Liens de la semaine (weekly)

  • « Back in the late nineteen-nineties, there was a lot of optimism about the future, and it wasn’t all emanating from those lucky souls who had gotten in early on the I.P.O.s of companies like Yahoo and Amazon. Many economists, with Alan Greenspan prominent amongst them, believed that over time the heavy investments in new information and communication technologies (I.C.T.) that companies were making would lead to rapid growth in productivity and wages. There was much discussion of a third industrial revolution, with the Internet playing the role that the steam engine played in the early nineteenth century and electricity played in the late nineteenth and early twentieth centuries. « 

    tags: internet productivity growth

    • Between 1996 and 2000, output per hour in the non-farm business sector€”the standard measure of labor productivity€”grew at an annual rate of 2.75 per cent, well above the 1.5 per cent rate that was seen between 1973 and 1996.
    • And with all the talk of “Web 2.0”€”2004 was the year that Tim O’Reilly, a notable Silicon Valley booster, held a conference devoted to that topic€”the technology optimists argued there was plenty of scope left for further gains.
    • Since the start of 2005, productivity growth has fallen all the way back to the levels seen before the Web was commercialized, and before smart phones were invented.
    • Now, productivity bounces around quite a lot from month to month, and one bad quarter doesn’t make a trend. Still, if the sluggish rates of productivity growth we’ve seen over the past two years were to persist into the indefinite future, it would take more than a hundred years for output-per-person and living standards to double.
    • Ten or fifteen years ago, there were hopes that new technology would lead to big productivity improvements in service industries such as health care, where doctors would be able to share patient information and carry out remote diagnoses, and banking, where people could manage their own payments online. But these hopes haven’t been fulfilled.
    • Invention since 2000 has centered on entertainment and communication devices that are smaller, smarter, and more capable, but do not fundamentally change labor productivity or the standard of living in the way that electric light, motor cars, or indoor plumbing changed it.
    • Another way that the official figures may simply be understating the economic impact of digital technology is in not giving enough emphasis to the provision of new goods, such as Web video conferencing, global-positioning-satellite phone apps, and mobile-phone banking, which simply didn’t exist twenty years ago
    • After running through a series of possibilities, such as measurement error (benign); cyclical factors (relatively benign); declines in the quality of education (not so benign); and the exhaustion of gains from information technology (definitely not benign); they throw their hands in the air and conclude: “depending on the answer, slow measured productivity growth may be consistent with continued rising living standards or a period of stagnation in the developed worl
  • « Disons que je mets cela sur le compte de la jeunesse€¦ Pour ma part, dans mon cours à  l’UdM, en point d’orgue à  mon cours sur la communication interactive en entreprise, je parle plutôt de dix compétences-clés d’ici 2020, en référence au travail fait par l’Institute for the Future du Research Institute de l’Université de Phoenix en Arizona. (Merci à  Patrice Leroux) Voici d’ailleurs comment ils présentent le graphique ci-dessous, assez connu, qu’ils ont publié sur ces compétences : »

    tags: digital competences humanresources

  • « Traditionally the value of knowledge to the organization has been explained by using the time-value of learning. That is, a new hire is worth X to an organization, evidenced by the salary they receive. Over time that salary grows and 10 years after starting, following promotions and raises, the individual increases their value to the organization to 3X, 4X or more (evidenced by their new salary).

    This increase in value has been explained as the value of the knowledge acquired by doing the job €“ which then translates into a more valuable worker for the organization. This increase in value, experts say, is the increase in value of the accumulated knowledge. This example has been documented extensively in books that talk about the value of knowledge for organizations.

    Well, I say, that is wrong and I can give you three reasons why that is no longer the case (although it might’ve been correct in the past). »

    tags: knowledge value customer

    • Reason 1 €“ it ignores the value to the customer

       

      As Wim Rampen put is so well a few weeks ago value can only be calculated in use €“ not as an accumulation.  Just because you do something many times does not mean it brings value; i

    • Indeed, stored knowledge these days is virtually worthless.  What worked yesterday will be unlikely to work tomorrow.  Processes, culture, technology, people, societies €“ they all are changing at a rapid pace;
    • Reason 2 €“ it only solves the equation of value for acquisition €“ not usage

       

      There are four stages to knowledge management (or the value chain of knowledge if you don’t like the word “management”):  acquisition, organization, distribution, and usage.

