1. Articles in category: Big Data and Analytics

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    1. Key trends in machine learning and AI

      You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. Here are the top takeaways from the conversations at the summit.

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    2. The two main pitfalls of business intelligence as we know it

      Business Intelligence, or as it’s more commonly known as in today’s lexicon “BI,” is one of the first things that pops into professionals’ minds when anything data-related in the workplace is brought up. Whether you’re on the information technology side of the business or a P&L owner, the answer to any information problem is to typically throw a BI solution at it. But, to be completely straightforward, this needs to stop.

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    3. Exploiting machine learning in cybersecurity

      Thanks to technologies that generate, store and analyze huge sets of data, companies are able to perform tasks that previously were impossible. But the added benefit does come with its own setbacks, specifically from a security standpoint. With reams of data being generated and transferred over networks, cybersecurity experts will have a hard time monitoring everything that gets exchanged — potential threats can easily go unnoticed.

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    4. Putting deep learning to work

      After demonstrating discontinuous jumps in image recognition performance and defeating Korean grandmaster Lee Se-dol at Go, a game long resistant to computer mastery, deep learning has kicked up a swirling cloud of hype. And controversy. On the one hand, serious folks are studying how to prevent a recursively self-improving super intelligence from seizing Earth’s reins from humanity. On the other, IBM’s “cognitive” marketing claims are rightly being called out as hyperbolic.

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    5. The Democratization of Public Data and Analytics Tools

      In recent years, we’ve seen a quiet but steady movement to open up more datasets for use by researchers, local governments, product development teams, and just about anyone else. Today, this “open data” movement makes an enormous amount of data freely available. In practical terms, when people talk about open data, they are usually talking about government data (local, state, and federal). The fact that data in the public sector is legally available doesn’t mean it is accessible. 

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    6. Cloud or on-prem? This big-data service now swings both ways

      There are countless "as-a-Service" offerings on the market and typically they live in the cloud. In 2014, startup BlueData blazed a different trail by launching its EPIC Enterprise big-data-as-a-service offering on-premises instead. On Wednesday, BlueData announced that the software can now run on Amazon Web Services (AWS) and other public clouds, making it the first BDaaS platform to work both ways, the company says. The BDaaS market is expected to be worth $7 billion by 2020, according toresearch firm MarketsandMarkets.

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    7. The Analytics of Language, Behavior, and Personality

      NLP touches our daily lives, in many ways. Voice response and personal assistants — Siri, Google Now, Microsoft Cortana, Amazon Alexa — rely on NLP to interpret requests and formulate appropriate responses. Search and recommendation engines apply NLP, as do applications ranging from pharmaceutical drug discovery to national security counter-terrorism systems. NLP, part of text and speech analytics solutions, is widely applied for market research, consumer insights, and customer experience management. 

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    8. How big data is changing the game for backup and recovery

      It's a well-known fact in the IT world: Change one part of the software stack, and there's a good chance you'll have to change another. For a shining example, look no further than big data. First, big data shook up the database arena, ushering in a new class of "scale out" technologies. Now there's a new sticking point: backup and recovery. Vendors of more traditional backup products are gradually adjusting their own technologies for big data.

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    9. Your Algorithmic Self Meets Super-Intelligent AI

      As humanity debates the threats and opportunities of advanced artificial intelligence, we are simultaneously enabling that technology through the increasing use of personalization that is understanding and anticipating our needs through sophisticated machine learning solutions. In effect, while using personalization technologies in our everyday lives, we are contributing in a real way to the development of the intelligent systems we purport to fear. Perhaps uncovering the currently inaccessible personalization systems is crucial for creating a sustainable relationship between humans and super–intelligent machines?

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    10. Cisco platform lets IT rein-in disruptive data center operations, security, applications

      Two years in the making, Cisco today rolled out a turnkey, full-rack appliance that promises to do just about everything it takes to control a data center -- from easing IT operations and controlling security to application monitoring.The platform, Cisco Tetration Analytics gathers information from hardware and software sensors and analyzes the information using big data analytics and machine learning to offer IT managers a deeper understanding of their data center resources.

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    11. Leveraging the power of scalable machine learning

      In today’s digitally driven world, enterprises need to find ways to extract valuable insights from huge amounts of data. To make sense of these data assets, enterprises need automated tools that continually analyze data and generate information and insights that business leaders can use to keep the organization competitive. That’s the idea behind machine learning. Scalable machine learning, also known as distributed machine learning, refers to algorithms and infrastructure that scale out to capture insights from huge amounts of data.

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    12. Preparing for an Influx of Health Care Data

      30 percent of all electronic data storage is health care data, and it is expected to grow by a factor of at least 20 by the end of the decade. Electronic health record data, such as diagnostics and clinical encounter summaries, are increasingly being augmented with various sources. This combined with data from claims, quality measurement, population health and genomic data from precision medicine. Wearables, personalized health data storage (which may be a card, not a portal) and smart phones will generate vast amounts of biometric data. 

