Category: enterprise analytics

Does Governance Outweigh the Art of Insight in the Age of AI?

Data visualization tools, desktop data discovery tools, and visual analytics are examples of traditional self-service BI tools that business analysts embrace because they provide a user-friendly way of quickly turning data into insights. These tools are geared toward business analysts that have the skills and knowledge to acquire the right data sets, perform the analysis, and present the insights needed to solve a business problem.

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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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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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Analytics Success Starts With Value-Based Design

[This is the first in a series of blogs on the Birst Value-Based Design (VBD) process. This installment focuses on identifying the top metrics for any analytics deployment, and future blogs will cover other aspects of the VBD process.]

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Panning for Golden Insights in Your Data Lakes

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Data lakes provide an economical means for storing and processing data. However, gaps remain in the maturity and capability of data lakes, leaving organizations struggling with how to reap their benefits in analytic scenarios.

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