Your Assumptions About Cultural Adaptation Are Probably Wrong – Andy Molinsky – Harvard Business Review
« The workplace has never been more global than today. But despite that, I often find the last thing on people’s minds when doing international work is the global element. Instead, and often for good reason, people focus on concrete and pressing work details: finishing that PowerPoint deck, running the financials one more time, or planning the logistical elements of foreign travel. As a result, they tend to follow « gut » theories €” what they assume to be true about adapting behavior across cultures.
The problem is that these gut instincts are often false, misleading, and difficult to apply. In studying this topic for the past decade and working with hundreds of professionals from across the globe learning to adapt behavior, I’ve identified three such « myths » of global adaptation: «
The real key to crossing cultures isn’t learning about differences: it’s being able to adjust your behavior
to actually take the differences into account.
The point is not to completely avoid « acting like the Romans, » but it’s to develop a way to customize or personalize how you act in the new culture so you act appropriately but at the same time maintain your own personal integrity. Adapting behavior in another culture is not like trying to hit the absolute bulls-eye of an archery target.
I can’t tell you how many times I have heard this from managers and executives: that the key to being effective is just « being yourself. » Of course, there is nothing wrong with « being yourself, » but at the extreme, this piece of advice completely ignores the fact that there are real cultural differences you must take account for when working overseas.
Big Data and Big Change Management: A Path Forward
« During the event, MIT researchers Alex “Sandy” Pentland and Erik Brynjolfsson talked about the big picture of big data and the finer aspects of utilizing data, from building trend models based on free Google data, to combining disparate data sources to create new business models.
But in all the classroom discussions that surfaced around the big issues from big data €” privacy, security, costs, infrastructure, data volume, data quality, and data governance €” the reality that many organizations grapple with is change management: whether or not they can manage the human and process changes necessary to make the most of their analytics initiatives. »
Bottom line: the real question is whether or not they can change the culture of their organization.
Consider the experience of one course participant, who talked about how her organization, a software company with 100 million customers, is struggling to implement a new analytics program that will enable both multi-channel customer communications and upselling. To achieve these goals, the company needs to change the way it handles sales data (replace batch processing of sales information with real time processing) and communicates with customers (replace blanket email communications with more personalized, one-on-one communications).
To get people to do what you want them to do €” in essense, to get buy-in on analytics €” Pentland talked about creating a model that combines the intuitive thinking employed by HiPPOs (highest-paid person’s opinions) with quantitative reasoning from “the quants.” Bringing the two together enables organizations to explore data, visualize relationships and understand in a human, intuitive way what data is telling them.
Brynjolfsson’s approach to managing process change is a pen and paper exercise that takes the form of his Matrix of Change model (Figure 1: Matrix of Change). The model helps companies determine three key points: Which processes need to change, which can stay the same and how processes interact. It does this by pitting old practices against new ones to determine which are opposing and which are reinforcing.
There is another angle to consider: whether initiating changes is going to produce results that make it worth the trouble. “Not all change management is worth doing,” Brynjolfsson told the class. “In Great Britain today, maybe changing driving direction isn’t worth it. That becomes an easier judgment to call.”
The Problem with Accounting
estimates and measurements are one of the most frequently identified trouble spots by the U.S. auditor watchdog, as managers and accountants have to spend more time focusing on the fair value of financial instruments, goodwill impairments and intangible assets in the new economy. »
accounting has the unenviable challenge of applying a tool set designed for the tangible economy to the rapidly-changing and radically-different intangible economy.
The accounting profession is trying to adapt to this changing world by focusing more intensely on fair value rather than historic costs. But the truth is that accounting (as it is conceived today) will never be able to fully account for many of the intangibles in a business
ICounting uses value transactions (the exchange of knowledge, trust and solutions) as a way to understand the health and success f an organization. Each has its place.
