Across the Rubicon

The Corporate World Needs More… Archaeologists? Data Storytelling Across the Ages

Thu, 24 Feb 2022 19:24:37 GMT

The Corporate World Needs More… Archaeologists?

Data Story-Telling Across the Ages

It’s probably hard to imagine two worlds more different than those of business and archaeology. The business world is one of executives, industry, technology, and finance – full of fast-paced meetings and tailored suits. Archaeology, by contrast, is a world of academics concerned with antiquities and esoterica. Our office attire typically consists of jeans and boots adorned by mud, bug-spray, and (very often) bandages. We spend much of our time in libraries or labs when we’re not off in the middle of nowhere digging very precise holes to find little bits of stuff.

Popular fiction describes archaeologists looking for lost civilizations, cities of gold, or vast treasures – and this amuses us greatly. In reality we are looking for the minutia of daily life of regular people, and rarely the kings and queens (although this is what makes the news). What might otherwise be known as trash dumps (we call them “middens”) gives us a window into what people had, used, ate, and discarded.

The whole discipline of archaeology, and the reason for all of our methods, is geared towards finding ways to collect as many forms of data possible to understand and fully describe events in the past. All of human behavior leaves something behind, and the questions are how can we find it and how much of it can we collect?

Curiously, the reason why we do these admittedly strange things is exactly why the corporate world really needs more archaeologists…

We tell very detailed stories about the past, using large amounts of meticulously curated data.

Sound familiar?

The Data of Archaeology

As fascinating as pyramids, temples, and royal riches may be, archaeologists discovered a long time ago that the big things provide limited information. Palaces and riches only tell about the elite and the powerful or grand events, but not what the vast majority of life was like. The majority of archaeologists spend their time looking at or for the overlooked – what historical archaeologist James Deetz famously wrote about with “In Small Things Forgotten” describing the everyday artifacts of American colonial life.

It turned out that the data that really told the stories about the past were in all of those little details. To really know how history was being shaped and how those grand cities came to be, we had to find the underlying patterns in the ephemera of the details.

We also learned over time that looking at artifacts alone wasn’t enough either. They were only a part of the data, but not the whole story. Looking only at artifacts is like reading the words in a novel after they’ve been shuffled – it may all be there, but still makes no sense. You need structure and context. We needed to understand the processes behind how those artifacts were created and used, and learn how different behaviors and effects of time influenced the distribution of those objects across the landscape.

We started studying the natural and cultural processes that gave rise to the patterns of artifacts as data. Archaeologists started seeking out expertise in everything from geology to climate to civil engineering to better understand what processes were transforming the artifact data and the technologies involved in their production and use.

You’ll find archaeologists specializing in flint knapping, glacial geo-physics, agricultural or industrial technologies, soil or food chemistry… all to tell more detailed stories about how people did the things they did. There are even more specialized computational archaeologists, archaeo-chemists, archaeo-botanists, and geo-archaeologists that have done advanced studies in multiple academic disciplines (and occasionally multiple degrees, such as ourselves) to be sure we can apply the most advanced tools and methods to tell even more detailed stories.

As archaeologists’ stories about the past got ever more detailed and sophisticated, though, it also became apparent that there was still a missing element. Understanding what data is there in the artifacts and how different processes affect the archaeological record gives us a very detailed picture of the past. That’s all well and good, but it still doesn’t answer the most important question and the reason we really do all of this grunt work – what does it mean?

The most important question to archaeologists is to answer why the past occurred as it did, and (for some of us anyway) how that all brought about the way that people are now. That grand and overarching why is the same as it is in all of science and philosophy, and of course is the most difficult question of all. To even attempt to answer why, though, scientists come up explanatory theories and hypotheses with which to test them. Scientists always have to have these sorts of theories. The theories about why guide our understanding of the what and how questions, which all of the data and methods are meant to support.

An Archaeology of Data

The point of this article isn’t to teach people about archaeology, though. Our point is that archaeologists are highly specialized data story-tellers, and to share the lessons we’ve learned in the field over more than a century of creating detailed stories about events and people we cannot directly observe. We have had to find ways to extract as much insight as possible from limited and very unstructured data.

To do so we’ve had to confront the complexities involved in data selection, collection, and feature construction. Archaeologists have spent a lot of time thinking about how to transform data into knowledge without the luxury of having much of any control over data availability or quality. As more and more organizations try to find ways to extract insights and value from data, they are running headlong into the same sorts of questions.

