How advanced BI tools are delivering self-service analytics

Yogi Schulz

@itworldca
Published: August 24th, 2015

The business intelligence (BI) and analytics market is shifting away from fairly centralized IT-led initiatives to more decentralized business-led initiatives. These initiatives are labelled self-service BI in the literature. They’re led by impatient end-users clamoring for faster access to data and shorter elapsed times to develop BI applications.

Adventurous end-users are leveraging the new capability of advanced BI tools that enable IT-independent analytics development and deployment with new data discovery functionality. The IT department’s involvement remains critical to the operation and enhancement of the BI data sources.

Here’s a summary of the more important BI/analytics capabilities, as I see them, derived from the excellent 2015 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms. This summary can help guide your BI tool selection and ensure your impatient end-users receive quickly developed BI applications.

Business end-user data mashup

Drag and drop end-user-driven creation of analytic routines now uses graphical icons, as in this example, rather than arcane code. The icon functionality serves to combine or mashup data from multiple data sources. Under the hood, these advanced BI capabilities deliver semantic auto-discovery, intelligent joins and hierarchy generation.

Previously, developing analytic routines with these advanced BI capabilities required software developers with specialized expertise. The development work was simply beyond the ability of even the most motivated and adventurous end-user.

Cloud Deployment

Platform-as-a-service and analytics-as-a-service are relatively new capabilities for building, deploying and managing analytic applications in the cloud.

In a BI context, cloud benefits include combining external data available in the cloud with proprietary data managed either on-premises or in the cloud. The impetuous to combine external data with proprietary data is that often the more impactful BI applications require this mashup. There are many examples of analytics that benefit from this combination of external and proprietary data. Here’s a short list of examples:

Weather by date/time and region to improve reliability of sales by product forecast.
Supplier product specifications and product availability to more accurately schedule project work.
Macroeconomic forecasts to improve sales forecasts by country.
Competitor financial performance to benchmark company performance.

Previously, developing analytic routines that required external data also first required that the external data be imported into an on-premises datastore and then accessed from there. This approach is often too effort consumptive and operationally tedious to be sustainable.

Development and Integration

Advanced BI tools provide a set of end-user visual programming tools and a development environment for building reports, dashboards, queries and analysis. The BI tools enable scalable and personalized distribution, scheduling and alerts. The BI tools should include the ability to integrate BI routines into a business process, an application or a portal.

Previously, developing this level of development and integration required specialized software development expertise. The development work demanded more effort than even the most determined end-user had available.

Free-Form Interactive Exploration

Free-form interactive exploration enables the exploration of data via real-time manipulation of chart objects by end-users. This feature includes an array of visualization options that go beyond pie, bar and line charts, to include heat maps, geographic maps and scatter plots. These advanced BI tools enable end-users to enhance the analytics while exploring the data by interacting directly with its visual representation.

Previously, developing visual representations required software development expertise to code, run and then confirm the accuracy of specified visual representation with the end-user.

Can you share any examples of how advanced BI tools have reduced the elapsed time required to deliver your BI applications?