From all the recent airplay on the topic of ‘analytics’, trending under the guise of rapidly proliferating marketing buzz-terms such as ‘Big Data’ and emerging computational practices such as ‘Machine Learning’, one might easily get the notion that ‘business analytics’ is a recent innovation.
My mission, and the intention of this humble blog is to provide a balancing voice in terms of the need to match and pace the technical implementation of analytics solutions with organizational management best practices, which is a technically informed social discipline. In this sense, we might consider that a typical organization is a type of ‘sense making machine’, albeit one whose operating system is composed of processes and whose software is the collective understanding of a purposeful community of people.
Hence today’s topic: retrenching to fundamentals, the better to understand the interplay of technology factors (both machine-based and practice-based such as implementing algorithms) from organizational considerations, which involves an understanding of human behavioral factors associated with transforming data into actionable information and knowledge. The intent is to highlight that advances in algorithmic approaches and computational power need to be considered as much of a challenge as a solution, in that without uniquely human modes for understanding, communicating, and sharing meaning associated with analytics, such solutions are, it goes without saying, inherently meaning-less, if not dangerous in terms of the potential to cause confusion, discord, and conflict through the creation of unnecessary ambiguity and complexity.
With some imagination, we can consider that ‘business analytics’ is intrinsic to humanity itself as it has evolved technical and social behavioral complexity. Indeed, by taking the ‘big picture view‘, we avail ourselves of an opportunity to separate marketing hype from the pure factors and challenges concerning the organizational management of business anlaytics.
It can be argued that ‘analytics’, in its purest sense, pre-dates the birth of agriculture to primitive hunter-gatherer tribes. Keeping a crucial handle on the seasons and stock of available foodstuffs for tribal communities involved active planning in negotiation with the community. When conditions changed, after consulting with local ‘experts’ and ‘skilled stakeholders’, leaders needed to plan group movements to optimize the survival, if not comfort, of the community.
The birth of agriculture raised the stakes: farmers were the equivalent of ‘neolithic geeks‘, a new breed of concerted ecological technician. Crop farming required not only the development of specialized tools, but a careful handle on a range of sensitive data points, including local seasonal rainfall and temperature variability, field yields, crop growing periods, harvest efforts, soil fertility, etc. The notion that many ancient monoliths and monuments served at least a partial purpose as astronomical calendars makes quite good sense when considering there were often slim timing margins for yearly agricultural planning, especially in colder Northern European climes.
The success of agricultural ‘analytics’ led to the birth of permanent settlements, villages, towns, and later proto-urban human communities, with all their associated financial and political complexity. Established settlements led to the growth of trade, but also competition and warfare. Markets and competitors led to greater demands on a host of technical activities which we could perhaps call nascent ’supply chain management’ challenges: trade route maintenance, welfare-based taxation, storage and distribution of goods, competitive pricing, insurance, workforce planning, and the outfitting and supply of military forces.
At base, the perpetuity of such increasingly complex communities began to depend more and more on tracking, manipulating, and interpreting pure data into information and knowledge. The one constant: the need to resolve and manage the needs of diverse, distributed stakeholders with complex technologies and procedures, all via the scrying lens of anlytics, whereby the collection and management of complex datasets becomes information (as data in purposeful context), decisions (as actions resulting from the interpretation of information), and knowledge (as the deep insight into complex systems gained by observing dynamic feedback in the perception-action cycle).
It is not my intent to over-play the point, however… We can, and should, flash forward to the Early Modern Age. Being based, myself, outside Amsterdam, the locus of global trade circa 1600, one need only pause at The Waag in the Nieuwmarkt, as I did just last evening, to realize ‘Big Data’ has been a 400+ year ‘sudden superstar’ in driving business outperformance.
In addition to hosting a lovely restaurant these days, back in the time of the Dutch Golden Age, the Waag was then a center of tracking and regulating goods arriving and leaving the city proper. As such, diligent record keeping allowed the ‘Heren’ (in Dutch, the ‘Sirs’ or Gentlemen) of the Dutch East India Company (VOC), the first true shareholder owned multinational, to engage in financial and logistical planning. Such an important ‘information gateway’ also required a professional class knowledgeable not only about trade and regulations, but skilled in weights and measurements, particularly the now lost art of quickly being able to ‘size’ a bulk of goods in terms of weight and volume by quick visual shortcuts and mental estimations. It was indeed, at the time, somewhat of a marvelous parlor trick to be able to accurately judge the weight and volume of just about anything purely based on a quick visual purview.
What is perhaps most significant about the Waag, in it having been an ‘information portal’, beyond being a simple physical gate, is that it was as much a part of the success of the Dutch Golden Age as shipping technology was. In other words, the bureaucratic ability of the the VOC Heren and Amsterdam Burgers to collect, process, store, and interpret information for planning purposes was part and parcel of the birth of globalization in Amsterdam, this swampland-transformed-to-17th-century-trade-mecca.
Significantly, the data being collected and the resulting information had a deep contextual connection to the processes and procedures associated not only with the management of the VOC and the management and growth of Amsterdam as a city, but also to perpetuity and expansion of the VOC as a global shareholder venture, with all the information sharing aspects that we associate with shareholder capitalism. Thus, the ‘data agenda’ surrounding the Dutch Golden Age must be considered as a triumph of information management as much as the result of shipping technology and robust adventurism.
And what message does this leave us with, the notion that business anlaytics is a multi-millennial sudden success story? The significant message is that efforts to introduce new methods for tracking and interpreting data, even when automated, must have intrinsic relevance, in an organic sense, to organizational context. In other words, there must be a shared and understood purpose for the interpretation of data and information. Without this, given the explosion in the technical ability to track and interpret data, there must be an inherent organizational capability to consider, interpret, and, when needed, evolve in keeping with new meanings and interpretations of the developing environment.
Not a week goes by when I do not hear a new horror story or cry of desperate help: organizations are drowning in data and employees are increasingly finding themselves in disarray due to conflicting informational interpretations from the data tidal wave. The upshot is the Indian parable of the blind men and the elephant: with numerous variations, one blind man believes they have found a tree (the leg), one a plant (the ear), one a brush (the tail), and one a plow (the tusk). Without the organizational processes (in social, cultural, and political form) to compare and resolve interpretations of data, there is as much danger as there is opportunity in the implementation of anlaytics solutions.
As the saying goes, for want of a nail, the kingdom was lost. In this case, the nail is to plan analytics initiatives putting organizational context (people and processes) first, behind technical innovation. Thus, going back to our neolithic farmer: the advanced plow is of little use when the crop fails due to misunderstandings in the harvest planning. And here, we are reminded of numerous IT analytics and ERP implementation projects gone wrong… The moral to our wide reaching story is thus a simple plea for managers to understand that analytics is primarily a sense-making activity, and thus is social, whatever else one might be led to believe by those seeking to sell pure technical implementations.