What archaeologists found was that trying to answer questions and gain meaningful insights had to carefully balance all three types of questions – the what, how, and why – rather than trying to focus on any one of them. An organization that is trying to be data-centric in its assessment and decision-making needs to do the same. It needs to carefully consider the intersections of data, method, and theory as being merely facets of the same insight. Whether or not you actually hire archaeologists to be your data story-tellers, you do need to find people that take the same holistic view of the stories in the data.

After all, archaeologists are out there telling detailed and data-driven stories about events that were hundreds or thousands of years in the past just from a few bits of ancient debris.

Odds are pretty good that our approach could probably find some patterns and insights in your data as well, don’t you think?

Reconsidering Nothing - Sometimes Nothing is Something

Thu, 17 Feb 2022 13:40:28 GMT

Reconsidering Nothing

Sometimes Nothing is Something.

Some might say that one is the loneliest number, but more often than not it’s zero that really gets the short end of the stick. This post is dedicated to a topic that frequently gets overlooked and is often under-considered – the absence of something. Very often, it’s just as important for us to know where something isn’t happening as it is to know where something does occur. So important, in fact, that it’s crucial to understand that zero isn’t really defined as the absence of something – it is actually the presence of nothing.

The discovery of zero marked a huge advance in our ability to describe the world. The first known use of a symbol zero as a numerical placeholder were the Sumerians, roughly 5,000 years ago, to indicate the absence of a number in place notation. It took another few millennia before zero became a numeral in its own right to indicate nothing in its present sense.

The Babylonians adopted it from the Sumerians, but historically a numerical zero arose independently in ancient India, China, and Mesoamerica as well. For archaeologists, especially epigraphers (people that decipher ancient scripts), the concept of zero is a huge technological indicator. It’s something that previously – i.e., for hundreds of thousands of years – humans simply did not appear to do. Behaviorally, from a common sense view, it really doesn’t make much sense. Why make special note that something is not present?

Signifying Nothing

The discovery of zero marked a cognitive and technological shift from enumeration to calculation. The true birth of mathematics in a very real way, discovering zero permitted a concept of nothingness or absence that had both philosophical and practical importance. By integrating a concept of what isn’t there with the observable things that could be counted, people could adequately describe the totality of the environment in discrete terms.

More importantly, they had a means to calculate what will or might be. In the ancient cultures where we find a discovery of zero, we begin to see enormous advances in astronomy and calendrics – both of which were used to make predictions as well as to record events. It marked a civilization’s shift towards devoting significant resources to anticipating events as well as describing them.

Take for example the complex system of calendars created by the ancient Maya, most notably the famous Maya Long Count calendar . It uniquely identified every day beginning with the date of their creation myth using a combination of base-20 and base-18 numerical systems! A symbol for zero was used to mark the initial member of the sequence for each place notation, allowing the calculation of for dates within calendar cycles well into the future.

Significantly though, it also allowed them to project the dates of important astronomical and ceremonial events into the future as well as into the past. They were able to calculate the timing of these events with remarkable accuracy, which guided everything from their ceremonial cycles to their agricultural efforts to their political events.

Being and Nothingness

A somewhat more subtle cognitive importance to zero is in recognizing nothingness as a category of data. Most often, as either scientists or analysts, we focus our efforts on where there are concentrations of values within our data. We are looking for patterns or processes with large and diverse bodies of information – e.g., calculating, describing, computing, and understanding what they mean. In doing so, we prioritize data that suggest the presence of something of interest. Those are the observable elements that we build our world around, so that is clearly where the “action” is. Of course we should be focused on what is there. Shouldn’t we?

What often goes overlooked is that you can’t know that there is a pattern unless you also know where that pattern is not occurring. In other words, a pattern is a pattern only if there are contrasts and boundaries – i.e., those gaps of nothingness that establish borders and constraints. It’s just as important to know where the pattern reaches its limits – i.e., where it slows or stops.

It isn’t sufficient if the pattern is only delineated by a concentration of somethings in a field of unknowns. That is why we have separate classifications for unknowns (e.g., NULL or ) versus nothings. Zero is an enumeration of known nothings. Where are the pockets or holes within the field of information that we’re calling a pattern?

Interestingly, there is an entire field of mathematics called homology that is dedicated to the study of a shape’s holes. Specifically, how shapes in arbitrary dimensions may be associated by understanding where the shape (or manifold) does not exist.

If you haven’t described the known holes in your data – where you know something is absent – you’ve only partially identified a pattern.