       

      When we look at the value of knowledge as what was acquired over time, we neglect the most important one: usage.  Knowledge these days is only valuable when it is being used €“ not stored. 

    • It is not about accumulating more knowledge (acquiring it over time), but about how to distribute it and use it.
    • Reason 3 €“ It does not accommodate extended ecosystems of knowledge

       

      This is the most important reason why the value of knowledge cannot be calculated from what a single individual (or even a group of them) accumulated over time: today knowledge is accessible in the most remote locations.  Note that I said accessible, not that it exists €“ knowledge always existed in remote locations, but until technical implementations of collective knowledge began to be used in the past dozen years or so, we could not access that existent knowledge.

    • f we value knowledge as what one individual can contribute, usually from the organization’s perspective, we miss out on the ability to measure the value of knowledge form all other contributors in this ecosystem. 
  • « Now as for the future of customer service (as evidenced by those wonderful data points that make patterns and trends happen), the future of customer service is evolving over time €“ there is no set model against which you have to build a similar solution. Not only that, but it changes from company to company, industry to industry, even for specific functions. This is what makes Customer Service interesting (and as the famous Chinese philosopher said €“ may you live in interesting times; to which I add €“ but not so interesting that they are absolutely crazy).

    Within this “craziness” I’m starting to see emerging models for the future of customer service. This is the timeline and projects that matter per my observations and conversations: »

    tags: customerservice

    • Communities and social €“ the questions I hear most are: what it is, how to use it, what to do to make it work, how to take it from reactive (last 2 years) to strategic, how to derive value from using it
    • Cross channel €“ questions that I hear the most: how to move from multi-channel to tracking inquiries across channels and time; how to identify what is one inquiry and the resolution for the same.
    • Fixing €“ the topics I discussed the most about this: budgets were cut, innovation did not happen, and social and collaboration / communities was thrust onto customer service to figure it out; how can all this be made to work while improving what we had?
    • Cloud €“ As controversial as the use of cloud for customer service is in certain environments, the conversations about how to ensure security and performance in the cloud, how the advent of cloud-based communications and leveraging new vendors and models to replace the hardware and technology that has been there forever (IVR, ACD, etc.).
    • Knowledge €“ once we figure out what we are supposed to do with social and communications, then we can figure out how to adapt to the new Knowledge Management paradigm (been writing about this for the last couple of months in the stone cobra blog). 
    • Automation €“ These are the questions you ask the most: how to take the value of automating partly online inquiries and move them to other areas; how to focus on reducing the number of inquiries to handle forty percent or more of the inquiries; how to provide automation via all channels from a central framework that allows to leverage automation across channels (while, of course, properly measuring interactions and solutions to justify the investment). 
  • « Processes have to be dynamic and ad-hoc to chase the changing business outcomes in today’s world. Like gymnast on floor exercise, she pursues excellence within the constraints of the floor mat and varies her paths and adjusts the routine as the music, pressure and mounting scores dictate. Processes have to do the same. Proceses have to be dynamic with explicit rules and dynamic orchestration. Proceses have to be ad-hoc with events, case mangement and social collaboration. Processes have to be intelligent with constraints, adaptive case management and goal directed. Processes of the future will contain various aspects of process adaptability and intelligence.

    Net; Net:

    Processes in the future will leverage rigid snippets (portions of fixed proceses) or processes will exhibt great adaptbility with the contraints of some rigid policies, but the days of pure rigid processes are numbered. « 

    tags: process businessprocess adhoc acm adaptivecasemanagement casemanagement goal

  • « L’une des difficultés récurrentes que rencontrent les organisations pour avancer sur le sujet du service, c’est qu’il est pour le moins diffus dans l’entreprise : la Direction Marketing, la DRH, la DSI ou encore la Direction des Opérations (ou de l’Exploitation) sont autant d’acteurs qui interviennent à  un moment donné dans la conception, le lancement, la production, le support€¦ d’une nouvelle offre.

