10 Hot Data Analytics Trends — and 5 Going Cold

Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.

Data analytics are fast becoming the lifeblood of IT. Big data, machine learning, deep learning, data science — the range of technologies and techniques for analyzing vast volumes of data is expanding at a rapid pace. To gain deep insights into customer behavior, systems performance, and new revenue opportunities, your data analytics strategy will benefit greatly from being on top of the latest data analytics trends.

Here is a look at the data analytics technologies, techniques and strategies that are heating up and the once-hot data analytics trends that are beginning to cool. From business analysts to data scientists, everyone who works with data is being impacted by the data analytics revolution. If your organization is looking to leverage data analytics for actionable intelligence, the following heat index of data analytics trends should be your guide.
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Big data, marketing and decision-making – what is it all about?

Last week I got asked if I know about big data and how you use it in digital marketing. Yes, of course, I do. I’ve been using big data for years when analysing numbers from websites and social media.

I’ve also been fortunate to speak at many conferences where some of the speakers are fully trained ‘big data ninjas’, and I’m lucky to know some of them personally.

Big data is complex information, and it feels as overwhelming as a huge waterfall. It’s only if you present big data in a meaningful way it helps you to make better decisions. Continue reading

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…

Big Data: The Engine Driving the Next Era of Computing

You are at a conference. Top business honchos are huddled together with their Excel sheets and paraphernalia. The speaker whips out his palmtop and mutters ‘big data’. There follows an impressive hush. Everyone plays along. You feel emboldened to ask, “Can you define it?” Another hush follows. The big daddys of business are momentarily at a loss. Perhaps they can only Google. You get it? Everyone knows, everyone accepts, big data is big, but no one really knows how, or why. At any rate, no one knows enough straight off the bat.

In the Beginning was Data. Then data defined the world. Now big data is now refining the data-driven world. God is in the last-mile detail. Example: In the number-crunching world of accountancy, intangibles are invading the balance sheet values. “Goodwill” is treated as an expense. It morphs into an asset only when it is acquired externally like say, through a market transaction. Data scientists now ask why can’t we classify Amazon’s vast data pool of its customers as an “asset”? Think of it as the latest straw in the wind of how big data is getting bigger.

Big data is getting bigger and bigger because data today is valued as an economic input as well as an output. The time for austerity is past. Now is the time for audacity. Ask how. Answer: Try crowd sourcing your data defining skills.

When you were not watching, big data was changing the way the technology enablers play the game in the next era of computing. Applications are doing a lot more for a lot less.

Big data isn’t about bits or even gigabytes. It’s about talent. Used wisely, it helps you to take decisions you trust. Naysayers of course see the half-full glass as if it is under threat of an overspill. They insinuate that big data leads to relationships that are unreal. But the reality we don’t know is what is behind all that big data. It is after all, a massy and classy potpourri: part math, part data, with some intuition thrown in. It’s ok if you can’t figure out the math in the big data, because it is all wired in the brain, and certainly not fiction or a fictitious figment of imagination.
When you were not watching, big data was changing the way the technology enablers play the game in the next era of computing. Applications are doing a lot more for a lot less. Just to F5 (we mean refresh…):
You and me can flaunt a dirt cheap $50 computer the size of your palm AND use the same search analysis software that is run by obscenely wealthy Google.

Every physical thing is getting connected, somewhere, at some time or the other, in some or the other ways. AT&T claims a staggering 20,000% growth on wireless traffic over the past 5 years. Cisco expects IP traffic to leap frog ahead and grow four-fold by 2016. And Morgan Stanley breezes through an entire gamut of portfolio analysis, sentiment analysis, predictive analysis, et al for all its large scale investments with the help of Hadoop, the top dog for analyzing complex data. Retail giant Amazon uses one million Hadoop clusters to support their affiliate network, risk management, machine learning, website updates and lots more stuff that works for us.

Data critics though are valiantly trying to hoist big data on its own petard by demanding proof of its efficacy. Proof? Why? Do we really need to prove that we have never ever had a greater, better analyzed, more pervasive, or expansively connected computing power and information at a cheaper price in the history of the world? Give the lovable data devil its due!