“n reality, the technology of social business isnâ€™t much of an obstacle, at least once you get beyond the internecine platform battles that are common in many large organizations. No, the problem is a human one, which is ironic because social is all about people. Yet businesses are also a kind of organism in their own way. And like a living organism, they have a defense mechanism in place that acts like an immune system to anything that would disrupt the status quo. “
“A number of middle managers jobs will disappear, a number of intermediariesâ€™ jobs will disappear, and a number of paid jobs will disappear because of the collaborative dimension of our society and of corporations.
Similarly to, and in addition to, the â€œSecond Economyâ€ the economics of collaboration will add pressure on the job market.
Part of Middle Managers jobs will disappear. The part of the jobs of middle managers which consisted in helping individuals and teams to connect with each other or with knowledge or tools by being a transmission relay â€“ up and down the hierarchy, across silos, or even with outside systems and organizations â€“ will just disappear thanks to the advent of collaborative culture, intelligent directories, social networks
Some paid jobs will be replaced by free amateur jobs or low paid jobs. Some jobs that used to be paid, because the value-add was relatively hard to access, start to be replaced by Internet systems bringing â€œapparentlyâ€ similar value.
In short the Second Economy approach says that the overall productivity gains, thanks to the enormous â€œcombined productivityâ€ (labor plus capital productivity) and to the integration of several digital technologies have reduced the number of jobs required for doing a number of tasks.
In reality, collaboration is a great opportunity for the society to eliminate the costs of many market imperfections an to focus on increased quality, for the corporations to have their employees work on really productive and high quality tasks, and for middle managers to bring real value to the work of their teams and to the individuals composing them.
on a more philosophical level, these two new sides of the economy could help us to realize thattechnology is here to help us work less and enjoy life, creativity and our families more. Obviously, from a political perspective it questions the way work and incomes are shared but it is an other debate.
This radical change has created a dilemma for senior executives: while the potential of social media seems immense, the inherent risks create uncertainty and unease. By nature unbridled, these new communications media can let internal and privileged information suddenly go public virally. Whatâ€™s more, thereâ€™s a mismatch between the logic of participatory media and the still-reigning 20th-century model of management and organizations, with its emphasis on linear processes and control.
1. The leader as producer: Creating compelling content
2. The leader as distributor: Leveraging dissemination dynamics
3. The leader as recipient: Managing communication overflow
4. The leader as adviser and orchestrator: Driving strategic social-media utilization
5. The leader as architect: Creating an enabling organizational infrastructure
6. The leader as analyst: Staying ahead of the curve
“Exactly. Enterprise politics and culture suggest analytics’ impact is less about measuring existing performance than creating new accountability. Managements may want to dramatically improve productivity but they’re decidedly mixed about comparably increasing their accountability. Accountability is often the unhappy byproduct rather than desirable outcome of innovative analytics. Greater accountability makes people nervous.”
At one global technology services firm, salespeople grew furious with a CRM system whose new analytics effectively held them accountable for pricing and promotion practices they thought undermined their key account relationships.
The evolving marriage of big data to analytics increasingly leads to a phenomenon I’d describe as “accountability creep” â€” the technocratic counterpart to military “mission creep.” The more data organizations gather from more sources and algorithmically analyze, the more individuals, managers and executives become accountable for any unpleasant surprises and/or inefficiencies that emerge.
Culture matters enormously. Do better analytics lead managers to “improve” or “remove” the measurably underperforming? Are analytics internally marketed and perceived as diagnostics for helping people and processes perform “better”? Or do they identify the productivity pathogens that must quickly and cost-effectively be organizationally excised?
For at least a few organizations, that’s led to “accountability for thee but not for me” investment. Executives use analytics to impose greater accountability upon their subordinates. Analytics become a medium and mechanism for centralizing and consolidating power. Accountability flows up from the bottom; authority flows down from the top.
Transforming the culture and practice of analytics inherently transforms your culture and practice of accountability.