    Il en va de même si l’on aborde le sujet du service par l’entrée de la « transformation culturelle » : différents acteurs sont légitimes pour prendre le leadership sur ce sujet et d’autres encore pour y apporter leur contribution€¦

    Ni tout à  fait « d’ici », ni vraiment « d’ailleurs », la question du service demeure un Objet Flottant Non Identifié, en ce sens qu’il n’est pas encore un sujet bien balisé dans les organisations. »

    tags: service culture management customerexperience governance

    • Voilà  donc notre problème N°1 : le service n’est pas le terrain « naturel » d’un acteur particulier, ce qui lui donne certes plus de chances de s’implanter mais ce qui rend aussi plus complexe son implantation durable, précisément parce qu’il n’est pas a priori clairement installé dans telle ou telle partie de l’organisation.
    • Le problème N°2, c’est que le service ce sont en fait €“ au moins €“ deux sujets qui se percutent souvent : celui de la culture de service, avec donc un prisme très RH (managérial et comportemental, mais aussi culturel au sens d’une transformation culturelle), et celui de l’offre de services, c’est-à -dire des prestations qui sont délivrées aux clients.
    • Un « Directeur du Service » pourrait ainsi être le garant d’un pilotage coordonné de l’offre et de la culture de service.
    • il s’agit de faire monter en reconnaissance et en exigence la question du service dans les entreprises. Organisée de cette manière, l’entreprise bénéficierait d’un vrai pilotage global de ces sujets et les dirigeants disposeraient d’une vision dynamique de leurs offres là  où, aujourd’hui, elles relèvent de pouvoirs éclatés dans l’organisation : la Direction Marketing initie des offres nouvelles et les « pousse », la DRH tente de suivre au niveau de l’évolution des compétences et de la conduite du changement, la Direction des Opérations fait ce qu’elle peut et la DSI€¦ a ses propres logiques !
    • i l’on est convaincu que le mal vient d’abord d’une inadéquation organisationnelle et culturelle des entreprises dont les schémas mentaux et organisationnels sont hérités du monde industriel, alors il faudra bien un jour envisager d’autres modalités organisationnelles.
    • Le mot de la fin : vers une Gouvernance du Service

       

      Ce redécoupage existe déjà  ici et là , pour partie, mais cette réflexion mériterait à  mon sens d’être véritablement poussée pour amener des idées nouvelles et suggérer ainsi des organisations mieux adaptées au contexte des services et aux enjeux forts de l’adaptation des modèles économiques aux transformations en cours (digitalisation des services, développement des logiques de peer-to-peer, développement des logiques d’usage, etc.).

    • Pour finir, il me semble essentiel d’aller sur le terrain de la mise en place de ce que nous nommons, avec Marc Prunier[3], une « gouvernance du service » : je veux dire par là  une instance (un « comité » et son processus de travail) qui a pour vocation de réaliser le travail de coordination dont nous avons parlé supra.
  • « Parler de data n’a de sens que dans le cadre de l’avènement du sujet dans la philosophie moderne Cartésienne, ce sujet qui reçoit les données du “monde extérieur”. C’est d’ailleurs en débat avec Descartes que Locke rejettera la notion “d’idée innée” en prônant un empirisme selon lequel nos idées ne sont pas déjà  là , a priori, mais proviennent des données immédiates de l’expérience.

    Le mot a ensuite pris un sens particulier au début du 20° siècle avec les débats des philosophes anglo-saxons autour des “sense data”. Les “sense data” sont ce qui nous est donné au travers de la perception (on retrouve les “données immédiates de l’expérience de Locke). Tout un débat a eu lieu pour savoir quelle interprétation donner à  ses “sense data” dans la mesure où l’on peut poser que notre rapport au monde et à  la réalité est toujours médiatisée par ce que nous en percevons aux travers de nos sens. »

    tags: data information calculation

    • Les data comme informations faisant l’objet d’un calcul

       

      En tant que “matériau brut” fourni par nos sens, les data ont ce caractère de matière première de la perception en attente d’être traitée par l’esprit. Se met ainsi en place l’idée que l’esprit procède par calcul (approche cognitive de l’esprit), une computation sur la base des data que nous percevons.

