Links for this week (weekly)

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  • ““There are two dominant narratives about the institutional changes we are experiencing and neither one of them gives anyone much respite: the first is that companies will fragment to smaller and smaller entities and even down to individual providers; the second is a winner take all world where only the largest survive. We believe that both of these narratives are too simplistic. We see a world where both of these narratives co-exist and are mutually reinforcing rather than conflicting. Scale and fragmentation interact in a symbiotic relationship where the growth of each is what drives the growth of the other.” “

    tags: digaleconomy structure organization costs transactioncosts complexity learning scalablelearnaning

    • In 1937, professor Coase published a seminal paper, The Nature of the Firm, in which he explained that, in principle, a firm should be able to find the cheapest, most productive goods and services by contracting them out in an efficient, open marketplace
    • The article concluded that “by reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets – rather than hierarchies – to coordinate economic activity. . . we should not expect the electronically interconnected world of tomorrow to be simply a faster and more efficient version of the world we know today.  Instead, we should expect fundamental changes in how firms and markets organize the flow of goods and services in our economy.”
    • His remarks focused on three key drivers of change: cheap transportation, cheap communications and cheap automation.
    • The  Internet has radically lowered the transactions costs of obtaining  goods and services outside the firm.
    • hey should essentially become an aggregation of specialized entities with complementary interests – expanding, contracting and reconfiguring themselves in a way that best adapts to or even anticipates market dynamics.
    • But, as firms are increasingly relying on business partners for many of the functions once done in-house, one of their major organizational challenges is how to best manage their distributed operations across a network of interconnected companies. 
    • Managing such complexities within the organization and across its ecosystem of partners should be one of the top priorities of firms.  It requires not just good IT systems and well defined, data-driven processes, but also good human communications and coordination.
    • Economic history to date is primarily a story of scalable efficiency
    • Unfortunately, these institutional architectures have a downside: The consistency and predictability they create to promote efficiency also limit an organization’s ability to try new things or change
    • Institutional innovation requires embracing a new rationale of scalable learning with the goal of creating smarter institutions that can thrive in a world of exponential change
  • “The need for high-performing employees, and for hiring the right candidate for the right job at the right time, has never been greater. Here’s how the talent hunt is being transformed.”

    tags: bigdata talentmanagement recruitment predictions predictiveanalytics

    • A third facet that is influencing the way we work is the rise of big data analytics. Humans have been creating and storing data at an exponential rate for thousands of years. What’s different now is the paradigm shift both in the way data is stored and how it’s being viewed and utilized
    • If you apply for a job today, you can be sure your prospective employer is going to be checking out your personal brand across all the social networks you are part of to see if you are a good candidate to hire
      • There are four basic reasons we use data:

          

           

        1. To make decisions.
        2.  

        3. To try and predict the future and act on it.
        4.  

        5. To benchmark ourselves against others.
        6.  

        7. To create language around which we can tell stories or communicate with one another (for example, how do I read my engagement data to show the impact of leadership in my organization?).
    • What does this mean for individuals? Essentially, it gives them the ability to choose where and how they want to work, to obtain better insights on mapping a career path for themselves, and to draw on the collective knowledge and experiences of the organization in order to adopt best practices, solve problems innovatively, and be far more productive at work, confident that their talents are being maximized.
    • On an enterprise level, the combination of analytics and human behavioral insights gives companies better sourcing capabilities and better predictors of where their next level of talent is going to come from, with the ability to predict the success of potential candidates before they even walk through the door.
    • An interesting advantage of big data is its use to dissipate workplace myths
    • Actually, we’ve discovered that engagement levels rise when employees are mission-driven, often by a big project or challenge
    • by studying more than 1,000 salespeople in several companies across diverse industry verticals, is that success at work is more likely to occur when salespeople have emotional courage and persistence.
    • A recent study by IBM revealed that 70%of CEOs claim human capital is the single biggest contributor to sustained economic value. At the same time, paradoxically, unemployment levels are high but 65% of global companies are having trouble finding candidates with the skills their workforces require.
    • Thanks to data analytics, organizations are in a better position to study potential candidates and pinpoint with amazing accuracy those who have the capability to do the job, the capacity to learn new skills that may be needed in the future, and who are a good match with the culture of the company.
  • “Big data has a value, for sure, which we often measure proportionately to its magnitudes: volume, velocity and variety. But big data also has a “disvalue” in roughly the same proportion: the more rapidly we collect more data of different types, the more likely we are to be intensifying business, legal and compliance risks associated with our stewardship of that data.

