5 Steps on the Journey to Modern BI

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5 Steps on the Journey to Modern BI

Modern business intelligence platforms go deeper than traditional models, asking the important questions about how and why something happened.

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Fluid Data Discovery

The analytics workflow is an important aspect of getting the fastest time-to-insight. Analysis is much faster in modern BI in which it is iterative and fluid. Such a user experience feeds the experimentation process, which is how you perform data discovery. The traditionally sequential steps of data integration, preparation, analytics and visualization should be blended into an open, fluid interaction, rather than a linear one. With fluid data discovery, you can experiment with each phase of the cycle and quickly run through experiments to find answers without ever having to switch context. Quickly and efficiently, you discover the answers you seek.

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Limitless Types of Data Sources

Traditional BI was limited to structured, highly aggregated data. Today's explosion of data is coming from many diverse sources and formats, requiring new analytic systems to incorporate data with no structure, ragged hierarchies, many-to-many relationships or newer structures like graph models.

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True Self-Service

The traditional approach to BI and analytics required a sequential process, using different specialized tools and methods. The fragmented, linear methods of traditional and first-generation self-service approaches disconnect the data integration and preparation from the analysis and visualization phases. Linked analysis and visualization is an essential part of the fluid data discovery process. Applying the analytic functions directly determines if the answers are valid. The dynamic visualization helps quickly see the accuracy of the answers. Iteratively adjusting the various upstream steps and immediately seeing the impact speeds the overall discovery process.

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Future-Proof Architecture

A modern BI platform should optimize for the latest and greatest execution engines while abstracting the technical complexities. How fast you can crunch through the data is critical, but you should only need to know that it executes quickly and efficiently so your experimental workflow is not inhibited. That gives you the best of both worlds—fast analytic job execution and future proofing for easy upgrades to new technologies as they mature on the market.

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Complete Governance

Without strong governance, self-service BI turns into chaos. Modern BI platforms should offer governance features that cover quality, data policies, security, data privacy, compliance and retention. Taking it a step further, data lineage capabilities allow users to track the entire lifecycle of analytic processing, showing where the data came from, how it moved through the process and how results are calculated at every step of the way.

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The Next Challenge for Hadoop: Quality of Service

After a decade of proving that Hadoop is not just hype, much of the focus and attention of this open-source community now goes to its evolving ecosystem of tools and applications—Spark, Impala, Hive—that are helping usher in new users exploring new use cases. However, as additional workloads are added to a cluster, the challenges of using Hadoop in production grow exponentially and become increasingly complicated. How will clusters react to massive growth and unpredictable changes in usage? Your cluster may be operating just fine right now, but what happens in a few months as you add hundreds of new workloads? When a cluster has hundreds of nodes each running dozens of jobs, Hadoop quickly becomes a chaotic system, and when uses are constantly and dynamically changing, it has an impact on business-critical performance. Ultimately, Hadoop won't move forward into the next decade unless the...
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