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The Seven Pillars of Green BI
Measuring the Greenness (Part 2)

by William Laurent, William Laurent, Inc.Monday, February 9, 2009

The first part of this article introduced and discussed the Seven Pillars of Green Business Intelligence. The second part discusses how to manage and measure Green BI initiatives.

In order for Green Business Intelligence to be effective, it must produce accurate, timely, and repeatable knowledge, just like any other BI architecture. Although the means to achieve robust Green BI will generically mirror most other types of BI domains, the end result of green intelligence is unique. Value innovation will be replaced with “green innovation” and influence all areas of corporate operations—from procurement of raw resources to product distribution and marketing. In addition, green reporting will seamlessly support regulatory compliance and clearly chart environmental footprints from a limitless number of vistas. A Green BI dashboard will also be able to measure the greenness of an organization from a business process point of view via custom metrics and KPI crafted exclusively to support sustainability goals. Green BI KPIs must be based (at least loosely) on the Seven Pillars of Green BI, with explicit maps (one-to-one, one-to-many) between KPIs and Pillars where possible.

While most companies can, to varying degrees, manage and measure their corporate performance (financial, compliance, staffing, supply chain management, etc.) through various BI practices, obtaining a lucid picture of enterprise environmental performance and footprint usually remains an onerous and manually intensive undertaking. Organizations may spend an unduly amount of time calculating their VAR (value at risk) each day, but they have little idea how much energy went into making a particular product or know how much power is being consumed by a physical front-office location.

 While traditional streams of business intelligence will take their data from databases, spreadsheets, and other electronic medium, resource consumption data tends to be unstructured and highly scattered on different physical devices, in numerous geographic regions. This information is always difficult to extract and integrate, serving as the biggest initial stumbling block to Green BI initiatives. Because knowledge is coming from non-automated silos such as water/gas meters, garbage bills, equipment energy specifications, the number of corporate parking spaces occupied, and other consumption statistics that remain decoupled from any current data systems, it may take months and months of discovery into a chain of consumption just to track down trusted “sources of origination”. For example, data from electricity bills, HVAC meters, and security systems may have to be manually researched and keyed into the BI system on a month-to-month basis until a more automated solution can be put into effect.

The Green Game

Vendors have become cognizant of the market opportunities that exist for green products; they tout their product’s energy and emissions savings as much as possible. And yet, determining which green hardware products to purchase for their enterprise is something that fills many IT directors and Chief Information Officers with a deep sense of dread. The reason is simple: There are few industry accepted standards for rating the greenness of equipment. Green ratings are dependent primarily on a hardware vendor’s own ways and means. To make the scenario more unclear, some vendors stake their claims of greenness on environmentally benevolent manufacturing methods, not the actual energy savings capabilities of their merchandise.

Old fashioned measures of energy efficiency for many hardware items are aimed to gauge power performance (i.e. amount of power used / number of ports); newer measures aim to convey power usage effectiveness which examine actual device throughput (for instance, bit-per-second/wattage.) To exacerbate matters, different equipment can achieve varying performance depending on the load of network traffic it encounters or its percentage of peak storage capacity used. Given this reality, determining the amount of power used per unit of work will never be a precise exercise for a Green BI analyst. While a device-by-device analysis is useful for procuring hardware, post-purchase green BI will usually seek a slightly bigger picture, such as looking at the power efficiency at a department or floor-by-floor level.

In terms of a dashboard realization of Green BI, SAS has been the leader so far in delivering solutions that can be mobilized to accommodate sustainability objectives. The SAS for Sustainability Management platform leverages SAS’s traditionally strong business intelligence features such as predictive analytics, time-series forecasting, statistical data mining, and activity-based management, integrating them with a well-defined sustainability reporting template. The platform offers both proactive risk assessment and reactive measures to gauge how well an entity is doing in their sustainability efforts. Users can do very cool things such as model different patterns of (peak/off-peak) energy sourcing and consumption for detailed “what if?” analysis.  

 

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