Digital platforms and workplace innovations are changing the nature of work. We’ll need to adapt if we’re to meet the challenges cities face. The remote workforce is an untapped resource — one we can mobilize through the smarter use of social tech.
The Field Study Handbook by Jan Chipchase is for anyone who has traveled and felt missed opportunities. Conversations that fell short of their potential. Questions left unanswered. Experiences that didn’t feel quite right. This handbook was designed to help you connect to the world out there in new ways.
People are looking for meaning in what they do. There is a more equitable, inclusive and empathic way of engaging the world out there. This is a call for remembering what makes us human. I wanted to create an artifact, a beautiful thing that takes up space in your life and nudges you with its presence.
I wanted to create an artifact. A beautiful thing. That takes up space in your life. And nudges you with its presence. To travel, and experience the world with an open mind, and fresh eyes. So that when you return you are ready to shape the world. There’s a bigger mission here than simply how to conduct research. That reframes the relationship between those who make things and those who consume them.
You end up with a lot of people who want to travel and learn and be empathic, but they’re not really being truthful about their intent. Basically ask yourself, why am I doing this? And be honest about the answer.
A more progressive spirit in the world of business wouldn’t be a bad thing. But it’s unlikely that more substantial and lasting progressive social change will come from a new corporate ethic. While proponents of progressive business often proclaim to be the vanguard of a new ‘revolution’ wherein the role of business in society as we know it will change, it is worth noting that progressive business is by no means a new idea.
For example, the 1956 book The American Business Creed — the most comprehensive account of US business ideology ever written — laid bare a conceptual tension between two kinds of business ideologies: ‘the classical business creed’ and ‘the managerial business creed.’ Where the former saw profit-maximization as the central goal of corporations, the latter saw social responsibility as the key goal.
Where the managerial creed existed alongside a rising middle class, today’s proponents of ‘progressive business’ find themselves in an almost contrary environment. There is no comparable, countervailing force to Big Business.
Where the mid-century managerial creed operated on the basis of there being a contradiction between being for-profit and for-society, contemporary proponents of progressive business often reject that there is any. And where progressive business people might increase the wellbeing of stakeholders to their particular corporations, other institutions such as social democracy and labour and social movements would be necessary if the goal is to raise the welfare of all citizens.
Progressive business is an idea with a longer history than commonly recognized. This history shows that while progressive business can certainly help achieve good things, it should not be a substitute for progressive politics.
Corporate social responsibility (CSR) has grown increasingly strategic, and a broader concept of sustainability has gained ground.
Public pressure to address negative corporate externalities, and pressing social, economic, and environmental issues drove the evolution of these practices. Over time, they have blurred the lines between the public, private, and civil sectors, and redefined traditional roles and structures in the process.
International nongovernmental organizations (NGOs) try to fill the gaps amid calls for greater efficiency and more demonstrable impact, but with fewer resources, more constraints, and increased competition for donor dollars. The public sector and civil society are increasingly looking to the private sector to play a larger role. The question is no longer if companies have a responsibility to society, but how best to execute it.
Many companies are stepping up to the call, navigating the competing demands of shareholders to deliver maximum profits in the short-term, and the demands of employees, consumers, and other stakeholders to respond to long-term social issues. Yet others still struggle to deliver cohesive and authentic programs.
A more integrated approach has the potential to transform such initiatives into programs that benefit business and society.
Humans are full of conscious and unconscious biases. For example, a 2012 study in Quebec showed that in considering equally qualified and skilled candidates, those with last names like Ben Saïd were 35 per cent less likely to be called back for an interview than those with last names like Bélanger.
Our machines are learning from this data. They are being taught through AI systems that in fact “Bélangers” are more qualified than “Ben Saïds.” So, as we use AI to predict recidivism in the criminal justice system, to determine loan eligibility or for job application screening, we are further embedding systemic discrimination in our institutions. This is unfair and unethical. It is also a great economic loss. One solution is to teach machines in a similar way to the human brain.
A lot of failed cross-sector projects happen because of a lack of government oversight, a lack of public understanding, a lack of public pressure in what is going into a complex project. That’s where journalism comes in.
Interest in addressing problems through collaboration among business, government and nonprofit sectors is on the rise. Meanwhile, journalists want to understand the mechanics, benefits and limitations of these relationships — partnerships that involve the “linking or sharing of information, resources, activities and capabilities by organizations in two or more sectors to jointly achieve an outcome.”
Journalists have a key role in covering these partnerships, not only to fulfill the Fourth Estate’s mission of holding public officials and agencies accountable for their work in these collaborations, but also to educate the public about cross-sector collaboration as a model for addressing public problems — both its benefits and limitations.