This is not merely a philosophical observation, though. When we focus only on the patterns of presence in whatever we are studying, there is a strong proclivity to dismiss the findings of when and where something isn’t found. Just a few years ago, there were a number of articles published about a phenomenon of self-selection biases against negative findings in academic research. In short, nothing would be published if an expected pattern had not been found by a project.

The result, in some cases, was that a number of negative patterns were not being identified and published. After all, who would want to risk their professional reputation by publicly announcing that their project failed to find any significant patterns? Similarly, we are frequently taught that it isn’t proper to prove a negative so it’s rare that researchers will wade into arguing that there is a persistent pattern of absence in an area where some effect is expected.

While it’s true that you can’t argue for a negative pattern based on a lack of evidence, it’s actually important to indicate cases where there is proven evidence for an absence of pattern. Not to do so would be effectively “cherry-picking” positive patterns to the exclusion of broader, and potentially more important negative patterns.

Nothing Ventured, Nothing Gained

The social pressure, whether in the academic or professional world, is to find and publish positive findings and insights – to demonstrate something for all the work involved. Aside from the bias towards finding positive patterns there is also a reluctance to admit when some approach, method, or experiment failed. We don’t like to admit that what we were doing didn’t work, that the results were inconclusive, or that the expected wasn’t found. In many cases, it’s actively discouraged. In general, we simply start into something else and move on.

People like to quote aphorisms that failure is “the best teacher” or “the paving stones of success” or some variant on how many ways Edison found to not make a light bulb. It is actually quite rare that anyone wants to be the cautionary tale. Most people are not willing to publish their failed experiments and rejected hypotheses, or to acknowledge that the time and resources didn’t produce results.

Unfortunately, this attitude is pernicious and the trend is overtaking the most crucial aspect of research – to understand the unknowns. Businesses want guaranteed, or at least safe, return on investment and many grant-funding organizations are following suit. Grant-seekers often have to supply some form of “capacity” statement, along with lengthy and time-consuming documentation of prior work and methods, to prove that an investment will have a certain clearly potentiated return.

In effect, the expectation is that any funded work must have an already known or expected outcome. That is, however, the opposite of research and promotes the dreaded “group-think” that true research is supposed to challenge. Why exclusively fund experiments we know will work, rather than expand on what we don’t know and find new and unexpected patterns? What insight is to be gained from aiming at the known, or from constraining all new work to follow well-trodden paths?

There is no innovation under those constraints, and failure to produce becomes a cul-de-sac rather than a lesson.

Saved by Zero

So what do we do?

Firstly we need to acknowledge, in this ostensibly data-driven world, that all information gained is of value. Especially so when that information involves either the unexpected, the missing fields of information, or the clear indications that some hypotheses or methods are simply dead ends.

Secondly, we need widen our scope of perceptions to realize that negative findings are made valuable when they illuminate contrasts, constraints, boundaries, and gaps. Recognize that the negative space of known nothingness is in itself a pattern.

And lastly that we must embrace negative of inconclusive outcomes for what they are really doing – i.e., moving us forward by clearly illuminating what does and does not work, showing where information is both present and lacking, or pointing the way towards potential areas of interest for future work.

If we consider the enumeration of nothingness in our data to be significant places, as we do with zero in numerical systems and calculations, then we may find that we learn to take that cognitive leap like the Sumerians and the Maya did centuries ago.

We just need to let the lonely yet powerful zero help to guide our perceptions of data – finding patterns to the positive, even if nothing was what we identified.

Is it Sustainable Yet? Examining the Multiple Meanings of ‘Sustainability’

Wed, 09 Feb 2022 13:41:17 GMT

Is it Sustainable Yet?

Examining the Multiple Meanings of ‘Sustainability’

One wouldn’t think that it would be overly difficult to agree on what a word means, but a remarkably large number of meanings seem to get attached to sustainable. There are nearly as many definitions of sustainable and sustainability as there are suggestions on how to achieve them. Not surprisingly, there is also a rather strong correlation between how the terms are defined and the problems or objectives they are used to discuss.

Therein lies a bit of a problem – it’s rather difficult to arrive at any consensus on a subject if people are using the same word to mean different things. Since policy debates require consensus, this is not a trivial problem.

The term sustainable development was coined in a 1987 report by the World Commission on Environment and Development titled Our Common Future (a.k.a. the “ Brundtland Report ”) sponsored by the United Nations. In it, sustainable development was defined as:

…development that meets the needs of the present without compromising the ability of future generations to meet their own needs.

The report goes on to recommend numerous policy priorities directed at various social, economic, and environmental concerns in pursuit of that aspiration of sustainability.

