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The Measurement of Whispers « miro about 14 hours ago
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ushered in the globe shrinking concept of Degrees of Separation forever changing our perceptions of relationships.
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The foundation of all models is built on biological (agent contagion) models where the mathematical relationships between: Susceptibility, Exposure, Infectiousness, Recovery have been outlined. Marketers need only to substitute: Target market, Response rate, Propagation and Relevance to simulate wondrous scenarios.
But understanding how we might get there requires us to first consider the sudden popular rise of Hush Puppies. It was the single question which set off Gladwell’s populist book (The Tipping Point)
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According to Gladwell, Hush Puppies became popular because they were being worn by a select few ‘influential’ people that ‘infected’ others
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Nonsense thought Duncan J Watts, if these people were so influential why didn’t the other things they were wearing, doing, eating or drinking also gain popularity? Tough question that.
Watts brings forth a different model incorporating the principles of percolation (which considers the problem from the system’s ability to facilitate/hinder/contain the spread*, see here) and infection by basically wondering how forest fires got started.
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the essence of his Big Seed model:
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Seed: How many people initially exposed to the message
Propagation (Z): How many people this message has been forwarded to
Responsiveness (B): The % of those propagated that will ultimately act on the message when received
which recognizes the need/place for both mass and targeted attempts at ‘infectious persuasion’.
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You don’t have to be special in any way, just a man with a match.
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The success of any viral program rests on its ability to propagate. Sufficiently high and it can reach a point of critical mass that can precipitate a
cascade. One definition of a cascade (see:
A Theory of Fads, Fashion, Custom and Cultural change as Informational Cascades, Bikhchandani et al) is characterized by the point at which individuals “go with the flow” forgoing their private knowledge in favor of public knowledge.
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Many liken cascades to an unstoppable avalanche but in fact some cascades are exceptionally fragile and can easily revert to a random state bouncing back and forth between the possible outcomes.
What fascinates us all is why the system that is able to withstand a multitude of simultaneous and /or sequential shocks without any effect can undergo a massive transition triggered by an innocuous signal.
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but as marketers trying to convince the population they have the better mousetrap, the greater truth can turn out to be more serendipitous. Because if your brand happens to experience enough of these aborted propagations at the wrong place or time ….the secret to life, happiness and the pursuit of infinite wealth grinds to a screeching halt or at best plods along at some lower general equilibrium level… at least until the next wave rolls through.
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One exposure does not automatically create the desired follow-on impact, but in fact once exposed to the message – may sometimes cause them to become immune to its effect, killing off any future chance of propagation
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1. Connectivity: His simulations looked at a range of contacts
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to understand at what point is a more connected node/person more or less important to a cascade. The important distinction to note here is that connectivity would refer only to those for whom the message has potential relevancy – hence it is a subset, not the total size of one’s contact list.
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A) When the landscape has few/sparse connections cascades are less likely to occur.
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Within that frailty, anyone who happens to be relatively more connected – becomes critical to a cascade potential (the hip Hush Puppy innovators in the Tipping Point thesis).
B) When the landscape has many/dense connections one would expect cascades to be a more common occurrence, right? Wrong, what happens is that cascades become limited by their ability to overcome the local (ie; individual’s) stability threshold. In other words a crowd of skeptics is still a crowds of skeptics…but any cascades that do develop will be LARGE.
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those conditions would hold true unless a wave forms. The difference between a wave and a cascade is the amount of organization around it. Most of these simulations are based on random hops through a (connected) network…enough hops and enough conversions and a cascade develops. But if an organized assault manages to take shape and propagate, a cascade is more likely to result since the probability of overpowering the threshold is greatly enhanced.
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2. Threshold for change: The model suggests the fewer the connections the higher the minimum threshold for a cascade potential. Having only a few friends it would appear, means one needs more persuasion to change.
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3. Homogeneous/heterogeneous:
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variability facilitates cascades and homogeneity suppresses it,
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Residential Customer Service Home - Qwest Communications on 2009-12-31
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Speed Test - dslreports.com on 2009-12-31
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OBIEE and Daily Business Intelligence | Dashboard Examples - Samples - Tutorials on 2009-12-24
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There are so many wonderful digital dashboard related websites (dashboardspy, dashboardinsight, dashboardzone to name a few) including some vendor specific blogs. This website serves to be just a digest for all the dashboard content.