    To the extent that we often don’t know exactly what it contains, big data carries its own special risks, as discussed in this recent article. Though the article focuses only on unstructured data, you can generalize its observations of more complex data sets that include relational and other data types.”

    tags: bigdata risk legal

    • Where risk mitigation is concerned, you don’t have perfect knowledge of what specifically from your big-data collection might potentially be subpoenaed in future litigation.
    • The chief risk factor surrounding big data is not knowing the potential future downsides associated with your failure to manage it all effectively.
    • My favorite discussion in the article is the “damned if you do, damned if you don’t” risks surrounding data stewardship. Undiscriminating retention carries the risks associated with what the data says, while mass deletion might put you in serious legal, regulatory, or business peril.
  • “Capitalizing on insights is one of five key responsibilities that marketers must embrace to transform the marketing function into a strategic business driver. Here are four key foundational elements you will need to put in place to capture – and act on – insights in real time:”

    tags: marketing insights analytics

    • Plan for spontaneity. The
    • Shortly after the power went out at the Superdome, analysts noticed the chatter on Twitter and Facebook had shifted to the blackout. Within minutes, Oreo’s digital agency had created a snappy post, which generated 15,000 retweets on Twitter and 5,500 shares on Faceboo
    • Accelerate your analytics. Real-time marketing requires real-time decision making, not just big data. Businesses are looking for ways to reduce the time between collecting the data and acting upon it. Procter & Gamble, for example, has invested in a “visually immersive” data environment, called Business Sphere, which delivers constant streams of business intelligence to employees around the globe.
    • Curate content, not just collateral. Research from technology publisher IDG found that IT professionals typically consume five pieces of content, created by or on behalf of the vendor, before they speak with a sales representative.
    • Know your customer before they are your customer. Customers have more choice than ever before, are better informed than ever before, and their opinions count more than ever before. Customers spend 50% of their time researching online and 70% of their decision making is complete before they speak to a sales person. If you are waiting for them to walk in your store or meet with a sales rep, you are too late.

  • “A new buzzword has hit the business world: Datafication – turning an existing business into a “data business”

    tags: humanresources data bigdata datification

    • Every business process is now “datafied” – we monitor it, store its history, and start to identify patterns of usage and therefore how we can improve or monetize it better.
    • Our research on Integrated Talent Analytics shows a small percentage of companies (less than 8%) are starting to “datafy” some very interesting things. Let me cite a few examples.
    • The impact of this finding? Managers can now shift compensation dollars from mid-performers to high-performers and dramatically improve retention without major changes in payroll expense.
    • After looking at a wide variety of HR and product data elements, they found a set of sales people who were more seasoned, better trained, and simply more business-aware of the products they were selling
    • They also found that candidates with many prior positions (ie. job hoppers) did not perform any better or worse than employees who had long term employment with their prior employer
    • HR and training managers have been trying to implement HR analytics and measurement systems for many years. Today if we just apply the buzzword of “datafication” to HR, we can see huge benefits in our own organization.
  • “Measuring ROI is an elusive task: you have external factors that affect the results; measuring the baseline is a subjective process; forecasting future impact is hard and even calculating the actual investment is a project by itself.