Comment les Big Data ont transformé Qantas Airlines | Petit Web
« Lancé en 1987, le programme Frequent Flyer de Qantas permet à la compagnie aérienne de réunir des milliards d’informations sur ses clients, non seulement sur leurs voyages, mais aussi sur leur situation bancaire (la carte est aussi un moyen de paiement) ou leurs achats du quotidien (la chaîne de supermarchés Woolworths est intégrée au programme depuis 2010). Restait à exploiter toutes ces données pour en tirer de la valeur. « Nous avons voulu utiliser ces data pour aller bien au-delà des campagnes emailing traditionnelles et générer de la valeur dans tout le business de l’entreprise » explique Vaughan Chandler. »
Pour cela, Qantas s’est doté d’une équipe « Insight and Innovation » dédiée à l’analyse des Big Data. Celle-ci n’est pas dépendante du département marketing, mais rapporte directement au CEO de Qantas Loyalty. L
Avec les informations à leur disposition, les équipes « Insight and Innovation » ont développé des modèles prédictifs, pour évaluer la probabilité qu’un client choisisse Qantas ou un concurrent lors de son prochain vol, et adapter les actions marketing en conséquence.
« Trop d’entreprises confondent « analytics » et « reporting ». Il ne suffit pas de connaitre ce que veulent nos clients, il faut savoir ce qu’ils voudront demain. Les analytics, c’est de la prédiction et un outil au service du changement
Les informations collectées sur chaque voyageur sont aussi accessibles aux personnels au sol et en vol, à travers un terminal mobile. « C’est une interface très simple, qui ne présente pas toutes les informations disponibles, mais seulement celles qui sont pertinentes –
Avant, la taille était un avantage, maintenant c’est devenu un handicap.
Mais nous avons un avantage concurrentiel : la taille critique pour déployer de la « customer knowledge intelligence » et agir ensuite à grande échelle. »
Le plus difficile est de donner les bonnes informations à la bonne personne au sein de votre entreprise. » Cette « bonne personne » ne se trouve pas forcément au sein du département marketing… les analystes doivent donc s’affranchir des silos.
« avant de se lancer, il faut s’assurer que votre organisation est prête aux changements impliqués par les data et surtout, qu’elle le souhaite. Les data doivent tout changer dans l’organisation, pas seulement le marketing ou les analytics. »
From talking in terms of systems to talking instead of systems
« Much of the current writing on organizations adopts the language of systems thinking – although what this means in an organizational context differs considerably from writer to writer. And management theorists and practitioners are not alone in viewing organizations in this way. Journalists, politicians, inquiry chairmen, and other commentators regularly refer to « the system », or « systemic failure » when pronouncing on events that hit the headlines. So seeing organizations as systems, which have the capacity to act in some way separately from the actions of ordinary people, appears natural and straightforward. But is it? »
The characteristic patterning of people’s thinking and acting (often reified as « the culture ») similarly emerges from this same conversational process.
In other words, the reality of organization is being continuously (re-) constructed in the currency of people’s present-day interactions: A dynamic network of self-organizing conversations, which does not respect boundaries €“ whether those implicit in the notion of a formal organization or others which define the supposed limits of this or that « system ».
First, many practitioners and academics seem to find it difficult to conceptualize organization as existing solely in the currency of people’s day-to-day conversations.
So what about the formal €˜trappings’ of organization €“ that is, the policies, structures, systems and so on mentioned earlier? When formally announced, these represent generalized and idealized statements of what those with formal authority have endorsed as the official way to proceed – imprints of the past conversations through which such decision-making was carried out
Although many of these formal artefacts will survive over time, it’s their perceived meanings and felt materiality that are important to what happens in practice, not the fact that these have a physical existence
The argument goes that, if you don’t accept the notion of organizations as systems, all that you’re left with is a collection of autonomous or atomized individuals acting independently. But this is not the case at al
Seeing organizations in these terms – as the emergent patterning of power-related interactions between interdependent people – more accurately reflects and wholly encompasses the €˜real-world’ dynamics of organization.
Individuals’ identities are continuously formed and re-formed in the same process of ongoing conversational interaction in which teams come to be recognized as such, and through which (the ongoing process of) organization emerges.
Since organization is (re)constructed in the currency of present-day interactions, everything necessary to judge what might be helping and hindering current practice and performance is present in those conversations €“ or else is conspicuous by its absence.
