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The Power of Data Insights - Big Data as the Fuel and Analytics as th… The Smart Way to Deal With Messy Data. The processing required to prepare unstructured data for analysis can be cumbersome and prone to error.

The Smart Way to Deal With Messy Data

That’s why companies should do more to organize their data before it is ever collected. Unstructured data — data that is not organized in a predefined way, such as text — is now widely available. But structure must be added to the data to make it useable for analysis, which means significant processing. That processing can be a problem. In a form of modern alchemy, modern analytics processes now transmute “base” unstructured data into “noble” business value.

Unfortunately, while these processing steps are impressive, they are far from free or free from error. We all know how each step in a process mangles information. Becoming a Data-Driven Organization. Data-Driven City Management. Chapter 3 Spotlight: Selection of Key Smart City Participants While no single organization or person coordinates all of the distinct efforts to improve the integration of information technology with Amsterdam’s city services, the city is well-placed to determine which of the many ASC pilots should be expanded.

Data-Driven City Management

The following sections spotlight key city managers, a smart city project, a founding member of the ASC platform, and the AMS Institute. City Managers Spotlight Baron became Amsterdam’s chief technology officer in March 2014, almost despite himself. Instead, Amsterdam offered him the job. One thing Baron knew perhaps better than anyone were the headaches cities face when trying to be smarter. Once Baron took over as Amsterdam’s first chief technology officer, he launched a data inventory. He also knew that the various companies involved in creating smart cities didn’t really understand cities. Trouble Comes to Light And that’s just the complication for lighting.

Mr. Automating big-data analysis. Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a “feature set,” or aspects of the data that are useful for making predictions.

Automating big-data analysis

The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses. Lessons from Becoming a Data-Driven Organization. Chapter 1.

Lessons from Becoming a Data-Driven Organization

Key Performance Indicators (KPI) Examples, Dashboard & Reporting. The Reason So Many Analytics Efforts Fall Short. Given the role analytics has played in reshaping industries and rewarding innovative adopters over the last two decades, it is surprising how frequently we are asked: “Does this analytics stuff really work?”

The Reason So Many Analytics Efforts Fall Short

More often than not, the reason for the skepticism is prior efforts that did not produce the expected competitive advantage. Such skepticism led us to study the impact that analytics has had on our clients over the last 20 years. Our surprising finding: Efforts to adopt analytics upset the balance of power in the C-suite, and this shift often had a negative impact on analytics initiatives. Given the myriad other factors buffeting companies at any given time, the design of our assessment was intentionally simple.

We only included companies that had implemented a major analytics initiative with an innovation or similar agenda. Further study of the less-successful cohort revealed that leadership issues were often at the heart of the problems. What should a CEO do? BigDataPhile — Big Data & Analytics News. Data. Welcome back.

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Size doesn’t matter in Big Data, it’s what you ask of it that counts. Big Data is changing the way we do science today.

Size doesn’t matter in Big Data, it’s what you ask of it that counts

Traditionally, data were collected manually by scientists making measurements, using microscopes or surveys. These data could be analysed by hand or using simple statistical software on a PC. Big Data has changed all that. These days, tremendous volumes of information are being generated and collected through new technologies, be they large telescope arrays, DNA sequencers or Facebook. Variety, Not Volume, Is Driving Big Data Initiatives. For large corporations, data variety trumps volume when looking for insights.

Variety, Not Volume, Is Driving Big Data Initiatives

When many executives think of Big Data, they think of large volumes of data. A common notion is that bigger is often better when it comes to data and analytics, but this is not always the case. In their 2012 article, Big Data: The Management Revolution, MIT Professor Erik Brynjolfsson and principal research scientist Andrew McAfee spoke of the “three V’s” of Big Data — volume, velocity, and variety — noting that “2.5 exabytes of data are created every day, and that number is doubling every 40 months or so. A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text. Big Data: The Management Revolution. Big Data. Home. Content - IEEECS. An IT manager recently told me he hadn’t touched the applications on his mainframe since Y2K.

content - IEEECS

For many other organizations, it has been even longer than that — and for good reason. Over the last 25 years, mainframes have been a solid investment in term of reliability, performance, and security. It’s estimated that within the insurance industry, half of all core applications are still on a mainframe. But for insurers and other data-intensive industries that rely on mainframe applications and data, those days are coming to an end. The modernization of mainframe applications is now a business imperative. Guest article by Ed Franklin, VP of Global Marketing, TmaxSoft That can be a scary proposition for those that have relied on their mainframes for decades. The main drivers for mainframe modernization To begin with, mainframes are running COBOL, PL/1, or other languages difficult to support. Then there’s the cloud. But modernizing is not as easy as it seems.

IEEE Computer Society. From the October-December 2015 issue Learning Visual Semantic Relationships for Efficient Visual Retrieval By Richang Hong, Yang Yang, Meng Wang, and Xian-Sheng Hua In this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc.

IEEE Computer Society

We first define five specific relationships between concepts. We then extract some concept relationship features in textual and visual domain to train the concept relationship models.

Open Data