**1. Introduction**

The future is already here. Several new players have already begun to understand the state-of-the-art Sustainability, while others are still in its infancy. There are new tools and techniques to respond to data-driven needs constantly appearing, and at the same time the demand for environmental, social and governance compliance is racing ahead.

The idea of sustainability dates back to the Industrial Revolution, early 20th century, when two opposing factions emerged within the environmental movement: the conservationists and the preservationists. The conservationists focused on the proper use of nature, whereas the preservationists sought the protection of nature from use. In the 1970s sustainable development was a key theme of the United Nations Conference, where the concept was coined to suggest that it was possible to achieve economic growth and industrialization without environmental damage. In the last decades the concept was further refined as 'development that meets the needs of the present without compromising the ability of future generations to meet their own needs' [1].

In essence, the problem today to be addressed has three main elements: 1) unsustainability of current social lifestyles; 2) new regulation on non-financial reporting; 3) introduction of alternative means of payment to exchange transactions.

According to recent studies unsustainability of consumption and production poses a major social problem. If the population reaches 9.6 bn by 2050, we'll need 3 planets to sustain current lifestyles [2]. The proliferation of brands and parties that compete towards a market that is limited in resources induces fierce competition. This is no longer sustainable for customers, who must face increasing costs and prices, but also for small and medium businesses which end up running out of business. Beyond customers and companies there is even one more overarching and vulnerable affected target by environmental accelerated destruction: the community.

Envisioning a gap to evaluate performance and develop a responsible approach to business, the European Commission amended the law to require large companies to disclose certain information on the way they operate and manage social and environmental challenges [3, 4].

Increasing importance of non-financial performance requires large companies to measure and report such type of indicators, namely social and environmental impacts of their activities.

Tracking impact performance and alternative frameworks to shape a better future offer the potential of standardizing metrics and catalyzing value creation towards common goals.

The focus of rewards is no longer just economic and thus customers are increasingly demanding new ways to interact with companies in exchange of a promise for future service [5]. At this point operationalizing value capture from high impact data comes in. This is also known as Operationalized Data Monetization (ODM).

Data and data analytics are accelerating exponentially. According to one survey, 55% of IT leaders named data analytics as one of their main priorities in 2019. (Only security was ranked higher, at 57%). Additionally, 3 of the Top 13 Priorities for Executives and Board Members were related to Data [6] (**Figure 1**).

The rise of digital technologies is reshaping customers' habits and company strategies. And to stay competitive, enterprises – usually responding to suggested digital transformation strategies and "best-in-class" digital benchmarks – are racing to respond to these trends.

Jolted by the resounding success and sheer scale of the 21st century AI and ML-driven digital behemoths (such as Google, Amazon, Alibaba, Tencent, Alipay, Baidu, and dozens of fintech and insurtech startups), companies have plunged headlong into digital transformation [7–10] in the hope of stemming the long-term disruption to their businesses. However, the success rate of digital transformation has proved to be very low. According to recent studies [11, 12], more than 80 percent of analyzed companies have faced limitations in making successful digital changes to their business.

**Figure 1.** *Top technology investments for 2019. Source: MuleSoft.*

*Social Impact Returns. Filling the Finance Gap with Data Value DOI: http://dx.doi.org/10.5772/intechopen.97407*

Most of these companies have missed out on the high-impact value-creation opportunities because of a failure to differentiate between digital transformation and data value capture to generate social returns. Digital transformation, in addition to improving the customer journey, also produces quantities of internal and external data. Data value capture, on the other hand, is the use of data to create economic value and social returns. Survival and let alone sustainable growth require companies to reach the minimum high impact data levels; as of today, there is still a long way to go.

A framework to measure social impact filling the finance gap has been woven into this article. It demonstrates the range of opportunities that can be achieved by adopting data and Sustainable Development Goals (SDGs) as core strategy.

The increasing importance of sustainability for organizations is backed, not only by the fact that most corporate leaders are incorporating environmental, social, and governance (ESG) issues in their agenda but also sustainable funds more than doubled 2019 records, reaching over \$51 billion in new investments, accounting for 25% of global new investments [13].

Executives and Leaders understand that taking responsibility for each of the sustainability pillars (economic, environment and social) implies accountability and impact on people, planet and profits; thus business performance and results.

Performance and results are mainstream measured and evaluated from the financial dimension; which is not comprehensive. This study aims to bridge that gap and raise awareness of the need to introduce the non-financial dimension. Such dimension can be easily understood in the current context of the COVID-19 pandemic situation; which has demonstrated that non-financial risks can pose further damage and in a more significant way than any of the precedent economic crisis.