In popular discourse and from a systems perspective, the emergent patterning of day-to-day interactions and behaviours is thought of as being a property of « the system » (i.e. the result of a « systemic » cause), which acts in some way €˜over and above’ the day-to-day interactions of individuals. But this patterning is recreated spontaneously in the moment of people’s current interactions. It is not stored anywhere.
And so, instead of thinking in terms of imaginary systems and seeking to act on imaginary wholes, we might choose instead to focus on the complex reality of the conversations and interactions in which we are actually engaged
The Rise of the Data Scientist: Recap of IBM Twitter chat
« On Thursday, May 9, I participated in an IBM Twitter chat with analysts, influencers, thought leaders, fellow IBMers and others on the topic of the rise of data scientists in the business and other applications of big-data analytics. The event used hashtag #cloudchat.
Here are highlights from the discussion »
A data scientist uses statistical analysis to uncover non-obvious patterns in data sets
People have been crunching numbers, engineering and analyzing for a long time, but data scientists combine these skills.
The rise of sensor driven data & other inputs has created demand for people with analytical & stat skills & tools to derive value
Best data scientists should have both the stat/predictive analysis skills and subject-matter domain expertise
math, code, curiosity+communication.
probability & statistics are fundamentals
training for data scientists should include some semantic web and linked data experience
today it requires programming skills, hence scarcity. Future = improved tools making analysis available to broader audience.
Tomorrow’s data scientists might rule the world€¦ seriously trend identification and prediction can impact all industries.
“Data science is a creative discipline. It’s knowing how to take intuition and put rigor around it that wins
context and domain knowledge guide the process. without it, there is no hypothesis.”
A McKinsey View On Whether Information Technology Matters
« Ten years ago this month, the Harvard Business Review published an article by Nicholas Carr, who argued that “IT Doesn’t Matter,” i.e., that information technology doesn’t create strategic advantage. To be sure, there are plenty of mundane IT applications€”expense reporting and benefits enrollment systems, for example€”but if IT categorically doesn’t matter, how does one explain, say, Google’s success? After all, Google went from a graduate student research project to over $50 billion in annual revenues and a market cap of over $300 billion Wednesday in large part by leveraging IT embodying innovative algorithms delivered cost-effectively at scale. «
He observed that “what makes a resource truly strategic€¦is not ubiquity but scarcity.” In other words, if every firm has access to the same servers, storage, networks, or packaged software (today one might add cloud infrastructure, platform, and software as a service to the list), then no firm can gain the upper hand by using them.
On the other hand, web innovator and venture capital general partner Marc Andreessen contends that “software is eating the world
,” and Kleiner Perkins general partner Mary Meeker lists numerous industries being “re-imagined”
According to Forrest, old IT addressed labor automation, individual worker productivity, and non-human scale computing; new IT should focus on digital products and services, team productivity, and business model transformation.
Forrest’s research shows that individual worker productivity improvements€”for example, using desktop computers for financial analysis or document preparation€”are now only responsible for about ten percent of knowledge worker productivity growth, down from a high of thirty percent a few years ago.
Digital products and services€”either purely virtual or digitally driven€”are one category of new IT, exemplified by the enormous wealth creation and valuations of digital-native icons such as Google, Facebook, and Twitter.
Team productivity growth offers opportunities beyond individual worker productivity, Forrest says, whether for internal collaboration, or for collaboration across supply chains or networked enterprises.
Business model transformation may well be the most powerful use of IT. Using Amazon as an example, Forrest lists five major levers:
Production and operations optimization
- Shaping customer preferences
- Transforming underserved markets
The Upside of Negative Thinking
« Belief in the power of positive thinking runs so deep here in America, it’s practically in our DNA.