“Fortune 500 companies are rushing to make big data investments. Who is leading this charge? What are they doing? What are they trying to avoid? A recent survey of C-level and function heads from Fortune 500 companies offers a unique glimpse into how the captains of industry are thinking about big data and how their companies are changing because of new insights gleaned from big data analyses. Randy Bean, a coauthor of the survey and cofounder of NewVantage Partners, which sponsored the study, sat down with David Kiron, executive editor of MIT Sloan Management Reviewâ€˜s Big Ideas initiatives, to discuss how top executives at some of the largest companies and organizations in the United States are managing big data.”
One was the promise of improved data-driven decision making. Everybody agreed on that in principle. All organizations expressed an interest in being able to do that, though what that means for different organizations varies. Some organizations are focused on strengthening their customer relationships; others are focused on managing business and systemic risk.
Most of these firms have been used to dealing with large volumes. That wasnâ€™t the critical issue. It was about integrating information from diverse sources. Across the board, that was really the primary focus of how firms wanted to use big data, and that included incorporating unstructured data.
Another key finding was that organizational alignment is a very critical factor in ensuring success. There was some division in terms of whether ownership for big data initiatives resided on the business side or the technology side, but there was common agreement that unless the business and technology sides worked together so that there was an understanding of the business objectives and the technology capabilities, a big data initiative would not be as successful as it should be.
the critical factors are primarily organizational alignment â€” getting the business and technology organizations to work together on the common objectives, understanding what the business objectives are and understanding what the technology capabilities are that will support those.
t itâ€™s not really the size and volume of the data. Itâ€™s the quality of the problem. The point there is that ultimately, organizations are trying to gain insights. That can be from small subsets of data or very large sets of data.
How do you translate and analyze vast amounts of data to gain key insights and accelerate that process so you can get from this data to the insights?
He suggested that chief information officers had really become infrastructure officers, and with so much of infrastructure activities migrating to the cloud, the CIO role was becoming less and less relevant to organizations.
Thatâ€™s probably an overstatement, but the point is that thereâ€™s been so much overuse and misuse of the term that organizations need and want to understand precisely how big data capabilities and big data initiatives will help them solve some of the top five business issues that theyâ€™re trying to address right now, be it customer service activities or compliance reporting and systemic risk.
We envision two data environments that coexist side by side. One is the traditional production operational environment, which has to be locked down â€” think of Fort Knox. I
The new environment is focused on discovery, and the benefit that big data technology and processes bring is that it makes it possible to â€œload and goâ€ â€” which means beginning to access and analyze all of your data without first going through the data engineering process, which is costly and time consuming.
The â€œdiscoveryâ€ environment can be an analytics sandbox to rapidly accelerate your ability to discern new patterns and the ability to integrate with a traditional production environment that will feed and enrich one another. We
“After covering some of the major trends of 2012 towards the end of the panel we turned out attention to what was coming in the year ahead. When asked what I thought needed to be on the business agenda, my response was Big Data, or as I more accurately described it, Staggeringly Enormous Data.
As I wrote in an article on Governance as opportunity, research by MITâ€™s Eric Brynjolfsson showed 5% higher productivity from organizations that do â€˜data-driven decision makingâ€™.
The key issue is the capabilities that organizations will need to get value from Big Data, and how to develop those capabilities.”
Consistently capturing relevant data. Many organizations already have a vast amount of data, and their immediate pressing issue is to extract value from that. However sustainable competitive advantage can only be gained from capturing the broadest range of potential valuable data.
Adding metadata. Most organizations have vastly more unstructured than structured data, and to extract value from their current trove requires a preliminary exercise of structuring and tagging that data that can be expensive and time-consuming. Capabilities in adding metadata to existing data are important, with automated tagging and the use of crowdsourcing two of the most promising domains.
Infrastructure and architecture. Clearly plenty of computing â€œironâ€ is required to store and manage massive amounts of data.
Data analytics. Demand for talented data scientists outweighs supply. Some organizations are fortunate to be able to attract and retain outstanding data analysts. However others are able to draw on external talent by leveraging data science competitions or defining specific projects.
Executive focus. Unless the top executive team recognizes the potential value of big data it will not allocate the requisite resources. More importantly, it will not spend the time to explore the questions and the possibilities that could lead to business value.
Communication of data analysis. The link between data analytics and executive focus comes largely from effective communication. Data visualization is a primary tool, which requires software but also softer skills in using visual representation to relate data to business value and decisions.
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