Fusion Tables and Heat Maps

This heat map created is a graphical two-dimensional representation of the data census figures from 2011 with the random distribution of counties and their boundaries across Ireland, their values are represented by colours. This fairly simple heat map provides an immediate visual summary of information. I have used colour to communicate relationships between the two data values that would be much harder to understand if presented numerically. Ok in the, Irish population example, I got two tables: one contains county-by-county population figures, the other one contains geographic information of each state and its borders and I uploaded them to fusion tables, were I merged the two tables. I was able to then visualize the new merged table on a map and subsequently applied a style to the map. The fusion table was able to pull county names, population figures and border information and outline the facts onto a base map.
Counties are coloured and categorized according to their population density. The graduated colour scheme allows for a quick and easy analysis of this data. It is evident from the map that the yellow coloured counties (e.g. Dublin, Galway and Cork) are the most densely populated areas with more than 250,000 people living in these counties. It is also apparent that the orange coloured counties (e.g. Leitrim) are the least densely populated with between (15,000 and 55,000) living in these areas. It is also therefore easy to surmise that the population density of the light green shows a big concentration of between (55,000 and 87,000) right up through the midlands and into Sligo.

By using the filter option on heat maps you can isolate different values take total population for instance this can be selected and used to see these values mapped in relation to Ireland. Secondly heat maps can be sorted in ascending or descending order. This is useful when trying to decipher quickly which areas have the lowest and highest values (e.g. figure out which county has the most females), or it is excellent when trying to quickly search for areas with specific values (you want the results on male population in Ireland so that counties with between 65,000 and 150,000 males are only displayed on the map, as shown below. This map could also provide an interactive and visualization aid for the distribution of the elderly population across Ireland.

From a conceptual point of view the preparation of heat maps can now cover a wide range of variables, not just population figures and distribution of counties. Areas like religion, nationality, education, social class, industry of employment, occupation, housing, cars per households and health and disability all fall under this theme, you could even have a scenario were you have a number of different variables to show the percentage of households with central heating powered by peat and map them accordingly, you might even be able to elaborate on the type of fuel used, the type of sewage system, the tenure(is it owner occupied or rented) or the actual type of housing unit(detached, one bed apt etc.). The possibilities are endless and one thing is for sure it without doubt shows very clear patterns throughout the country for whatever geo-spatial mapping you require.

Coaching by Numbers: Is Data Analytics the Future Of Management?

 

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Maths over Mourinho? Analytics over Ancelotti? Data analysis is now commonplace in both the sporting and business worlds, but does human decision making still dominate in management, let us investigate.

 

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Most of us are familiar with the 2011 film moneyball, were the killer weapon in the movie is data, and they place their trust in computer-generated algorithms rather than common sense. The film spurred a great deal of speculation about the idea that technology may eventually replace sports managers. The underlying logic to this is twofold. First, computers are able to gather and process much more data than humans do, which enables them to better predict future performance, and secondly unlike humans, computers are not biased by emotions or subjectivity, so their decisions are bound to be more rational than ours.

 

Data alone is trivial, indeed it is only when combined with expertise, experience and knowledge that   data can enhance our ability to make the right decisions. The point of data is to refine our intuition, but, at the same time, a great deal of intuition is needed to make sense of any data. Unless you know what to look for, the data will only show numbers. This is why experts are capable of making intuitive decisions that mirror data-driven decisions.

 

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Humans are only partially rational, because of this, a purely rational approach to managing people does not work. This why sports athletes need human coaches, who can tune into their emotional states and empathise with them. Of course it may be possible to refine artificial intelligence to mimic human coaches in this task, but a fundamental difference between machines and humans will remain, namely that machines won’t care about the athletes – at best, they will be able to fake feelings for them but they will still seem pretty unbelievable. Athletes are pre-wired to respond more emphatically to humans than computers. Having your coach watching you creates a strong process of psychological influence, called leadership, which machines will never manage to imitate. Thus even if data does a good job at diagnosing problems, the intervention – acting on those problems, including making decisions and influencing athletes – is best left in the hands of humans.

 

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The application of technology and data to sports management mirrors the wider realm of business. Consider the field of talent management, the area of human resources concerned with the selection, motivation, and retention of employees, especially at the top of the organisational hierarchy. Despite substantial technological developments in this area during the past decade, big data and computer-driven algorithms have yet to have a real impact on management practices. Sure, it is now easier, faster, and cheaper to find suitable employees for a job, to quantify their contribution to the company, and to make data-driven decisions regarding rewards, promotions and retention.