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Map Windows My Documents Folder to Network Drive | Team Tutorials on 2009-12-22
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MicroStrategy BI License Revenues Up 39% -- MicroStrategy Earnings -- InformationWeek on 2009-12-20
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Worldwide sales of BI software grew 22% in 2008 to $8.8 billion, according to Gartner, with SAP/Business Objects, SAS Institute, Oracle, IBM/Cognos, Microsoft, and MicroStrategy owning 75% of the market. By comparison, the market for worldwide BI software grew only 13% between 2007 and 2008. At an Oct. 20 conference, Gartner ranked advanced analytics as No. 2 on its Top 10 list of the most strategic technology areas for 2010.
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Dashboard Best Practice – Google Analytics Intelligence Report | Pointy Haired Dilbert: Charting & Excel Tips - Chandoo.org on 2009-12-20
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Yesterday while checking my website analytics reports on Google analytics site, I have noticed a new beta feature called “Intelligence”. Out of curiosity I clicked on it. It took me to a an intelligence alert dashboard.
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There are at least 10 dashboard best practices you can pick up from this and use in your day to day work. See it:
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- Use date windows so that end-users can change the date to see different report. This can be done in various ways. For eg. in our KPI Dashboards using Excel posts, we have used scroll-bars. If you have pivot reports, just add the date to “header” section. Otherwise, you can also use data filters to make your charts dynamic.
- Band / highlight selected dates to so that users know what they are looking. This can be done using simple formulas and a combo-box control. Here is an example of conditionally banding charts in excel.
- Use effective colors - Google uses simple but very effective colors. [Get 73 beautiful excel chart templates and make better charts]
- Use basic charts
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- (scroll down and see the video to understand how this dashboard works). Making Dynamic Charts in Excel – Comprehensive tutorials & examples.
- Be smart with data labels: While data labels can help understand the charts, often dashboards have too many charts and thus data labels make it look cluttered. A simple solution is to use data labels conditionally. Ajay at has another good example at databison on interactive data labels.
- Let your users customize the dashboard: This means ability to switch rows to columns, choosing how much information to see etc.
- Highlight important information: use different font (or font size), have special area on the dashboard to display key metrics etc.
- Show metrics by dimension:
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A good dashboard tells what is important and what is not: While we can argue that dashboards should show “only” the important, a good dashboard lets user customize the contents and clearly tells what is not important if it ever shows up.
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There is so much more beauty and design behind this google dashboard than what I can capture in a simple post. So I have recorded a small video (4 mins).
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Dirty Sparkle!: Storytelling and Resonance on 2009-12-19
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as I sat in the station drinking my tea out of a cardboard cup, I had a rare opportunity to explain my theory on more qualitative science to someone who is most definitely steeped in the quantitative. I explained that storytelling, even at the level of the shop floor or canteen, is a valuable tool in finding out what is wrong with the safety and reliability of, say, an aircraft. Because of hierarchical barriers, many manual workers feel unable to communicate their worries to their managers and they do not have the appropriate expert language. This barrier and translate into a culture of non-disclosure as confidence dips. A better way would be to communicate on a level where each party is free from hierarchical pressure and can recognise a common language outside the range of expertise. My colleague was excited by this but expressed his reservations: this could never be defended in the engineering workplace as there was just no valid explanation for this.
I explained to him that the valid explanation lay in, of all unlikely places, feminist thought. The diffusion of hierarchy is a much studied area in equality and discrimination in the work of Ann Oakley (1981). Further, the validity of storytelling as an exchange of information through understanding (and not just a tool for social interaction) lies in the study of narrative psychology and identity construction(Crossley, 2000a; Frank, 2000; McAdams, 1993, Mishler, 1999). Each person has a 'personal script' running through their consciousness that tells them 'who I am' and 'who I am in the world'.
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The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management on 2009-12-18
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As we discussed in Driven to Perform: Risk-Aware Performance Management From Strategy Through Execution (Nenshad Bardoliwalla, Stephanie Buscemi, and Denise Broady, New York, NY, Evolved Technologist Press, 2009), the end state for next-generation business applications is not merely to align the transactional execution processes contained in applications like ERP, CRM, and SCM with the strategic analytics of performance and risk management of the organization, but for those strategic analytics to literally drive execution. We called this “Strategy-Driven Execution”, the complete fusion of goals, initiatives, plans, forecasts, risks, controls, performance monitoring, and optimization with transactional processes.
