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?
The post Seven Steps to Success for Predictive Analytics in Financial Services appeared first on Birst.
Every retailer is facing a similar challenge. If you are a retailer and constantly feel the pinch from online giants like Amazon and Google, you have an opportunity to gain back control and competitive advantage with more personalized products and services, building that intimate relationship that these giants simply cannot provide.
The post How Are You Analyzing and Adjusting to the Mobile Shopper? appeared first on Birst.
For all the exciting discovery that data analytics enables, data preparation involves, for most users, an equal amount of drudgery. That’s true for a number of reasons, first and foremost being that enterprise data is rarely structured for analytic use; it’s often designed for transactional system performance or to minimize storage. Wrangling in data that’s spread across different locations and technologies (database, cube, cloud-based, on-premises, flat files, etc.), and then cleaning up “dirty” (incorrect, improperly encoded, duplicated or blank) data is a time-consuming and labor-intensive task, constantly repeated as data sources come and go.
The post How “Able” Is Your ETL Process? Modernizing Data Prep, Part 1 appeared first on Birst.