Over 30 years later, the precise meaning of sustainable and its practical implications remain subjects of some debate. One fairly recent analysis (Moore et al. 2017) identified more than twenty distinct definitions of “sustainable” being used in articles on sustainability – and that’s just in healthcare! Currently, the UN’s Sustainable Development website lists seventeen distinct goals identifying targets for sustainability efforts.

It really isn’t surprising that both policymakers and the public get a bit vexed by figuring out what sustainable means and how to promote it in practical terms. Conversely, it also leaves a lot of room for critics and opponents of sustainable development efforts to gain traction.

If a word can mean anything,

then it essentially means nothing

This sort of ambiguity is not unexpected for such a complex subject. It’s fairly common in academic research for terms to take a long time to arrive at common definitions and meanings. At one point, for example, there were nearly 200 definitions (Kroeber and Kluckhohn 1952) being used by anthropologists for the term “culture” – one of the most important terms in the field of anthropology.

The word merely became a stand-in for whatever aspect of culture someone was studying, which obviously would not apply for someone studying some different aspect. Unfortunately, that also meant that there was no way to evaluate between them. Each definition only worked within its own scope of interest, and rarely made any sense when applied to anything else.

Sustainable is falling into much the same predicament. It is defined largely in terms of whatever aspect of sustainability is being addressed at the time. Each meaning is distinct, often contradicting other definitions, and overly specific to a narrow subject area. The lack of common definition, however, tends to be a major obstacle to properly framing the problem and can lead to largely unproductive debates.

The sustainability movement is still going through just these sorts of growing pains. While the idea of sustainable development is more established, equivocal definitions can’t frame the problem in a way that easily points to obvious and practical steps to achieve its goals.

Sustainability, an archaeologist’s view

The truth is that, despite appearances to the contrary, most definitions of sustainable are not all that different once you look behind the jargon. The common yet often implicit theme being expressed through them all is a simple question – what happens if it isn’t sustainable? That consistent thread is very familiar to those who have studied archaeology.

Why would that be so familiar to archaeologists? Simply because the majority of the societies that we study are no longer here. At some point, their cultural institutions and practices could not be sustained. When archaeologists talk about the “collapse” of a group or civilization, that’s what we mean.

It’s not that they died off (although that did sometimes happen) but that their larger social institutions and normal behavior were no longer feasible to continue. Even after Rome fell, there were still Romans trying very hard to figure out how to get by and what to do next. People are highly adaptable, but institutions tend to be very slow to adjust to change.

While there is no single cause for a civilization’s collapse, in nearly all cases the culprit is that the institutions failed to adapt to changes in their physical, social, or political environments. Those should sound familiar to people working in sustainable development – they’re essentially the three “pillars” of sustainability. They can also be thought of as environment, social, and governance (ESG) factors for those in the business and finance worlds.

Archaeologists study the historical evidence of the successes, and the failures, of how people used their environment and resources or adapted to changes in climate and landscape. We study the remains of how people navigated periods of political and economic upheaval. Archaeologists have devoted a lot of time to studying what successful and unsuccessful adaptation looks like.

Our contemporary concerns are not significantly different from those in the past. Only the particulars have changed. There is little new under the sun, after all.

Sustainability, adaptation, and the three-body problem

The practical upshot is that sustainability is really just the ability to successfully adapt to change. Sustainability is a strategy rather than an outcome. Overly narrow or technical definitions can create obstacles to success by promoting fixed targets and metrics. Those may be easier to measure and manage, but they don’t sufficiently address the underlying need for adaptability.

Worse yet, such plans tend to give the impression that our problems can be best tackled by preventing change instead of adapting to it. Too many seem to view sustainability as a means to either limit change or otherwise continue some version of current needs and wants into the future. That problematically assumes that the needs of the future are merely an expansion on our needs now.

That isn’t quite the point. The point is that we really don’t want to go the way of Rome… or any of the numerous other empires and civilizations that tried, and failed, to continue on as though they were immune to changes around them.

For sustainable development to emerge as a collection of successful adaptive strategies, we also need to consider the three pillars – environmental, social, and economic/political factors – as interconnected facets of the same problem. Confronting only one dimension of the problem is futile, since each is affected by the others. We cannot successfully address environmental issues without simultaneously balancing them against their socio-political and economic consequences. Addressing a just single problem area results in a never-ending cascade of reactions with no feasible solutions.

