’ll dive deeper into the eight key ways that, of the three options, Birst is best “able” to meet the rigorous requirements of today’s enterprise users.
The post How “Able” Is Your ETL Process? 8 Ways To Modernize Data Prep, Part 2 appeared first on Birst.
Remember when you began your career and the prospect of retirement was an event in the distant future? How many of the poor decisions you made over the years could have made for a better retirement outcome had you had a crystal ball to see into the future? With better knowledge about the future, would your decisions have been different?
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