Duality of models and frameworks is not yet a common practice but combination of quantitative and qualitative metrics is the path to superior and sustainable performance through continuous improvement. Filling the finance gap is challenging but undertaking a proper approach is also doable. And in this context is where technology as a facilitator is key to make it happen.

#### **2. Social impact returns**

#### **2.1 Call to action. Solution is duality**

The use of data and data analytics is centuries old. Developing technologies and tools together with decreasing data costs have eased that firms increasingly use data as support for decision making.

The cost of computation is roughly one hundred-millionth what it was in the 1970s. And the cost per megabyte of data storage has fallen from US\$85,000 in 1956 to just \$0.00002 today in constant dollars. Furthermore, connection speeds of hundreds of megabits per second now cost only tens of dollars per month [12, 14]. As a result, organizations have installed a myriad of systems – computers and software – to enhance their services, resulting in the capture and storage of enormous amounts of data, most of which remains underutilized [15].

It can be empirically and statistically observed that reliance on just quantitative (data based) models and attempting to exploit and understand all the data investing heavily in Data Lakes and Advanced Analytic tools does not work. The qualitative component, which includes counting on the right people and skills, is essential to enhance decision making.

Towards the end of the 16th century, insurance companies were formed on the basis of the monetization of shipping data [16–18]. Actuarial science applied to longevity and health are the backbone of the life and health insurance industries

and have been around for decades [16, 19]. The same is true for the linkage between weather forecasting and commodity trading [20, 21]. There are many other familiar examples where the true value is captured through the combination between quantitative and qualitative aspects. This is what we refer to as need for duality.

Duality is present in every aspect of our lives: humans are rational and emotional; animals have a physical and psychological component; customers are no longer just interested in products but also in user experience; major risks caused by extrinsic and non-business related causes may result even more harmful by those that can be measured by traditional economic KPIs. All in all, we are shifting from the "what" to the "how" and this can have a clear impact on profitability and performance.

Defining and quantifying Key Performance Indicators (KPI) and undertaking these as the basis for operating decisions must be done. But to succeed, beyond just quantitative data, there is a need to introduce a qualitative component to understand which is the minimum data required for high impact decisions (**Figure 2**).

#### **2.2 Quanti- vs. quali?**

The answer is both. There is no single vision for Sustainability nor one definition for social impact return. Many will link these concepts with Corporate Social Responsibility (CSR), others with environmental problems, and very few will get it right by understanding that it is simply "the act of generating measurable economic benefits from available data sources".

To illustrate the call for quanti- + quali- based models, let us take the financial sector. The need for such combined framework emerged and materialized with the reform of the Basel Accord (1988), relying on three pillars: capital adequacy requirements, centralized supervisory and market discipline [22, 23].

For the purpose of understanding the framework proposed, we can draw the following analogy:


**Figure 2.** *The data intelligence gap. Source: The Gartner group, Essex.*

feed back the complementary level, the ability to systematize a dynamic of continuous improvement and sustained profitable growth will be limited. This was precisely one of the core reasons for the amendment of Basel Accord [24, 25].

## **2.3 Non-financial risk management**

The Non-Financial Reporting Directive (2014/95/EU) requires large public interest entities with over 500 employees (listed companies, banks, and insurance companies) to disclose certain non-financial information. As required by the Directive, the Commission has published Non-Binding Guidelines to help companies disclose relevant non-financial information in a more consistent and more comparable manner. However, to date it is unclear for companies how to comply with the Regulation and at the same time it is also unclear who/how to certify that companies are compliant with the Regulation.

How to respond to these challenges? In this regard we have developed a solution for non-financial reporting based on a dual model (quantitative + qualitative KPIs) that makes converge people, technology and social impact.

Social return can be measured by the value enterprises create by utilizing their data to develop and implement their products and services profitably while they contribute to attain the Sustainable Development Goals (2030 Agenda) [26]. To achieve this, companies will need to embark on a shift in organizational behavior, designed to opt for more sustainable ways of working that reduce enterprise complexity, excess of consumption and facilitate impact on society. This transformation entails converting insights into actions. It tackles the following key dimensions (**Figure 3**).

*(1) Regulation.*

Companies must report non- financial indicators. Such Regulation approved by the European Parliament implies that companies need to adapt and adequate their current reporting.

## *(2) Social.*

Applicability and measurement of company data to contribute towards the Goals of the 2030 Agenda (17 SDGs) demands convergence between People, IT and Social Impact.

Sustainability entitles that companies' investment must generate returns which can be re-invested in producing further improvements. Returns materialize either increasing revenues, reducing costs or aiding in risk control.

#### **Figure 3.**

*Key components to generate transformation within an organization (specific orientation towards social impact return). Source: Own elaboration.*