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    13. Microsoft bets on Apache Spark to power its big data and analytics services

      Microsoft today announced that it is making a serious commitment to the open source Apache Spark cluster computing framework. After dipping its toes into the Spark ecosystem last year, the company today launched a number of Spark-based services out of preview and announced that the on-premises version of R Server for Hadoop (which uses the increasingly popular open source R language for big data analytics and modeling) is now powered by Spark.

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    14. Artificial intelligence is changing SEO faster than you think

      By now everyone has heard of Google’s RankBrain, the new artificial intelligence machine learning algorithm that is supposed to be the latest and greatest from Mountain View, Calif. What many of you might not realize, however, is just how fast the SEO industry is changing because of it. This article will take you through some clear examples of how some of the old rules of SEO no longer apply, and what steps you can take to stay ahead of the curve in order to continue to provide successful SEO campaigns for your businesses.

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    15. The Four Stages of the Data Maturity Model

      To extract value from that data, IT and the business must partner to create an operating model that solves specific business problems, invests wisely by employing the right technology for each phase of the data lifecycle, and delivers self-service analytics with reduced development time and costs. To get there, you need to know where you’ve been and where you are in your data maturity. Dell created a data maturity model to chart our own data maturity and help our customers track theirs. 

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    16. Big data and analytics spending to hit $187 billion

      Worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to $187 billion in 2019, according to the new Worldwide Semiannual Big Data and Analytics Spending Guide from IDC. Services represent the biggest opportunity. They will account for more than half of all big data and business analytics revenue for most of the forecast period. Within the category, IT Services will generate more than three times the annual revenues of Business Services.

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    17. The Elephant In The Factory

      No longer simply the domain of Web properties and other Silicon Valley startups, Apache™ Hadoop® and related big data systems are being adopted by companies across every industry. This open-source acceptance is challenging IT professionals to integrate these technologies into their data processing and analytics ecosystems. Open-source technologies have drastically changed the economics of large-scale data storage and processing in these four ways:

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    18. How storage is changing in the age of big data

      Data is growing exponentially — with no end in sight. As more devices continue to connect to the Internet of Things, sensors on everything from automobiles to appliances increase the data output even more. By 2020, IDC predicts that the amount of data will increase by a thousandfold, reaching a staggering 44 zettabytes of data. The only logical response to this data deluge is to create more ways to store and maximize all this information.

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    19. 5 Things You Need To Know About Data Exhaust

      Big data is now a familiar term in most of the business world, and companies large and small are scrambling to take advantage of it. Data exhaust, on the other hand, it is less widely known, and in some ways it's an evil twin brother. It is essentially all the big data that isn't core to your business which can be enormously useful. However, the bottom line is that it's critical to be selective about what data exhaust gets saved, to avoid the risks associated with it. 

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    20. Business alignment techniques for successful and sustainable analytics

      How to explore business alignment techniques by analyzing business strategy, decomposing strategy into how data will be used to help meet strategic goals, decomposing data usage into critical components and understanding patterns of use. This article explores business alignment techniques, such as Business Information Requirements BIRSM or “Line of Sight,” that support these capabilities and further secure a foundation for the success and sustainability of the analytics effort.

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    21. Igniting Faster Analytics With the SMACK Stack

      Enterprises are getting better at using the Hadoop platform for batch processing in analytics applications. This capability is helping business leaders gain rear-view insights into business trends, which is good, however, not good enough. To compete effectively in a world that is increasingly driven by big data, enterprises now need to get better at using data in real-time analytics applications to understand what is likely to happen in the days ahead. SMACK is a big data and analytics toolchain which brings together key open source technologies that work together to accelerate the data pipeline—from processing to analysis

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    22. Rome Wasn't Built In A Day, Why Should Your Analytics?

      The biggest question facing organisations yet to embark upon any serious analytics activity is where to start. Choosing to do nothing is not an option when two thirds of respondents to a recent survey report that big data and analytics initiatives had a significant, measurable impact on their revenues. Armed with the right, detailed data, a company can define and prioritise the parts of the business or the types of challenges that data and analytics can have the largest impact.

       

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    23. The Evolution of Data Processing Frameworks and Using the Right Tool for the Right Job

      The common tools of the past included Relational Database Management Systems (RDBMS) commanded by using Structured Query Language (SQL), and these are still very much in-use today where they still fit. Over the last decade we’ve seen the rise of “Big Data” frameworks such as Hadoop, Cassandra, and MongoDB bring greater levels of speed, capacity, and efficiency to this process or sorting through larger volumes of data. The next few years should give way to even more new tool combinations used in concert to form a fully modern data pipeline leveraging the best of both batch and real-time analytics. 

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