Big Data’s Relationship with Business Intelligence and Data Warehousing – Big Data Roundup

1. Big Data’s Relationship with Business Intelligence and Data Warehousing

It seems like you can’t pick up a technical magazine without reading about how big data is changing the world—and the untold implications of this technology. But what the heck is big data? And didn’t we already solve this thing with business intelligence and data warehousing?

Big data, or BD, is the collection of transaction-level detail for analysis. The data is kept close to the transactional detail so it can be examined for hidden trends only seen when you analyze the individual transactions. The data can come from different sources but is analyzed in a common pool. This is most often a feed (or copy) of the transactions as they occur; they are streamed to the BD solution. Often, the value of the data is very time-dependent; the sooner the information is available, the more valuable it is.

There are four key terms used when talking about BD:
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2. Visualizing Big Data with augmented and virtual reality: challenges and research agenda

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.
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3. A Successful Approach to the Big Data Adoption Journey

Randy Bean recently wrote in the Wall Street Journal, “Big Data represents a business adoption paradox: It promises speed, but successful business adoption takes time. When I advise executives or speak to business groups, I encourage organizations to view business transforming initiatives like Big Data as a journey. Success ultimately depends upon organizational alignment, process change, and people. Organizations need to develop a long-term plan and destination with many checkpoints along the way. True there are opportunities for “quick wins”– to ensure credibility, build organizational support, establish momentum, and secure funding—but for the most part, patience and persistence are essential.”
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4. Why your next big database decision may be a graph

NoSQL databases are clearly on the rise, but not all NoSQL is created equal.

After all, 451 Research recently discontinued its longstanding tracking of NoSQL database popularity, arguing that since “none of the top 10 look like changing places any time soon, and none of the players outside stand any chance of breaking into the top 10, the time has come to retire the NoSQL LinkedIn Skills Index.”
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Data analytics isn’t about Insights. Idexcel Big Data Roundup

1. How To Use Data To Outsmart Your Competitors

The pressure’s on to use data to outsmart your competitors. Here are six ways companies can use data to imagine and even re-imagine what’s possible.

“Business as usual” can be a risky business practice, especially when there’s cultural resistance to change. While some companies are embracing agile practices, there are a number of data-related barriers that keep companies from reaching their potential, most of which have to do with people, processes, and technology. Read more…

2. Ten Ways Big Data Is Revolutionizing Supply Chain Management

Bottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today. Read more…

3. How Data Projects Drive Revenue Goals

The vast majority of organizations have either already implemented a big data project or plan to do so, according to a recent survey from CA Technologies. The report, titled “The State of Big Data Infrastructure: Benchmarking Global Big Data Users to Drive Future Performance,” indicates that a great deal of these projects are integrated throughout the entire organization. Companies are pursuing big data and analytics primarily to improve the consumer experience while adding to their customer base. However, there are formidable challenges, including a lack of trained staffing to make data projects succeed, as well as the inherent complications of such implementations. Read more…

4. Importance of Big Data Analytics for Business Growth

Until recent years companies have always evaded the question of using data analytics for business execution, leave alone big data. Most of the time it was due to cost of analysis that the organisations kept in mind while keeping away from data analytics. With everything going digital, data is pouring in from all kinds of sources imaginable. Organisations are getting inundated with terabytes and petabytes of data in different formats from sources like operational and transactional systems, customer service points, and mobile and web media. The problem of such huge data is storage and with no proper utilisation of the data, collecting and storing is a waste of resource. Earlier it had been difficult to process such data without relevant technology. Read more…