Big (and Smart) Data for Digital Globalization

Data is all around us whether we use it or we are part of it. More than another trend, data is the way to move with agility and make every step and achievement tangible for those who do not see or believe it. One of the most transformational and accelerating factors of digitization is precisely how data is considered, leveraged, valued, and distilled. As data mining is not new it has become more than just a back office type of activity. It is all about turning facts into more than facts, figures into more than figures, and content into more than content.

For digital globalization practitioners and leaders, data shines like a glittering prize. That is why they face similar challenges to all business leaders when it comes to making the most of data. With the world to conquer and a number of diverse audiences to engage, they have to transform big data into smart data to focus on what enables making–and avoids breaking–the digital experiences local customers require. Specifically they must pin down the right data at the right time in the content supply chain to convert it into reliable indicators and valuable assets in the long run. In addition, due diligence is required to cover the cost and efforts of funneling, acquiring, and maintaining data. While the amount, the nature, and the scope of data depend on digital globalization targets and priorities, several categories may help establish a good base line to identify smart data and agree on a starting point for global expansion.

  1. Customer understanding data-Ranging from general (e.g. census) to segmentation data these data enable you to bear in mind what customers do at all times as prospects, decisions, buyers, or users.
  2. Usage data-As typical performance data this remains crucial in any proper mix of smart data for digital globalization.
  3. Content effectiveness data-Capturing and measuring the real impact of content on experiences is tricky and must reflect the nature and ecosystem of the content.

Read more at Big (and Smart) Data for Digital Globalization

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KB – Neural Data Mining with Python sources

KB – Neural Data Mining with Python sources

The aim of this book is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify.

The algorithms of computing contained within the book are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.

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Five Data Mining Techniques That Help Create Business Value

Five Data Mining Techniques That Help Create Business Value

The term data mining first appeared in the 1990s while before that, statisticians used the terms “Data Fishing” or “Data Dredging” to refer to analysing data without an a-priori hypothesis. The most important objective of any data mining process is to find useful information that is easily understood in large data sets. There are a few important classes of tasks that are involved with data mining:

  1. Anomaly or Outlier detection
  2. Association rule learning
  3. Clustering analysis
  4. Classification analysis
  5. Regression analysis

Data mining can help organisations and scientists to find and select the most important and relevant information. This information can be used to create models that can help make predictions how people or systems will behave so you can anticipate on it. The more data you have the better the models will become that you can create using the data mining techniques, resulting in more business value for your organisation.

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