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George Anadiotis

Agile business intelligence: reshaping the landscape

Table of Contents

  1. Summary
  2. Motivation and market definition
  3. Evaluation methodology
    1. Advanced visualization
    2. Data-source agility
    3. Domain-specific knowledge
    4. Cloud support
    5. Distribution-channel agility
  4. Key takeaways
  5. About George Anadiotis

1. Summary

The last few years have brought a wave of changes for business intelligence (BI) solutions. A set of redefining technological trends is reshaping the landscape from a slow and cumbersome process practiced mainly by large enterprises to a much more flexible, agile process that mid-market companies as well as individuals can utilize.

This report explores the key features that influence the evolution of agile BI and takes a look at the BI landscape under this light. At first glance, polarization seems to exist between traditional BI vendors, who are focused on extract, transform, and load (ETL) and reporting, and the newcomers, who are focused on data exploration and visualization, but a closer look reveals that, in fact, they converge as adoption of useful features is taking place across the spectrum.

This report will illustrate for both the traditional BI vendors and the newcomers that:

  • As the market is expanding, features such as cloud support and embedded domain-specific knowledge in BI solutions are key. Initially, the benefits will be more obvious to those smaller players who do not have the resources for in-house infrastructure and extended internal projects and who are driven more by needing immediate results. Over the long run, however, these features can benefit all types of organizations.

  • Ubiquity and mobility are key features of data today; therefore, the ability to support a multitude of data sources with as little effort as possible – integrating them and accessing analysis results via a multitude of channels – is important in order to keep up.

  • We are shifting from static reports to interactive visualization. The focus is also shifting from having an overview of metrics to being able to discover what are the causes and effects of the phenomena the metrics express.

Thumbnail image courtesy of SirkulT/Thinkstock.

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