 

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However, few organisations have adopted such tools widely, and those who have are not obviously more effective than their counterparts. Besides, there is a high price for the dataification of management practices. First, despite the objectivity of such practices they are unlikely to be perceived as fair by the workforce. Second, making these practices transparent increases the probability that individuals play or game the system (just like hotel owners may fake their tripadvisor ratings, or those of their competitors). Third, when transparency is avoided ethical issues and anonymity concerns emerge. For instance, most companies would learn a great deal about their employees by mining their e-mail data, but I suppose who would want to work in a place like that.

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In short, sports analytics, computer-driven algorithms, and big data can certainly improve human decision-making in the field of competitive sports, but so long as the athletes are human, technology alone will not improve their performance. Data can help us make better predictions, but it will not make people more predictable than they already are. Finally most coaches, clubs and managers have access to the same quality and quantity of data, but significant differences between their performances remain because human decision-making still dominates the game, so despite the appeal of sports analytics it is fairly unlikely that jose mourinho or carlo ancelotti will be out of work soon.

The Big Data Revolution

 

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How do we define big data?

Ok while I fully expect every individual or company to add its own personal tweaks here or there, here is the one-sentence definition of big data to get he conversation really started.

Big data is a collection of data from traditional and digital sources inside and outside of a company that represents a source for ongoing discovery and analysis. Some people like to constrain big data to digital inputs like web behaviour and social network interactions, however we cannot  exclude traditional data derived from product transaction information, financial records and interaction channels, such as call centre and point-of-sale. All of that is big data too, even though it may be dwarfed by the volume of digital data that is now growing at an exponential rate. In defining big data it is very important to understand the mix of unstructured and multi-structured data that comprises the volume of information.

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Unstructured data comes from information that is not organized or easily interpreted by traditional databases or data models, and typically, it is text heavy. Metadata, Twitter tweets, and social media posts are good examples of unstructured data.Multi-structured data refers to a variety of data formats and types and can be derived from interactions between people and machines, such as web applications or social networks. A great example of this would be web log data, which includes a combination of text and visual images along with structured data like form or transactional information.

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Every enterprise needs to fully understand big data – what it is to them, what it does for them, and what it means to them. The importance of big data is immense, this can be achieved through a multitude of different features which offer a broad spectrum within organisations, examples of these could be a data analysis tool, data warehouse testing, data asset management and comparative data analysis all of which provide interactive aids in the big data phenomenon.

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Information is arguably the most important fuel businesses run on. Intellectual property such as patents, institutional knowledge collected and stored by employees, sentiment gleaned from millions of social media posts, and consumer insights from the analysis of myriad online transactions are just a few examples of information assets companies leverage today. Companies all over the world need to wake up to the reality that information governance is more important in the era of big data than it was beforehand. New big data tools leveraging technology such as Hadoop can process and analyse high volumes of data at reasonable costs, creating business intelligence that companies can use for competitive advantage. The beauty of Hadoop is that business users can keep everything which is crucial because organisations do not want to archive or delete meaningful data.

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Ok this brings us on nicely to “what is meaningful data” or “how does a company know what data is meaningful”, Business Intelligence or (BI) programs can make sense of structured data, giving companies a good – or even exact – sense of what data is meaningful. The percentage of a companies information volume that consists of structured data is surprisingly fairly small, so information hoarding in order to leverage big data tools may work in the structured data world, but will not work in the broader information world that includes unstructured content, most of which is duplicate information or unnecessary (think of all the junk and transitory email). You might ask, how do we know what to delete?, well according to the experts the current methods of information classification are inconsistent and do not scale well. The most deleted content? Email which is broadly time-based deletion, meaning that companies delete email after a certain amount of time, this ultimately could lead to deleting valuable information. What is needed is a way to analyse information automatically, with some human review to judge its business value. While BI has gained mainstream traction in the structured data world, content analytics have not yet in the unstructured content world. What companies must understand is that big data and the intelligence it can deliver is good and worthy of embracing, and that effective information governance not only helps make business operations more efficient, but very importantly mitigates risk. Most organizations are so busy just trying to manage structured information that they haven’t yet addressed unstructured content , much less given enough attention to litigation risk associated with information. Now is the time.

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