Category: Cloud BI

Birst 7: A new level of ease of use and collaboration across centralized and decentralized analytic teams

Cloud, digital transformation, mergers and acquisitions, big data analytics, data monetization, and more are all critical business initiatives creating an even greater divide between centralized IT and decentralized analytic teams in the business. This is why it is all too common for an organization to utilize at least two different Business Intelligence (BI) tools to support these different analytic needs.

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Birst Smart Analytics: Using AI to Operationalize BI

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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How AI is Lowering the Barrier to Entry for BI and Analytics

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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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 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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