In many ways, balancing strategies between the three dimensions of sustainable development can be seen as the classic three-body problem . To find a stable state of balance between the interacting requirements involves continuously adapting dynamic solutions, rather than fixed or static remedies. This is not to say that current aspirations such as “net-zero” emissions or circular economy resource protocols aren’t worthwhile. It does mean that considering those strategies only in terms of the need to make our current systems viable… isn’t really a viable long-term solution unless we successfully balance them against all other interacting systems. It doesn’t help to solve one problem by creating or exacerbating others.

Sustainable strategies

When viewed as strategies of adaptation, sustainable development becomes a systemic imperative towards balancing needs and resources across all aspects of society. Current definitions for sustainable and sustainability have an unfortunate tendency to flatten the terms towards specific technical goals or solutions. More worrisome is the tendency to think of it as a means to preserve and maintain the status quo through fixed measures and policies. Instead, we need to be thinking about how to evolve our current practices within constantly changing conditions.

Otherwise, we could find ourselves becoming yet another case study for future archaeologists…

References

Kroeber, A. L., and Clyde Kluckhohn. 1952. Culture: A Critical Review of Concepts and Definitions. Vol. 1. Papers of the Peabody Museum of Archaeology & Ethnology 47. Harvard University.

Moore, Julia E., Alekhya Mascarenhas, Julie Bain, and Sharon E. Straus. 2017. “Developing a comprehensive definition of sustainability.” Implementation Science 12 (1): 110. https://doi.org/10.1186/s13012-017-0637-1 .

World Commission on Environment and Development. 1987. Our Common Future. Oxford: Oxford University Press. https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf .

What Does Social Mean? Why Social Impact, Social Responsibility, and Social Factors Often Miss the Mark

Thu, 20 Jan 2022 18:28:11 GMT

What Does "Social" Mean?

What Does Social Mean? Why Social Impact, Social Responsibility, and Social Factors Often Miss the Mark

We have been reading through lots of articles, discussions, and forums for professionals in fields like CSR (corporate social responsibility), ESG finance (environmental, social, and governance), sustainable development, and such. It’s striking just how many ways social is defined or measured. As anthropologists, reading these discussions brings on a curious mixture of empathy, humor, and frustration. There isn’t much about those definitions of social that would be overly familiar to anthropologists or other social scientists.

In recent years there has been an increased interest in social factors from businesses, policy makers, and the public. The effects of social media on public debate, the rise of influence and misinformation campaigns, environmental and social effects of industries, and conscientious financial investing have all raised interest and engagement in understanding the social consequences of economic and political activities. This is, in our opinion of course, to be encouraged.

The increased attention in professional fields are signals that both public and private sectors recognize that they’re in the same boat as the rest of the public. An increasing number of people are intentionally and strategically voting with their money, as well as their ballots, as an effective way to promote social change. We all do whether we realize it or not, but that’s a topic for another post. A large number of folks want a more socially responsible world, so organizations and institutions are evaluating social impacts.

The question is – how? Social is one of those words that everyone knows and uses, but rarely take the time to really think about. We all just know what “social” means, right?

Truth is that what social means is surprisingly hard to define precisely. There is a lot of academic literature (spanning hundreds of years) debating the nature of human societies and institutions, and even more trying to figure out just how it all works.

Unpacking the Social

What we’ve consistently found in the way the social is defined and discussed is a tendency to focus on specific actions and outcomes for particular communities and situations. They’re narrowly defined, discrete, and limited to direct and observable effects. This makes a certain sense from practical measurement and management perspectives but doesn’t really capture the breadth of social factors or scope of their consequences.

Obviously, we can’t distill everything into a short post like this. There are, however, a few key points that need more attention:

Everything that involves people is going to have a social aspect to it. All human activities are innately embedded within social contexts.
If it is social, then it is a network.
Effects will propagate through that network, often in unpredictable ways. Things that impact one part of a network can have consequences for another, even if seemingly unrelated.
It is often in the furthest connections or weakest links in a social network where the greatest effects are seen. This effect was noted by Stanford sociologist Mark Granovetter in the 1970s, and has been hugely influential in the social sciences ever since.

People are inherently social, and not just in our connections with the people around us. Our sociality effects how we think, what we believe, and why we make our choices. Even when we’re not directly interacting with anyone, our social contexts imbue even our individual thoughts and actions. Our social behavior is the product of a myriad of experiences and interactions, each informed by our socially filtered information.