When I first started out, I felt like I always had to be “go, go, go.” One month here, a week or two there, and although I enjoyed the adventurous aspect, that sort of pace is not sustainable, at least not for me. The more I traveled and the more nomads I met in co-working and co-living spaces, I found a lot of them traveled at a slower pace.
The most successful nomads — either financially or those who have been maintaining a nomadic life for years — all have a home base (or two) somewhere where they spend 3 to 6 months on average. The rest of the time, they travel and work from other locations. So, I think that there’s this sort of misconception that to be nomadic you have to constantly be on the move and that is just not the case.
At the Linked Data-driven company of the near future:
1. You will find it curiously difficult to distinguish between “traditional” data workers (analysts, data scientists, etc.) and those in other functional areas who, at other companies, are less reliant on data. The agent of change here is the unambiguous way that Linked Data represents the world.
2. You will marvel at the volume and variety of data accruing from disparate sources, flowing from team to team, integrating with other data, producing unexpected insights, available to anyone at any time.
The data, for example, would be browseable and searchable by humans, crawlable and queryable by machines. Additionally, just like the Web, Linked Data enjoys a remarkable network effect in that each data set added to the network increases the incremental value of every data set in the network.
3. You will be inspired by the rapid creation and adjustment of models and automated processes in response to real-time data. Much of this agility is fueled by machine learning models being deployed at a far faster pace than can be achieved without the aid of Linked Data.
This is because the output of machine learning is tightly correlated with the quality of input data. People who work in this area spend much of their time cleaning and preparing input data, whereas semantically linked data has been “pre-understood” and embedded with knowledge.
[Now,] the energy devoted to the costliest, slowest phase of data work — preparation — can finally be reallocated to more productive activities like analysis.
Does that phrase startle you? It floored me the first time I heard it from Mara Zepeda, a thriving Portland, OR entrepreneur. From my big company days, I’d regarded networking as a pretty relentless, sterile exercise. Go to a conference, collect business cards. Call 20 “contacts” and be satisfied if anyone engages at all.
The comforts of a big company logo and a shared contact management system could keep me going forever. Flying solo, however, that hard-nosed old system falls apart.
I found myself swapping favors with other strivers, hoping that time and trust would take us to a good place. We started with trifles like restaurant recommendations or a few minutes of editing advice on a blog post; eventually, we teamed up on everything from high-profile speaking engagements to a hike across the Grand Canyon. We owned up to our vulnerabilities and created opportunities for each other.
The result: friendships across America (and England!) that straddled work and our off-duty identities in ways I hadn’t expected.
Drilling for data is a massive undertaking that requires more than most nonprofits have. Outside experts can help but work cultures need to change. How can digital nomads be partners in this new data endeavor?
In a public event put on by Stanford PACS in October, Josh Levy, founder and director of Digital Security Exchange, brought attention to a deep data deficit in the social sector. He says matchmaking as a metaphor is useful for understanding what his platform does, but it actually works more like a knowledge exchange.
For Levy, getting nonprofits to sync their data practices with the rest of the world isn’t a top-down assumption of we know what’s best, but rather an affirmation of the value many nonprofits bring to the digital security enterprise. Too bad they can’t see it.
Levy says, “The fundamental data literacy that needs to happen just isn’t in place, and that’s no one’s fault. Nonprofits are under-resourced, they’re under capacity, they have too few people working on too many things, making not enough money. So very rarely will there arise organically this notion of what about data, [much less] coming up with a governance model for it.”
Compounding the issue is organizational paralysis. Little room for advancement results in top-heavy, risk-averse, innovation-poor environments where very few have the time to regroup or improve. Not good, considering at what pace the social sector hemorrhages data. That’s hard knocks for many whose careers depend on knowing more about the people they serve. But help is in sight.
No One’s Fault, Everyone’s Responsibility
When it comes to solving problems, no sector is perfect, but all sectors working together can come close. Nonprofits shouldn’t take Levy’s observations as a scathing rebuke, but rather a call to improve. I’m lucky to have worked with a few nonprofits that are leading in the cross-sector space. TechSoup in San Francisco brings tech solutions to social change agents at reduced rates. The organization registered its one millionth NGO in 2015, and continues to make an imprint on social investment with a virtual slew of professional solutions from tech partners like Microsoft and Adobe.
TechSoup is a powerhouse already, but imagine the potential with two or three million nonprofits under the same umbrella of tech standards and codes of ethics. Many see cross-sector partnerships as the future of corporate social responsibility. It makes sense, given the ethical standards and insights of many nonprofits. As a resource for the social sector, TechSoup can help forge the relationships that facilitate quality and timely data flows, and build a data culture that values diffuse reciprocity as part of a core stratagem in the war against wicked problems.
