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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10 Best Practices for Managing Modern Data in Motion

Before big data and fast data, the challenge of data in motion was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line. In today's world, consuming applications and routes and rules for moving data constantly change. Big data processing operations are more like a city traffic grid than the linear path taken by traditional data. The emerging world is many-to-many, with streaming or micro-batched data coming from numerous sources and being consumed by numerous applications. Because modern data is so dynamic, dealing with data in motion requires a full lifecycle perspective including day-to-day operations and agility over time. Organizations must tune the performance of their data movement system as both data infrastructure and business requirements...