Links for this week (weekly)

  • “Social ROI should show quantitative metrics that show progress in moving people through a relationship pipeline. However that is defined.”

    tags: socialmedia ROI casestudies KLM

    • The mantra for KLM on social is “we are a guest at someone else’s party” so act accordingly.
    • It’s another channel with loads of data, just like the web was another channel 15 years ago.
  • “Truth is, I shouldn’t. Knowledge, skills, and abilities (aka KSAs) are three different things. And it’s important to know the difference – even though the difference can be subtle.”

    tags: knowledge skills abilities humanresources

    • Knowledge is the theoretical or practical understanding of a subject.
    • Skills are the proficiencies developed through training or experience.
    • Abilities are the qualities of being able to do something.
    • For instance, if the issue is knowledge, then maybe we can create an in-house library that employees can check out books on the topics. But if the challenge is skills, the answer might be training. And if abilities need to be improved, is it possible to develop personal action plans that give employees the opportunity to refine their abilities.
  • ” Dans une base de données d’un opérateur national comprenant quelques 1,5 millions d’abonnés, il suffit de 4 points pour identifier 95% des gens. “Nos données de déplacements sont encore plus personnelles que nos empreintes digitales.””

    tags: bigdata data identification privacy identity predictiveanalytics predictions

    • Cela signifie qu’à partir d’un profil d’usage de votre téléphone, pris comme une simple ligne de chiffres dans une énorme base de données où chacun paraît protégé par la masse, on peut en déduire vos caractéristiques psychologiques… c’est-à-dire des choses qui n’ont rien à voir avec l’usage de votre mobile a priori.
    • l est difficile d’anonymiser les données transactionnelles. Qu’enlever les numéros de téléphone ou les noms des abonnés ne suffit pas à rendre ce type de base anonyme.
    • , le MIT imagine un service de requête permettant de protéger l’anonymat des données, tout en permettant de les utiliser.
  • “Companies positioned to win the User Experience Economy track five key metrics for their mobile apps:”

    tags: userexperience experienceeconomy mobile mobility mobileeconomy

      • Adoption: As measured by the number of installs of the app – that is, the number of downloads from either a public or enterprise app store. But an install by itself does not equal an engaged user. This requires…
    • Engagement: User engagement is measured by average session length. Session length refers to the amount of time a user spends in the app each time it’s opened.
    • Retention: The number of active users divided by the total number of installs.[2]  Retention carries an important time dimension, usually comparing changes week over week or month over month to establish trends.
    • Conversion: This measures how many users who begin business processes enabled by the app actually get through to completion. Take a basic order-to-cash example: 1) user logs in 2) selects merchandise 3) chooses payment option 4) confirms and submits order. Measuring conversion tells you how many people who opened the app actually made it through all four steps
    • Exceptions: The ratio of app crashes to app sessions. In the User Experience Economy, any ratio over 1:20 spells trouble – users will lose patience and delete the app.
  • ““[T]he ways we connect and share in our personal lives have not carried over to how we connect, innovate, and learn from each other professionally. This is in spite of the fact that the Internet and Web 2.0 tools are driving a convergence of the personal and the professional. More and more people are using technology to work anytime, anywhere; the traditional boundary between work and life is rapidly dissolving. One might expect social media and other knowledge-sharing tools to be as widely used within as well as outside the workplace. But rather than increasing, the integration of such tools into the work environment is actually declining. “

    tags: socialmedia adoption change control

    • A few leading edge companies are able to keep up, but the vast majority of more traditional firms are lagging behind.  These companies are working harder than ever, trying to achieve greater efficiencies and predictability.  They keep trying to fit new technologies and practices into old business models. 
    • “the historical value accorded to efficiency and controllability by businesses accustomed to a less changeable, less transparent world”
    • Simply put, there is a growing mismatch between the old frameworks and practices that many companies use and the structures and capabilities required to be successful in a rapidly changing environment.  Legacy corporate practices are holding businesses back from fully participating in new opportunities.  . . .As long as our institutions continue to resist the Big Shift, the journey ahead will remain stressful and pressure-packed. As workers and as leaders, our lives will not get easier unless we decide to shape, rather than simply adapt to, the future.
  • “Here’s a simple rule for the second machine age we’re in now: as the amount of data goes up, the importance of human judgment should go down.”

    tags: bigdata decision judgement

    • As I’ve written here before, human intuition is real, but it’s also really faulty
    • Purchasing professionals do worse than a straightforward algorithm predicting which suppliers will perform well.
    • What you usually see is [that] the judgment of the aided experts is somewhere in between the model and the unaided expert. So the experts get better if you give them the model. But still the model by itself performs better.”
    • Instead of having the statistics as a servant to expert choice, the expert becomes a servant of the statistical machine.”
  • “Pour tenter de se figurer les conséquences d’une telle densification des échanges possibles entre individus différents, les spécialistes commencent à évoquer le concept de “swarm intelligence”. Cela correspond en français à la notion d’intelligence “distribuée” ou “en essaim” utilisée pour caractériser l’évolution des bancs de poissons, des colonies de fourmis, ou encore des groupes de chauves-souris qui exécutent dans les airs un ballet coordonné pour échapper à leurs prédateurs.”

    tags: socialnetworks smarmintelligence revolutions degreesofseparation

    • “La société fonctionne toujours plus en réseau, et les médias sociaux lui fournissent l’outil qui facilite cette distribution plus large et multidirectionnelle des liens entre individus. Mais les organisations non, juge Agusto de Franco, les entreprises commes les gouvernements restent organisés pour un monde plus hiérarchisé que distribué”
    • Le risque que courent les structures encore très pyramidales du pouvoir est, selon lui, “de persister dans des systèmes trop fermés, qui n’échangent pas suffisamment d’ énergie et de matière avec l’extérieur”

Posted from Diigo. The rest of my favorite links are here.

Head of People and Business Delivery @Emakina / Former consulting director / Crossroads of people, business and technology / Speaker / Compulsive traveler

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