    That said, one can measure almost anything (I recommend reading the book How to Measure Anything). There are testimonies of increased sales results after implementing gamification systems by 5-15%. But, I believe we need to dive in to understand how to analyze success.”

    tags: gamification ROI

    • A. Measuring the effect of Gamification on our target users’ behavior:
       Gamification design should modify the behavior of our users directly (call to action; goal setting; increasing the efforts) or indirectly (improve motivation; team spirit; reduce churn or turnover
    • B. Measuring the effect of the modified behavior on the business results
       We can succeed in designing a great engaging gamification process, but if it is not aligned properly with the business goals, it will have no meaningful effect. For example, if we reward service agents to shorter service calls and won’t balance it with quality of service, we will promote customer churn.
    • 1. Define the Success KPIs
    • 2. Compare the KPIs Before and After Implementing Gamification
    • 3. Compare the KPIs in correlation with Gamification Activity Performance
    • 4. Measure the Balance of Different Aspects of Gamification in Different Groups
    • 5. Use Control Groups
    • 6. Run a simulation before you start the implementation
  • “Je suis particulièrement étonné par le discours actuel sur les big data ; discours selon lequel nous serions passé de la causalité à la corrélation. Je pense surtout à la thèse de Viktor Mayer-Schönberger et Kenneth Cukier, dans leur livre Big Data : une révolution qui va transformer notre façon de vivre, de travailler et penser. “

    tags: bigdata causality correlation probability

    • Faut-il rappeler que la corrélation (originairement appelée “co-existence”) n’est pas l’opposée de la causalité ? Que le coefficient de corrélation permet précisément de mesurer la force de la liaison entre 0 (indépendance) et 1 (dépendance stricte). Dans le cas où le coefficient est 1 : on a la pleine causalité directe
    • Ce qu’il faut plutôt souligner, avec les Big Data, c’est l’importance du schème probabiliste bayésien au détriment du schème fréquentiste
    • les algorithmes de corrélation dont nous parlons reposent sur les probabilités bayésiennes qui ne travaillent pas à partir de données exhaustives et dont la fréquence est objective mais à partir d’échantillons et en avançant à tâtons dans une économie entre probabilités a priori et a posteriori
    • Les big data ne sont pas des corpus immenses et stables mais des flux sans cesse mis à jours et évolutifs : d’où l’utilisation des probabilités bayésiennes pour éprouver des conjectures.
    • Il aurait été bien plus intéressant de préciser que l’utilisation du schème probabiliste bayésien suppose une conception “à rebours” de la catégorie de causalité, en allant non plus des causes aux effets mais des effets pour remonter aux causes
    • il est plus que jamais nécessaire de dépasser la stricte catégorie de Causalité : soit à partir d’une causalité à rebours (inférence bayésienne), soit à partir des méthodes corrélatives.
  • In January, San Jose State University (SJSU) made headlines when it announced that it would let students take credit-bearing online courses through the MOOC-provider Udacity. The courses were remedial in nature, focusing on topics like basic math, elementary statistics, college algebra, introductory computer programming and psychology. And the hope was that thousands of students could eventually take these courses at reduced rates — $150 per online course versus $620 for a traditional course. It sounded like an easy way to slow down the ever-escalating costs of secondary education”

    tags: casestudies mooc sanjosestateuniversity undergraduate

    • It turns out that San Jose State students were failing Udacity courses at a rate of 56 to 76 percent, according to the San Jose Mercury News.
    • inally, we also discovered that, outside of SJSU, students were completing MOOCs at a rate of only 7.5% on average
    • “The plan right now is to pause for one semester, there are a couple of different areas we need to work on
    • If you’re a lifelong learner, you probably don’t have much to lose or complain about. You might already be well educated, and you might enjoy having a big list of free MOOCs to choose from.
    • In MOOCs, you’re not a student, you’re a number — you’re one of 50,000 in a course, or you may be one of the 76% at SJSU that failed
    • At this point, educators, politicians and journalists would be advised to take a more measured approach to MOOCs. They should adopt a position of healthy skepticism, ask more intelligent questions about what MOOCs can offer undergrads, and see real results before deciding that MOOCs are the easy solution to a complicated problem.

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