    • Les data comme information stockée

       

      Avec le développement de l’informatique de la seconde partie du XX° siècle, les data vont être associées, en plus du calcul, à  celui du stockage. La chose était relativement inédite car jusqu’alors les data étaient toujours se qui se donnait sans pouvoir faire l’objet d’une mise en réserve : la data était reçu puis traitée, mais elle était évanescente.

    • Les data comme objet de transfert

       

      Après avoir été associées au calcul et au stockage, les data vont être associées, à  la fin des années 90, au transfert ; renouant par certains aspects avec la définition latine originelle.

    • “A datum is an element of information that is transfered from a component, or received by a component, via a connector.” p. 11
    • Selon cette définition, si l’information ne fait pas l’objet d’un transfert entre différents composants d’une architecture logicielle, alors on ne peut pas parler de data.
    • Les data dans l’écologie relationnelle des metadata

       

      Il se trouve qu’une quatrième compréhension des data émerge au même moment que celle du transfert, il s’agit du discours sur les metadata en vertu duquel une data n’existe, et ne peut être utilisée, que pour autant qu’elle est qualifiée et catégorisée (elle a une signification explicite) par d’autres data, ces dernières étant explicitement qualifiées de metadata, c’est à  dire des data de data.

  • « Governance can be defined as the method by which you define, measure, and manage performance of a system to meet user and corporate expectations. The analyst firm Gartner Research says that most social collaboration initiatives fail “because they follow a worst practice approach of €˜provide and pray’, leading to a 10 percent success rate.” Almost every project methodology, whether some version of agile or the waterfall method, advises the need for adequate time up front for planning, as the cost of a change increases dramatically over time. Even the most agile management methods rely on strict planning and constant prioritization. »

    tags: governance collaboration socialcollaboration analytics governanceanalytics

    • Gartner’s research into the social collaboration efforts of more than 1,000 organizations has identified several prominent patterns. The most apparent was that social collaboration initiatives that have a clear and compelling purpose from the outset tend to succeed. While this may seem obvious, the vast majority of organizations treat collaboration as a platform decision, rather than a solution to a specific business problem or a route to a desired outcome.
    • As organizations experience growth, or seek to gain competitive advantage, they often look to technology to propel the business forward. However, what should be viewed as a business solution, owned and managed by business users for the purpose of meeting business requirements, are instead treated as IT initiatives,
    • Use cases and feature requests can be prioritized, identifying which will provide the most value to the business.
    • Healthy governance practices in one area of the business, which leads to the sharing of best practices across other segments of the business.
      • Meer poses three questions that governance analytics must be able to answer:

         

           

        1. What happened/what is happening within our system?
        2. Why did it happen/is it happening?
        3. What is going to happen next? (predictive analysis)
    • In every tool that you use internally, or that in some way touches your customer directly or indirectly, there is data that can be used to improve that customer experience, and the efficiency and effectiveness of your team
  • « The New York Times published an article “Big Data, Trying to Build Better Workers” last week that focused on what is described as a €˜new’ area of work-force science. It’s a nice to say so, but the reality is that this is fresh lipstick on the continuing drive to understand workplace attitudes, psychology and sociology. Calling it new is a nice way to draw attention to a key way of improving our organizations, but it doesn’t need to be. The difference now is that better data analysis tools, integrated with employee process execution data, is associated with the currently vogue Big Data analytics. »

    tags: humanresources socialanalytics analytics behaviors

    • The innovation here is about bringing it all together, this large volume, multivariate data that can be associated to particular psychographic profiles, or even individuals. It is the integration of the data first, and then new models for finding the needle in the haystack of data.
    • It comes back with showing particular correlations to process improvement gains, and that in the social science sphere is where it may get closest to evidence. You have to look deeper into these systems. Correlation is not causality as always, but it is good evidence that it can happen.
    • . ERP software and database companies like IBM, SAP and Oracle are key to delivering these capabilities. The business problem comes in the form of what questions, factors and characteristics to look for, and how to ask these questions. This is where the specific domain knowledge of Kenexa (an IBM company), SuccessFactors (an SAP company), Deloitte, or Vovici, or the many independent HR consultants focused on employee performance analysis. The magic happens when the two knowledge domains come together.
    • We are no longer talking about large groups of people that fit into known categories, but micro-segmentation into smaller and smaller groups of people that are dynamically identified from a mixture of attitude and behavior.

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