AbeBooks has millions of books. Description: Facts, as such, never settled anything. They are working tools only. It is the implications that can be drawn from the facts that count, and to evaluate these requires wisdom and judgment. Clarence B.
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Facts, as such. Sign up. Mallya, hiding somewhere in London. Well, there is a correlation, but…what the heck? Causality means A causes B, whereas correlation on the other hand, means that A and B tend to be observed at the same time. This post is all about correlation vs causation, and the mad rush to engineer answers from the vast amount of data, without understanding the why. Google system proved to be more useful and a timely indicator than government reports.
Storing and managing these huge pools of information is a challenge for many enterprises, but big data also offers significant opportunities. Contact us Businesses are now faced with building comprehensive, connected data systems to understand customer behavior, map customer journeys and redefine them to create not only delightful experiences but also get into a sense-and-respond paradigm.
This is one of the important characteristics of a digital enterprise — an enterprise with a data platforms that champion the cause of stitching together disparate data to develop a customer insight data repository.
In addition, a recent report from Forrester, describes a future where cloud-enabled business networks deliver an ease of engagement never before seen in traditional business-to-business B2B collaboration solutions.
According to Forrester, these networks enable business collaboration by sharing data in real time on a single cloud platform based on trust relationship models rather than by mapping and exchanging B2B data.
As industry boundaries continue to blur and multiple relationships are increasingly required to create value, this business network capability will manage increased collaboration complexity and make every relationship more effective. As a best case scenario — the connected digital experience will look something like this: A grocery chain collects data from your shopping, social, and other connected behaviors, combines it with sensor data from smart-connected devices and analyzes it to understand your eating and shopping habits.
There are many reasons to be excited about the broader opportunities offered to us by the ease with which we can gather and analyze vast data sets. However, if you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. Probably not. An example is, Potholes on roads. One of the popular radio stations in Bangalore urged its listeners to use a smartphone app and take pictures so that civic authorities can act on it.
As citizens of Bangalore downloaded the app, drove around, uploaded the pictures, suddenly the civic authorities got on their hand an informative data exhaust that addresses a problem sending inspection guys to survey various roads and manually record the state of the roads in a way that would have been inconceivable a few years ago, that too without much involvement from the civic authority.
LTI What the smartphone app and the citizen journalism really produced is a map of potholes that systematically favored the most commuted roads used by IT professionals, who were owning smartphones and were really suffering because of the potholes elongating their commute times. The point I am trying to make is, what about other roads, which are not used by these IT professionals and where there are serious pothole problems?
There must always be a question about who and what is missing, especially with a messy pile of found data. The paper became famous as a provocative statement highlighting a serious issue. When examining a pattern in data, scientists are trained to ask whether such a pattern might have emerged by chance.
The multiple-comparisons problem arises when you start looking at many possible patterns. This problem is more serious in found data sets, where just by applying sophisticated algorithms, one can generate vastly more possible patterns than there are data points to compare.
Thus, without careful analysis and questioning the WHY, it is inevitable that you would end up with a dismal ratio of genuine to spurious patterns of signal-to-noise. Fundamentally, asking whether correlation is enough, is actually asking the wrong question. The higher that confidence level, the more reasonable it is to take action in response. If the risk of acting and being wrong is extremely high, acting on even a strong correlation may be a seriously grave mistake.
The data might show you that people who buy meat and milk are good car insurance risks, while people who buy pasta and spirits are poor risks. The good news is that this explosion of data opens up a lot of possibilities. Being smart with data is not about collecting a million tweets. Follow the Author It is about the in-depth analysis of tweets, including analysing hashtags containing metadata around the device type, geographic location, time and the context of the conversation.
In these insights are the true value of data, allowing companies to push information to customers that is context-sensitive and meaningful.
Companies that are using data to its full advantage look at it in three ways in order to pull as many insights as possible: An individualistic approach is mostly gut-driven. It relies on analysts who have spent a long time in a particular domain, finding answers that require access to all types of data across various systems. Process-centric is a disciplined approach to data.
It involves consistently employing common processes, and the reuse of components. A Data-driven approach identifies evidence-based data analysis traits. This pattern requires deeper data analysis than the two approaches above, as it requires an understanding of context, and the application of sophisticated algorithms to identify patterns.
Big Data Imperatives
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Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.
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