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While classic analytic tools and applications have always done a good job of helping users understand what has happened and then analyze the root causes behind this performance, the value of this information is often stale before it reaches its intended audience.
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3. The industry will put reporting and slice-and-dice capabilities in their appropriate places and return to its decision-centric roots with a healthy dose of Web 2.0 style collaboration.
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for more tactical and strategic decisions, mash-ups will allow users to assemble all of the relevant data for making a decision, social capabilities will allow users to discuss this relevant data to generate “crowdsourced” wisdom, and explicit decisions, along with automated inferences, will be captured and correlated against outcomes. This will allow decision-centric business intelligence to make recommendations within process contexts for what the appropriate next action should be, along with confidence intervals for the expected outcome, as well as being able to tell the user what the risks of her decisions are and how it will impact both the company’s and her own personal performance.
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4. Performance, risk, and compliance management will continue to become unified in a process-based framework and make the leap out of the CFO’s office.
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5. SaaS / Cloud BI Tools will steal significant revenue from on-premise vendors but also fight for limited oxygen amongst themselves.
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From a functionality perspective, these tools offer great usability, some collaboration features, strong visualization capabilities, and an ease-of-use not seen with their on-premise equivalents whereby users are able to manage the system in a self-sufficient fashion devoid of the need for significant IT involvement.
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Only vendors whose offerings were designed from the beginning for cloud-scale architecture and thus whose marginal cost per additional user approaches zero will succeed in such a commodity pricing environment, although alternatively these vendors can pursue going upstream and try to compete in the enterprise, where the risks and rewards of competition are much higher.
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6. The undeniable arrival of the era of big data
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The largest web players such as Google (BigTable),Yahoo (Hadoop), Amazon (Dynamo), Facebook (Cassandra) have built their own solutions to handle their own incredible data volumes, with the open source Hadoop ecosystem and commercial offerings like CloudEra leading the charge in broad awareness. Additionally, a whole new industry of DBMSs dedicated to Analytic workloads have sprung up, with flagship vendors like Netezza, Greenplum, Vertica, Aster Data, and the like with significant innovations in in-memory processing, exploiting parallelism, columnar storage options, and more.
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7. Advanced Visualization will continue to increase in depth and relevance to broader audiences. Visionary vendors like Tableau, QlikTech, and Spotfire (now Tibco) made their mark by providing significantly differentiated visualization capabilities compared with the trite bar and pie charts of most BI players’ reporting tools. The latest advances in state-of-the-art UI technologies such as Microsoft’s SilverLight, Adobe Flex, and AJAX via frameworks like Google’s Web Toolkit augur the era of a revolution in state-of-the art visualization capabilities.
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8. Open Source offerings will continue to make in-roads against on-premise offerings.
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9. Data Quality, Data Integration, and Data Virtualization will merge with Master Data Management to form a unified Information Management Platform for structured and unstructured data.
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Increasingly, data quality and data integration will be interlocked hand-in-hand to ensure the right, cleansed data is moved to downstream sources by attacking the problem at its root. Vendors including SAP BusinessObjects, SAS, Informatica, and Talend are all providing these capabilities to some degree today.
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canonical information models can be overlayed on top information assets regardless of where they are located, capable of addressing the federation of batch, real-time and event data sources. These disparate data soures will need to be harmonized by strong Master Data Management capabilities, whereby the definitions of key entities in the enterprise like customers, suppliers, products, etc. can be used to provide semantic unification over these distributed data sources. Finally, structured, semi-structured, and unstructured information will all be able to be extracted, transformed, loaded, and queried from this ubiquitious information management platform by leveraging the capabilities of text analytics capabilities that continue to grow in importance and combining them with data virtualization capabilities.
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10. Excel will continue to provide the dominant paradigm for end-user BI consumption.
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With Excel 2010’s arrival, this includes significantly enhanced charting capabilities, a server-based mode first released in 2007 called Excel Services, being a first-class citizen in SharePoint, and the biggest disruptor, the launch of PowerPivot, an extremely fast, scalable, in-memory analytic engine that can allow Excel analysis on millions of rows of data at sub-second speeds.
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Tarek Hoteit Notebook: The Heart-Brain Connection: The Neuroscience of Social, Emotional, and Academic Learning | Edutopia on 2009-12-15
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The Heart-Brain Connection: The Neuroscience of Social, Emotional, and Academic Learning | Edutopia
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