The foundations of that underlying sociality are communities of interacting individuals, each of which have their own collection of connections – e.g., the now-famous 6 degrees of Kevin Bacon (or, more formally, the “small-world” experiment ). While the particulars have been debated (i.e., how many degrees, what counts as a connection, etc.), the network properties of social connections are firmly established in the social sciences.

Networks have some peculiar properties simply by their nature. Most especially, the propagation of effects through a network isn’t necessarily limited by how many connections any individual node may have. As long as there is at least one connection, there is a potential pathway. This makes determining the scope of social factors particularly difficult. It isn’t a simple matter to determine just how far effects may reach.

That brings us to our last point – i.e., Granovetter’s “weak ties” in social network problems. While it is natural to look first at direct connections and effects for social factors, it’s the indirect ones where things get complicated quickly. Identifying the scope and impact of social factors need to consider those distant connections and indirect effects through weak social ties. These are the ones that often escape notice and mark the difference between intentions and outcomes.

What this means for Social Responsibility

The practical upshot, at least as far as the professional world goes, is that part of the mismatch between the aspirations of ESG and other social impact fields is in an overly narrow concept of social. This is found not only in the disparities between ESG rating methods, but also in defining the scope of social responsibility and sustainability. The social aspect is pervasive, and its effects far-reaching. It is also, thankfully, a very well-studied problem.

We just need to get everyone studying it on the same page.

Is it Data Yet? Why We Really Do Need to Think About the Science of Data

Tue, 11 Jan 2022 12:38:31 GMT

Is it Data Yet?

Why we really do need to think about the science of data.

Data is all the rage. There’s a proliferation of data analysts, data scientists, data architects, data engineers, data story-tellers. Everywhere you look businesses, organizations, policies, decisions, and processes are all touted as data-driven. One starts to get the sense of the old advertising tropes – “New & Improved! Now with XYZ…!” as though the rush to be perceived as data-driven entails an improvement. Were you not basing anything on data before and just making it all up as you went?

Well, obviously not. Data is not new, but our ability to handle it and derive insights from it has arguably been vastly improved. We now have incredibly sophisticated methods and far more computing power to wield them. There is, however, a subtle and often missed question lurking in the background. What, exactly, is data?

It may seem a specious question at first, but how we define what is and (especially) is not data plays a large part in how successful are attempts to derive insights from it. This is so important that I’m actually going to skip right to the punchline:

It isn’t data until you have a question .

Almost anything can be data, but what actually is data ultimately depends on what question you are trying to answer. Consequently, for it to be good data there also has to be a good question behind it. We all know that arriving at the right answers requires asking the right questions, but identifying what is the right data for that question can be surprisingly difficult.

A data science parable from the early 1900s

To give an illustration of why data needs a question – a why to go with the what – I’ll use what may seem to be an odd example. Franz Boas is generally regarded as the father of modern anthropology in the United States. A fascinating and somewhat controversial historical figure, Boas nonetheless had an enormous influence on the study of human history and behavior.

Boas’ early career was in natural history as a museum curator, which may have contributed to his later methods for a scientific anthropology. In short, he collected everything related to a group or culture – artifacts, stories, languages, art, folklore… all of it. There are notebooks, tomes, and encyclopedic volumes just for the catalogs of all the things they collected. Boas and his contemporaries filled museums, and entire careers were made just from cataloguing it all – over decades!

The basic intuition was that a science of human behavior had to be empirical. By gathering all the empirical data possible then, the universal patterns they were looking for should become obvious once enough data was available. It certainly seemed a reasonable approach. That’s not quite what happened, though.

Instead, most of that material ended up gathering dust and anthropologists looked for a different way to go about being a science. Why? In no small part because there was too much of it to even begin looking for patterns. Since no real question was ever formulated beyond a broad “why are people different” there was no way to know what was a pattern. They had a lot of potential data, but with no question they had no way to parse it at all as data.

Data is as data does

In an age where everyone is trying to hoover up as much data as possible in hopes that it will somehow drive value, understanding this difference becomes exceedingly important on multiple levels. It’s critical to know what questions are being asked, and whether these volumes of data are really worth the costs. Not just economically, but also ethically – should we be collecting data without a clear idea of why? …are there collective benefits to go with those costs?

For potential data to be useful data, there needs to be a question. Data scientists may sometimes refer to this as feature selection or feature engineering, but what it really means is that for a measurement or observation to be data it has to be directly related to an outcome or question.

There has to be a context to make observations into data. All to often in the rush to chase value that context is an afterthought. In reality, to be data-driven means being question-driven from the onset. It’s the question that determines whether data can become information, which ultimately is the goal.