Beginning with mid-century advice classics such as Dale Carnegie’s How to Stop Worrying and Start Living and Norman Vincent Peale’s The Power of Positive Thinking, and continuing in recent years with bestsellers such as Rhonda Byrne’sThe Secret or Deepak Chopra and Rudolph E. Tanzi’s Super Brain, positive thinking (or « positive psychology ») has cemented itself not only as a fixture of popular wisdom but as a kind of social imperative as well. An optimistic outlook, it is believed, leads to success and health and ripples out to other people, while a pessimistic outlook leads to failure and malaise and leaches energy from those around us. »
1. Trying to be happy makes us unhappy
2. Argument is better than harmony
3. Happiness limits the full range of our emotions
4. Optimism blinds us to risk
5. Positive thinking takes us out of the present moment
What Value Creation Will Look Like in the Future
« Organizations have nearly perfected implementing the industrial model of managing work €” the effort applied toward completing a task. For individuals, this model ensures that we know what we’re supposed to do each day. For organizations, it guarantees predictability and efficiency. The problem with the model is that work is becoming commoditized at an increasing rate, extending beyond manual tasks into knowledge work, as data entry, purchasing, billing, payroll, and similar responsibilities become automated. If your organization draws value from optimizing repetitive work, you’ll find that it will be increasingly difficult to extract that value. »
The value of products and services today is based more and more on creativity €” the innovative ways that they take advantage of new materials, technologies, and processes
Value creation in the future will be based on economies of creativity: mass customization and the high value of bringing a new product or service improvement to market; the ability to find a solution to a vexing customer problem; or, the way a new product or service is sold and delivered.
Organizational structure will have to change to meet the new reality of creativity as a core component of value and continuous innovation as the mechanism to sustain it.
Supply chain management is about taking out cost and making process efficient, but, as we’ve said, this won’t be enough; value chain management is about how to create value; how to coordinate the continuous innovations of creative contributors and how to make that process efficient for the consumer and the contributor
The value chain will supplant the supply chain.
Master the machines. It doesn’t take a programmer, math whiz or rocket scientist to know that machines are taking over every form of routine work (whether physical or intellectual
Get obsessed with value. How do you define it? Measure it? How do the changes you are thinking about create value? W
Make creativity real. This isn’t an R&D effort or something to be done outside of your normal role. The skunk works is the organization. If you’re in senior management, make it clear that your organization is beginning a long term process of embedding innovation into your DNA;
Beyond social: the rise of the emergent business €” GigaOM Pro
« My real reason to move beyond social business is not just that people are confused and seem to not be learning much about social business, really, although that is obvious. Instead, we need to realize that there are a collection of forces €” including social €” that are impinging on the world of business and a suite of responses to those forces that businesses are adopting, formally and informally.
So, perhaps we should demote social to just one of an intertwined set of trends that have a certain dual nature: Like homeopathic medicine, they both cause and cure an itch. »
Social €” The rise of social tools, and the social revolution on the web, has conquered the world in the past ten years. They are ubiquitous, and has corrosively altered the context and content of media, business, and society. The adoption of metaphors from open social networks €” like following, likes, tags, and activity streams €” have started to made deep inroads in enterprise computing, but have not reached the levels of saturation or societal impacts that we’ve witnessed on the open web. And the adoption and application of those metaphors has been, to date, fairly conservative, and organized around 1990€²s notions of collaborative tools.
Ubiquitous computing €” The post-desktop model of computer use, where communication, coordination, and computing have been integrated into everyday life. This is often (mistakenly) called mobile computing, which is simply based on the notion that stable computing has already been done, and it’s the handheld devices are principally about mobility, which is wrong.
3D work €” Work has become distributed, discontinuous, and decentralized, hence, 3D. Increasingly, people work wherever they find themselves: in the office, commuting on a train, coworking in another city, in a hotel lobby, at a café, or at home on the kitchen table. The distributed nature of work is a choice for some and a necessity for others, but even the most demanding companies that still require you in the office 40 hours a week still expect you to answer a clients call during dinner, or respond to a weekend email at home
Fast-and-Loose €” Business management and organizational culture are shifting with the specific pressures of the postnormal era: the new economic realities and practices are transforming business from within and without. These include, in no particular order: a permanent boom-bust cycle, a precarious/freelancer/contingent workforce, short tenure jobs, lean management, cooperation replacing cooperation, social networks displacing business processes, the democratization of work, and the new feudalism.
For all these reasons, the next generation of business €” business in the postnormal €” may best be characterized as emergent. It will reflect the volatility, complexity, uncertainty, ambiguity of our economics and society, and in a €˜like cures like’ fashion, business will be defined by properties that may have been impossible to expect based on just looking at the individual aspects of business organization, tools, or culture.