“Over the next several years, some very big forces will hit the retail industry.
From new payment types to variable store footprints, theyâ€™ll change how and where goods are stored and sold. Data analytics tools like predictive modelling, and â€˜always onâ€™ digital technology will make it possible for consumers to interact with retailers in new ways.”
Customers will expect rewards that better reflect their unique value.
As retailers learn more about customer behavior and preferences, theyâ€™ll move beyond discount or points clubs to benefits that recognize what a particular shopper values most,
Retailers will use technology to help store associates recognize and develop a relationship with each shopper.
Merchandising models will reflect a changing navigation process.
Todayâ€™s planograms are based on a variety of assumptions about how consumers walk through a physical store. But by using tools to understand what attracts shoppersâ€™ attentionâ€”where they move quickly and where they lingerâ€”retailers will be able to make dynamic decisions about promotions and traffic flow.
Customized couponing is changing attitudes toward dynamic pricing in the offline retail environment.
Physical stores recognize that they have become virtual showrooms for online sales channels, and they are developing more effective responses to keep the sale.
Tomorrowâ€™s customers will place even greater demands on inventory and customer service systems.
Many of todayâ€™s customers already expect retailers to provide consistency across touchpoints, with integrated promotions, return policies, etc.
Sophisticated displays allow customers to access the full range of sizes, colors, and features for any product, regardless of location.
Expectations are moving quickly, and retailers will be expected to lead, not follow.
Many brands are leading, facilitating and participating in online discussions. But the power of social networking goes far beyond sharing information. Increasingly, social media helps shape customersâ€™ decisions and even tastes.
Stores are rethinking their use of physical floor space to reflect a brand strategy.
Physical retailers will try a variety of approaches as they compete with online sellers
The role of a store associate is changing. This has significant implications for training techniques, competency models for hiring, and even compensation.
The checkout process is changing with the evolution of payment preferences and the rise of cash alternatives
Loss Prevention, Privacy and Cybersecurity
Vulnerabilities in any one channel represent a threat to all of them.
By creating ever larger and richer data stores of information about customer interests and behaviors, and providing real-time access to that information to store staff, retailers are also exposed to new security risks and privacy concerns.
“With technology investment spreading ever farther and wider in most organizations — to lines of business, the CMO, and the new Chief Digital Officer — the CIO still owns the sometimes mundane but ever-challenging job of creating a coherent and effective digital workplace in most organizations.”
The first is the need for a consistent and ubiquitous corporate information landscape that’s easy to access and easy to use, so that workers can find accurate, timely information, analyze it, and share the results.
The second is to move all existing applications and information onto many more digital channels and devices.
The modern CIO’s purview is typically bounded by three major constraints: The size of the business opportunity represented by a given digital change, the risk to the business if it’s not done properly (or the risk of not doing it at all), and the combined cost and time frame.
Co-design for a consistent digital user experience, inside and outside. Augment and update frequently.
Seek out major digital gaps in workplace processes and operations, end-to-end, and plug them as part of a more cohesive strategy that includes digital skill building.
Pro-actively provide data-driven stakeholder enablement through self-service, but communicate a consistent and clear digital workplace vision that everyone can support.
Open up data, IT systems, and digital engagement processes whenever possible.
Put together a joint business/IT roadmap for all of the above, and start enlist help across the organization
“There are as many definitions of the digital workplace as there are organizations. But in light of this month’s focus on the digital workplace, it could be useful to look at what a digital workplace is, and clarify some of the things that it is not.”
The digital workplace is meant to be a virtual equivalent to the physical workplace, which requires strong planning and management due to its fundamental role in peopleâ€™s productivity, engagement and working health.
A technology layer â€” advances in technology are driving changes in the digital workplace, and this is what makes it a current issue.
Management and design â€“ proactively developing a digital workplace means addressing it as a whole and co-ordinating between technology, process and people.
The digital workplace provides organizations five services or capabilities (the outer ring of the figure above):
Communication and employee engagement
Finding and sharing of information and knowledge
Business applications (process specific tools and employee self-service)
Agile working â€” the ability to be productive any time and place
The digital workplace encompasses all the technologies people use to get work done in todayâ€™s workplace â€¦ It ranges from your HR applications and core business applications to e-mail, instant messaging and enterprise social media tools and virtual meeting tools.
“If your company is like most, it tries to drive high performance by dangling money in front of employeesâ€™ 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.
Weâ€™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 itâ€™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 couldnâ€™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 reviewsâ€™ shortcomings, they often believe that the solution is simply to design better evaluation forms.
But the forms arenâ€™t the problem. What turns reviews into a blame game is the link to compensation. Sever that link, and youâ€™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 â€“ and in the process drove rapid economic progress. Those of the future, by substituting for manâ€™s senses and brain, will accelerate that process â€“ 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 â€œSecond Economyâ€ (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
And here is the even more sobering news: Arthur speculates that in a little more than ten years, 2025, this Second Economy may be as large as the original â€œfirstâ€ economy was in 1995 â€“ about $7.6 trillion
An emerging field in radiology is computer-aided diagnosis (CADx). And a recent study published by the Royal Society showed that computers performed more consistently in identifying radiolucency (the appearance of dark images) than radiologists almost by a factor of ten.
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, â€œreduce its generosity to people who are not working but increase its support for people who are,â€ consider moving to a progressive consumption tax, and â€œdoubling down on human capital, from early education programs to community colleges and beyond.â€
Ultimately, we need a new, individualized, cultural, approach to the meaning of work and the purpose of life.
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 â€˜people trackersâ€™, 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 â€œfan clubsâ€, is growing.
“Despite the buzz, and continuing innovations by technology that are making Talent Analytics a downright phenomenal tool, HR is a bit â€” behind.
On the one hand weâ€™ve got brand new streams of verifiable information not even possible a year or so ago. Weâ€™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 HRâ€™s new BFF. This potential cloud-sized trove of valuable information â€“ worked with the right algorithms and filters â€“ 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.Itâ€™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 organizationâ€™s needs,
5.Talent Analytics is mobile. Everythingâ€™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 â€˜digital breadcrumbs,â€™ so to speak, which offer clues about the learning process. Weâ€™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 whatâ€™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 othersâ€™ 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 learnersâ€™ 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 learnerâ€™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, â€œCorrelation does not prove causationâ€? If youâ€™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 â€˜rankâ€™ 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 learnerâ€™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 itâ€™s appropriate to share and use that data, simply because it can yield such powerful results.
“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 â€œtechnologyâ€ when you say â€œdigital workplaceâ€. My definition of the digital workplace is â€œthe intersection of People, Organization and Technologyâ€.”
Le Big Data aurait donc une vocation principale : celle dâ€™innover, questionner et sortir du cadre.
le Big Data questionne lâ€™analyse et lâ€™enseignement traditionnel qui nous empÃªche de voir les relations entre les parties (morcÃ¨lement des matiÃ¨res) : il faut sâ€™attendre Ã trouver ce quâ€™on ne cherchait pas.