New Metrics

How to Transform Today’s ‘Senseless’ ESG Data Into Tomorrow’s Actionable Knowledge

Part Three in a 10-Part Series by Reporting 3.0. See previous parts below.

By Bill Baue | Published 7 years ago | About a 9 minute read

Part Three in a 10-Part Series by Reporting 3.0. See previous parts below.

“Big Data has limited value if not paired with its younger and more intelligent sibling, Context. For organizations and businesses to survive today, they have to contextualize their data.”

So wrote Alissa Lorentz of Augify on Wired in 2013, addressing not corporate sustainability/reporting data (as those of you who know our work might guess), but the data field writ large. She goes on to cite the example of a company using business intelligence (BI) software that “has not leveraged its data to its potential” because it fails to link internal data to external data about the outside world. Such “contextualization is crucial in transforming senseless data into real information – information that can be used as actionable insights that enable intelligent corporate decision-making,” she concluded.

Most of the data in the corporate CSR/ESG/sustainability & reporting fields falls into this “senseless” category, we at Reporting 3.0 came to realize when first embarking on the year-long multi-stakeholder development process of our Data Blueprint in early 2016. Indeed, much of the literature and hand-wringing about data in the corporate sustainability/reporting fields focused on problems around aggregation & accessibility, comparability & consistency, quality & verifiability, etc.

These are certainly important issues, but we stepped back to consider our purview, encompassing not only companies but also their intersection with the broader systems within which they operate, as well as our theory of change: that disclosure (including data) is a fulcrum that can leverage necessary transformation at the systems level, including economic system design change. Accordingly, we recognized the above list as “second-order” issues, and that our calling is to focus foremost on the “first-order” issue of designing a coherent information system architecture that addresses systems-level issues head-on.

Donella Meadows: Thresholds & Carrying Capacities, Capitals & Context

One of our early “discoveries” in the first step of our standard Blueprint development process, our literature review, yielded what became a kind of “bible” for the Data Blueprint: Donella Meadows’ 1998 report to the Balaton Group, Indicators and Information Systems for Sustainable Development (which we found in Mark McElroy’s Capital Theory References bibliography, and which the Donella Meadows Project recently added to its Dana Meadows Must-Reads list). Dana’s writing played an integral role in framing our thinking – in particular, this excerpt discerning what we call second-order from first-order indicators:

An environmental indicator becomes a sustainability indicator (or unsustainability indicator) with the addition of time, limit, or target. The central questions of sustainability are: How long can this activity last? How long do we have to respond before we run into trouble? Where are we with respect to our limits?[S]ustainability indicators should be related to carrying capacity or to threshold of danger or to targets. Tons of nutrient per year released into waterways means nothing to people. Amount released relative to the amount the waterways can absorb without becoming toxic or clogged begins to carry a message.

The information system from which these central indicators can be derived will measure capital stocks at every level and the flows that increase, decrease and connect these stocks (bold added).

We found it striking that Meadows adopted a multicapital approach (drawing on the rich history of intellectual foundations documented in McElroy’s bibliography) that gained such credence and uptake in the reporting world with the embrace by the International Integrated Reporting Council (IIRC) of the multiple capitals in its 2013 Integrated Reporting <IR> Framework.

As well, we were struck by her adoption of what we now call a “context-based” approach (drawing on the Global Reporting Initiative (GRI) Sustainability Context Principle first articulated in its G2 Guidelines in 2002) making the “micro-macro” link between the level of individual actors’ impacts, and the systems-level impact on overall capital stocks and flows (within their carrying capacities).

The Daly Triangle: Interlinking Ultimate Means (Natural Capital) with Ultimate Ends (Well-Being)

In the report, Meadows envisions an information system that ties together:

the Ultimate Means of natural capital through
the Intermediate Means of human capital & built capital to
the Intermediate Ends of human capital & social capital (including financial capital) and
the Ultimate Ends of wellbeing (or what Mary Mellor calls “wellth” in a play-on-words that subtly calls into question our prioritization of “wealth”)

To help visualize these interconnections, Meadows appealed to the Daly Triangle, named after World Bank economist and Ecological Economics co-founder Herman Daly, and predicated in large part on what Meadows calls the Daly Rules for sustainability of natural resources:

Renewable resources (fish, forests, soils, groundwaters) must be used no faster than the rate at which they regenerate;
Nonrenewable resources (mineral ores, fossil fuels, fossil groundwaters) must be used no faster than renewable substitutes for them can be put into place;
Pollution and wastes must be emitted no faster than natural systems can absorb them, recycle them, or render them harmless.

In other words, the “Daly Rules” call for operating within natural cycles of renewal, regeneration and assimilation; operations outside these cycles must be engineered out of the system. Here’s the hierarchical relationship of ecological and social resources Daly first proposed, as amended by Meadows into the Daly Triangle:

In the Virtual Dialogue vetting Exposure Draft 2.0 of this Data Blueprint, ECO-OS CEO Noam Gressel gave constructive feedback on the Daly Triangle: “While thresholds are key to Meadows’ thesis, their importance is not brought to life in the graphic representation by the Daly Triangle.” Taking our cue from Dana, who noted that “Daly originally drew it as a triangle or pyramid, and for historical purposes I will use that symbolism, though the shape is not necessary to the logic,” we set out to integrate sustainability thresholds into this visualization.

Our reassessment of the Daly Triangle resulted in a series of tweaks:

First, we recognized the equivalence in importance of the Ultimate Means (natural capital) and Ultimate Ends (well-being), so we equalized our representation of them by placing two triangles side-by-side but facing opposite directions, then we fused them to create more of an hourglass representation.
We then flipped the progression, with natural capital atop as the “sands” of capital resources that flow through the hourglass (which also introduces Meadows’ element of “time”).
Finally, we introduced sustainability thresholds (in the form of ecological ceilings and social foundations established by Kate Raworth in Doughnut Economics)

We called the resulting graphic, which underpins our advocacy for a holistic integral information system, the Daly Hourglass:

Building on the foundation set by the Daly Hourglass, the R3 Data Blueprint advances a general specification based on three primary dimensions necessary for building out a seamless data infrastructure and integral information system that transforms raw (“senseless”) data into insightful information, decision-useful intelligence, and actionable knowledge, and thereby fulfills the potential of triggering transformative systems change.

Integration of the multiple capitals to optimize positive synergies (and mute / eradicate negative interactions) between and amongst them, to better support the creation of financial value, societal (shared) value and system value.
Contextualization of organization-level impacts on the multiple capitals within their carrying capacities at the systems level, preferring virtuous (regenerative) over vicious (degenerative) cycles. Context-Based Sustainability (McElroy’s framework for implementing the GRI Sustainability Context Principle) calls for identifying thresholds separating sustainability from unsustainability, as well as allocations of fair-share contributions to maintaining the overall sufficiency of vital capital resources and cycles.
Activation of responses when the sustainability of any capitals – and hence the potential for biota well-being and human fulfillment – is placed at significant risk. Data without engagement falls short of its potential; “activated” data fulfills its potential of driving the change signaled by integrated, contextualized data. The key to activation is evidence-based advocacy by context-driven stakeholders. And activated data also catalyzes acceleration to scale up change to trigger tipping points of systems change. Indeed, properly contextualized data embeds a gap analysis (i.e. Dana’s “carry a message”) to signal the magnitude of unsustainability, and hence the pace and scale of reform needed to achieve sustainability.

Interestingly, while we (Bill and Ralph) drafted the Data Blueprint and Reporting Blueprint separately, we recognized significant overlap between each Blueprint’s three primary thematic areas: