« If your company is like most, it tries to drive high performance by dangling money in front of employees’ noses. To implement this concept, you sit down with your direct reports every once in a while, assess them on their performance, and give them ratings, which help determine their bonuses or raises. »
- Performance reviews that are tied to compensation create a blame-oriented culture.
- In 2010, we replaced annual performance reviews with quarterly sessions in which employees talk to their supervisors about their past and future work, with a focus on gaining new skills and mitigating weaknesses.
- Employees might have been skeptical at first, so to drive the point home, we dropped annual individual raises. Instead we adjust pay only according to changing local markets.
- We believe that traditional performance reviews do little to motivate people. The way to drive high performance is through honest feedback that employees and managers really hear.
- We’ve found that our new system greatly improves the feedback process. Supervisors and employees say the sessions are less stressful and more productive than the old performance reviews.
- Although it’s too soon to see any impact on the income statement, there has been a noticeable increase in collegiality.
- A recent survey found that only 1% of American companies have rejected traditional reviews, and most of those seem to be start-ups or nonprofits. We couldn’t find a single other big company that had done it.
- The reason companies hang on to this tradition, of course, is their anxiety about high performance.
- Even if executives acknowledge performance reviews’ shortcomings, they often believe that the solution is simply to design better evaluation forms.
- But the forms aren’t the problem. What turns reviews into a blame game is the link to compensation. Sever that link, and you’re on the way to creating a review system that can open up the channels for real feedback throughout the organization.
« The technologies of the past, by replacing human muscle, increased the value of human effort – and in the process drove rapid economic progress. Those of the future, by substituting for man’s senses and brain, will accelerate that process – but at the risk of creating millions of citizens who are simply unable to contribute economically, and with greater damage to an already declining middle class. »
- Baxter, a $22,000 robot that just got a software upgrade, is being produced in quantities of 500 per year. A few years from now, a much smarter Baxter produced in quantities of 10,000 might cost less than $5,000. At that price, even the lowest-paid workers in the least developed countries might not be able to compete.
- The “Second Economy” (the term used by economist Brian Arthur to describe the portion of the economy where computers transact business only with other computers) is upon us
- The simplistic policy answer is better training. But at this pace of change, improving the educational system will be perpetually too little and too late.
- David Brooks has suggested that the government should aggressively build infrastructure, “reduce its generosity to people who are not working but increase its support for people who are,” consider moving to a progressive consumption tax, and “doubling down on human capital, from early education programs to community colleges and beyond.”
- Ultimately, we need a new, individualized, cultural, approach to the meaning of work and the purpose of life.
« Les formations proposées aujourd’hui en France sont pensées pour le marché du travail des années 1990 par une génération dépassée par la révolution technologie en marche. Tel est le constat sévère du chirurgien Laurent Alexandre, cofondateur de Doctissimo. Ce diplômé de Sciences Po, d’HEC et de l’ENA intervient le 14 octobre lors d’un événement sur « Le recrutement en 2025 » organisé par Link Humans. «
- Les technologies NBIC (nanotechnologies, biotechnologies, informatique et sciences cognitives) serviront à mieux recruter.
- on peut même imaginer que la séquence ADN sera utilisée dans certains pays pour déterminer si le profil du candidat correspond à celui recherché.
- Aucun cerveau biologique n’arrive déjà plus à la cheville de l’IA. Watson, un système expert d’IA, s’est vu soumettre un problème hypercompliqué sur une mutation particulière. Il a lu en une seconde ce qu’un cerveau humain aurait mis trente-huit ans à lire pour poser un diagnostic.
- Il faut recruter des chauffeurs qui aient l’intelligence et la plasticité cérébrale nécessaire pour se reconvertir
- Dans vingt ans, la moitié des métiers auront disparu.
- Aujourd’hui, la formation initiale est inadaptée. On continue à former des gens sans intégrer les progrès technologiques…
- En période de révolution technologique, les vieux, qui sont d’habitude ceux qui ont l’expérience et la sagesse, deviennent des vieux cons.
- Le système universitaire américain intègre mieux la prospective et la formation est davantage portée vers le futur. Ce n’est pas un hasard si Google ou Amazon sont nés là-bas.
- Aujourd’hui, il faut miser sur une grande culture générale et une grande culture éthique
- Seuls les plus intelligents, créatifs et adaptables s’en sortiront.
- Il vaut mieux un bon Mooc qu’une formation académique dépassée
- Qu’est-ce qu’on fait des gens peu doués dans monde de robotique et d’IA ? » : c’est LA question du XXIe siècle.
« The HR Trend Institute distinguishes eight trends influencing the domain of organisations and people in organisations.
The table below gives an overview of the eight trend areas. »
- No more performance reviews
Finally organisations are sensing that they have wasted an enormous amount of time, money and effort on a process that will never work properly
- The org chart is fading away
This is partly wishful thinking from my side, but there are weak signals that the org chart is fading away
- Privacy seems to be less of an issue
New generations of ‘people trackers’, far beyond time tracking, are emerging.
- The sharing economy is also entering organisational life
Sharing cars, sharing houses and sharing garden equipment is getting more usual. The possibilities for organisations are big, and this will take off in 2015. Who needs 100% of the office space 24/7?
- Mobile/ Mobile/ Mobile
Also in the HR domain mobile solutions will become more and more the standard. The smartphone is essential equipment for almost all employees. Today it is all about apps, the future will probably offer a more integrated user experience.
- Real time succession management
Technology and the smart use of HR analytics enable a far more effective succession management
- Robots in the boardroom
Robots are not just for manufacturing. The first robots have entered the boardroom, and this trend will continue.
- The end of Powerpoint
Who likes if when a presenter enters the room and it turns out she or he is going to present a large number of slides to illustrate the presentation? Hardly anybody likes this, only the people who have more work to do and who can process some e-mails while the presentation is dragging on.
- Community management as a recruitment tool
Recruitment has to make the shift from reactive to proactive. The practice to create communities around your organisation, a kind of “fan clubs”, is growing.
« Despite the buzz, and continuing innovations by technology that are making Talent Analytics a downright phenomenal tool, HR is a bit — behind.
On the one hand we’ve got brand new streams of verifiable information not even possible a year or so ago. We’ve got the ability to mine real data on potential hires and workforce strategy, adding a hefty dose of science to the art of recruiting and managing talent. It was a full year ago that I wrote about why Big Data is HR’s new BFF. This potential cloud-sized trove of valuable information – worked with the right algorithms and filters – can be turned into actionable insight. «
- 1. Talent Analytics has the capacity to be a powerful descriptive tool, looking at past performance and information to enable strategic change
2. It’s also an incredible predictive tool. By analyzing the skills and attributes of high performers in the present, organizations can build a template for future hires.
- 3. By its nature, Talent Analytics is democratic: merit may well trump a fancy education, skills may supersede proximity, and remember those apparently intangible aspects, like social skills, flexibility, emotional intelligence, initiative and attitude? They are now measurable.
- 4. Talent Analytics is evolving rapidly, as technology has created more fluid, flexible, powerful tools. Advanced software algorithms, for instance, can identify talent and match it to an organization’s needs,
- 5. Talent Analytics is mobile. Everything’s mobile. Your talent acquisition strategies had better be, too. New mobile apps make talent searches a matter of anytime and anywhere,
« When learners interact with content in your course, they leave behind ‘digital breadcrumbs,’ so to speak, which offer clues about the learning process. We’re now able to collect and track this data through learning management systems (LMSs), social networks, and other media that measure how students interpret, consider, and arrive at conclusions about course material. »
- We have to become more willing to share what’s working and not working. In return, all organisations that are trying to tackle big intractable problems in education should be more generous with each others’ ideas and evidence
- . Feedback: Big learning data can be informative from a feedback and context perspective.
- 2. Motivation: If you implemented big data in a comprehensive way, learners potentially become invested in inputting data to the process because they see the impact of how it work
- 3. Personalization: Big Data will change the way we approach e-learning design by enabling developers to personalize courses to fit their learners’ individual needs.
- 4. Efficiency: Big Data can save us hours upon hours of time and effort when it comes to realizing our goals and the strategies we need to achieve them.
- 5. Collaboration: More often than not, specialists from multiple departments must come together to keep a Learning Management System functioning at its best.
- 6. Tracking: Big Data can help us understand the real patterns of our learners more effectively by allowing us to track a learner’s experience in an e-learning course
7. Understanding the learning process: By tracking Big Data in e-learning, we can see which parts of an assignment or exam were too easy and which parts were so difficult that the student got stuck
- 1. Privacy: As companies like Google have extended the services they offer to include email, document storage and processing, news, Web browsing, scheduling, maps, location tracking, video and photo sharing, voice mail, shopping, social networking and whatever else might be of interest to their users, they gain access to even more personal data, which they collect, store, and cross-reference.
- 2. Dehumanization: Apart from the obvious potential for error and prejudice, this use of profiling is objectionable because it dehumanizes those being judged, as well as those making the judgments.
- 4. Correlation vs. Causation: Have you ever heard the phrase, “Correlation does not prove causation”? If you’re a good scientist, all of your efforts will be based in recognizing the difference between these two terms.
- 5. Claims Beyond the Data: Take university rankings, for example. University rankings are used by politicians, universities, parents, and students alike. But oftentimes, where they claim to ‘rank’ universities, they tell you very little about about teaching
- 1. Transparency. Learners have the right to know how learning data will be used, shared, stored, or leveraged.
- 2. Privacy. Who gets to see the aggregated data of 1,000 learners? Who gets to see a single learner’s data?
- 3. Value to the learner. Big learning data can provide great value back to the learner. What have other learners who have taken the same program found most difficult?
4. Depth of measurement. We have looked at whether learners passed an exam, but more valuable data might include the answer, as well as characteristics of how learners answer the question.
- 7. Expense. Some data that we will use in big learning data will be more expensive to get than what we have traditionally used. But what we easily collect tends to be superficial or inaccurate.
- 8. Many factors influence learning. We need to have an anthropological view of the learning process to understand that there are many factors that may influence learning.
- 9. Presenting data. We need to adopt a strategic approach to presenting data. How do we display data so that it brings meaning to people?
- 10. Readiness. This refers to the extent to which individuals making decisions are ready to operate with a massively enhanced set of data.
- 12. Infrastructure. Institutions will need to upgrade, alter, or change learning systems to prepare for big data use.
- 13. Openness. We need to understand where, how, and in what way it’s appropriate to share and use that data, simply because it can yield such powerful results.
« Une société de services française a décidé d’instaurer un «management cellulaire». Une pratique qui éradique de nombreux problèmes et conflits en interne, sans entraver la croissance l’entreprise. »
- Il faut se forcer à ne pas embaucher un profil type pour un projet, sans trop se poser de questions, comme on a tendance vouloir le faire lorsqu’il y a un besoin inopiné»
- Il faut d’abord mettre les personnes dans le bus avant de choisir sa destination
- Chez Sogilis, avec ce système de management cellulaire, chaque collaborateur est sur un même pied d’égalité
- Pour chaque cellule, il y a un «référent» choisi par les membres de la cellule, qui est chargé d’être à l’écoute, à tout momen
- Tous les employés jouissent du même confort, de la même liberté: aucune contrainte horaire, aucune obligation… À partir du moment où le travail est bien fait
- Si les carriéristes et individualistes ne sont pas les bienvenus, il est tout à fait possible d’avoir le profil ‘geek entrepreneur’
- Pour le moment, cette approche du management n’a pas trouvé sa limite. «Chez WL-gore (fabricant du tissu Gore-tex), aux États-Unis, ils ont mis en place exactement ce même système, et l’entreprise compte aujourd’hui plus de 8000 salariés.» Preuve que cette pratique managériale ne se limite pas aux petites structures.
« Digital is a new way of working. It simplifies. It accelerates. It clarifies. It humanizes. Technology is only a small part of the digital way of working. Most people misunderstand this. They think “technology” when you say “digital workplace”. My definition of the digital workplace is “the intersection of People, Organization and Technology”. »
« L’approche philosophique développée par Luc de Brabandère lors de la 7ème édition de l’USI, résume à elle seul la pensée de la plupart des intervenants : le Big Data aidera parfois à découvrir, mais très peu à inventer. Face à l’incertitude de notre monde, à la remise en question des règles établies, il faut se préparer et le Big Data peut y aider. »
- Le Big Data aurait donc une vocation principale : celle d’innover, questionner et sortir du cadre.
- le Big Data questionne l’analyse et l’enseignement traditionnel qui nous empêche de voir les relations entre les parties (morcèlement des matières) : il faut s’attendre à trouver ce qu’on ne cherchait pas.
- La complexité, c’est simplement que les choses ne sont pas isolées, séparées
- Le Big data, plus qu’une innovation technologique, interroge donc l’humanité sur la question du paradigme. Il aide à prendre conscience de l’émergence trans-humaniste. Dit autrement, nous existons autant sur la toile que derrière nos écrans.
- «En introduisant un principe d’innovation comme on a déjà un principe de précaution dans notre constitution