Category: Networked BI

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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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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Improve your company’s bottom line with analytics

The explosion of data in the financial services industry is creating new opportunities for business intelligence and analytics. At the same time, this explosion presents significant challenges. Over 2.7 zettabytes of data exist in the digital universe today. Every day, financial services companies seek to use these massive volumes of data to increase margins, reduce risk, boost customer satisfaction, and win in a globally competitive marketplace.

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More than Data: It’s Networked BI that Enables a “Single Version of the Truth”

Data governance is a broad topic that people seem to agree is important, but still argue about the details. Specifically, it’s hard to find an IT professional or BI and Analytics user who doesn’t believe that good execution on data governance will yield the “single version of the truth” required to make confident, accurate business decisions.

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