Category: Analytics

Inside the Mind and Methodology of a Data Scientist

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.

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How “Able” Is Your ETL Process? 8 Ways To Modernize Data Prep, Part 2

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.

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Seven Steps to Success for Predictive Analytics in Financial Services

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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How Are You Analyzing and Adjusting to the Mobile Shopper?

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.

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How “Able” Is Your ETL Process? Modernizing Data Prep, Part 1

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.

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What AI Means to a Data Scientist

How many times have you wished for more hours in the day so you can complete more tasks? A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. However, there are simply not enough data scientists in the world to deliver on the AI potential. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases?

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What AI Means to a Retailer Dedicated to Customer Experience

Retailers are focused more than ever on quickly adjusting to changing customer preferences and demand. Specialty’s Café and Bakery is a great example of a retailer that is using data to drive decisions related to product development and selection, inventories, staffing, and more to attract and keep customers.

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Will AI Increase the Reach of BI and Analytics?

The business intelligence (BI) assembly line is broken, with adoption or utilization rates of only 30 percent in a typical organization, according to Gartner[1]. These adoption rates include all the users of the BI system – administrators who manage the system, analysts who build reports, and business users who consume reports for better decision making. This underutilization ignores employees outside the BI system who could be using valuable data to make better decisions. No one can argue that business performance and innovation are negatively impacted when data assets are not fully leveraged for decision making.

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