Then there’s WINGS, the global association based in São Paulo. As a proverbial “butterfly on the wall” for more than three years, I was able to engage with experts ranging from social investors and SROI practitioners, to community philanthropists and tech4good software developers. Listening in on conversations between the world’s smallest and largest philanthropic organizations offered perspectives on how experts in different sectors relate to and communicate with one another.
WINGS, a metanetwork of 20+ thousand philanthropic entities, serves as an information broker that also drives standardization. In 2014, we launched a Global Philanthropy Data Charter designed to unite the sector around data and global development. In 2017, WINGS and Foundation Center released a new version that includes guidance on how to engage in data-sharing practices. I’m excited to see where the project goes, and how strategic alliances fare as a critical success factor.
The Charter gives nonprofits a practical place to start with their data. Theoretically, inertia takes over from there. Levy likens data to a “gateway drug,” in that once it enters your life, you begin thinking about how to store it, name it, control it and share it. By working with consultants who specialize in this line of work, nonprofits are in a better position to partner with the tech companies that are ready to provide funding.
Drilling for data is a massive undertaking that requires time and well-coordinated resources. And that’s not all. Before beginning, everyone from the CEO to the mail clerk has to be in sync with how they handle and report their data. Outside experts can help with data transformations, but work cultures must change first.
Blood from a Stone
Nonprofits are strange birds crunched by capacity issues that weigh heavily on the sector as a whole. Corporate envy drives expectations, despite typically low levels of investment in tech and human resources. Levy says, “People with a high level of technical skill don’t always know where to apply that skill.” He’s talking about the highly specialized private sector employees who bring the fuel to cross-sector initiatives, an example of what Giving Tuesday’s Asha Curran calls “sector generosity.”
What’s ironic, though, is how the vast majority of nonprofit workers — the social change agents who move the needle on the ground — are underappreciated and, as Levy suggests however implicitly, underused.
This is due to what Sean McDonald at Digital Public calls “governance in a loop.” First, I think the elephant in the room, the topic no one wants to talk about but everyone should, is the antiquated power structures that tether nonprofits. A topic for another time, but in short, the social sector should experiment more with democratized models of governance and communication in the workplace.
McDonald says, “Governance, when inclusive, participatory and meaningful, teaches people a huge amount about process and underlying economies. Right now, we have a lot of closed door decision making determining what was historically public policy. We need more people involved in making decisions that define our norms around our norms, particularly norms around social sector and public interest work.”
Governance in this context clearly applies to the workplace. If funder-driven nonprofits are hard-pressed to work with budgets not made for people, how can they adequately invest in their employees, much less their data?
Reimagining mission objectives is a start; no one organization can do everything or be everything to everyone all of the time. Yet, nonprofits often expect too much of themselves and their workers. No strategic plan should be implemented without a focus on partnerships (internal or external), especially for nonprofits whose funder-driven objectives take the lion share of the daily humdrum.
Social change isn’t limited to the social sector. Nonprofits should be willing to outsource their data needs, much like they would for editorial, social engagement or event planning. But how would they do it? Pro bono talent agencies like Taproot are invaluable, but pro bono can only go so far. To scale up, nonprofits should consider integrating talent-for-hire programs into their budgets and innovation portfolios. How can remote workers become full-time partners in this endeavor?
Make Me a Match
Cross-sector initiatives like Digital Security Exchange can gauge the value of a distributed workforce of data experts. Echoing Microsoft’s call for tech companies to be “medics in cyberspace,” nonprofits can call on a workforce of digital nomads to help them transform their data into business intelligence. Given the current scenario, matching experts-for-hire with nonprofits in need isn’t such a bad idea.
The number of fed, state and local civilian employees eligible for retirement has risen sharply. Meanwhile, new talent isn’t flocking to fill open government positions.
Massachusetts Comptroller Tom Shack suggests technology as a solution. “No one is going to hire their way out of the Silver Tsunami. We’re going to have to tech our way out of it.” Shack launched CTHRU, a cloud-based, open records platform that eliminates hundreds if not thousands of hours of work by his staff to access and share data. Rather than keep the state’s financial information locked in PDFs, individual computers, or in the customized, cumbersome, legacy finance systems, CTHRU shows payroll, budget, and spending data to anyone on a mobile device.
Shack understands the urgency of unearthing as much data as possible before employees with valuable institutional knowledge of programs retire from state service. Governments produce vast amounts of data. Of all the ways technology can reduce staff workloads, making data standardized and accessible in the cloud is one of the most impactful. Unlocking “tribal knowledge” trapped in employees’ minds and their computers opens up nearly endless avenues for process improvement.
With automated data flows, agencies can give the new workforce the empowerment of analyzing and learning from the data, not just the job of collecting and storing it.