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COG-500 - IBM Cognos 8 Analytic Applications(R) Developer - Dump Information

Vendor : IBM
Exam Code : COG-500
Exam Name : IBM Cognos 8 Analytic Applications(R) Developer
Questions and Answers : 60 Q & A
Updated On : October 15, 2018
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COG-500 Questions and Answers

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COG-500 IBM Cognos 8 Analytic Applications(R) Developer

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com COG-500 Dumps and Real Questions

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COG-500 exam Dumps Source : IBM Cognos 8 Analytic Applications(R) Developer

Test Code : COG-500
Test Name : IBM Cognos 8 Analytic Applications(R) Developer
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM IBM Cognos 8 Analytic

IBM pushes automation, NLP in Cognos Analytics 11.1 update | killexams.com Real Questions and Pass4sure dumps

IBM has delivered a number of AI-powered tools to its BI platform, Cognos Analytics, to permit greater superior analytics, whereas at the identical time making the platform easier for consumers to use, the company referred to.

the new Cognos Analytics 11.1 platform aspects encompass an AI assistant, pattern detection, visualization concepts, superior storytelling equipment, and extra superior analytics and predictive analytics capabilities.

The replace, published in a Sept. 27 IBM weblog submit, represents a "huge investment," stated Greg Adams, vice president of IBM company Analytics, in an interview. the new tools, beyond including greater advanced capabilities, also make the platform simpler to make use of for nondata-oriented personnel and citizen records scientists -- a category of users that has been transforming into rapidly.

Following a style

the brand new elements in Cognos Analytics 11.1 make "the platform a good deal easier to make use of to generate insights, in particular the herbal language search, which enables users to have true conversations with the information," said Wayne Eckerson, founder and foremost consultant at Eckerson group.

IBM is [in the] center of the pack, which is a tad marvelous, given their Watson heritage. Wayne Eckerson, Eckerson community

"It automates statistics instruction and statistics modeling initiatives, so analysts can focus extra on insights. It additionally auto-generates insights in accordance with a box of hobby chosen by using a person, using algorithms to tease out key drivers of a metric, as an example," he continued.

nonetheless, Eckerson cited, these are features that "all the BI companies are racing to comprise."

"Some are a bit bit ahead of others, however most will emerge as within the identical location in 18 months. IBM is [in the] middle of the pack, which is a tad magnificent, given their Watson heritage," he said.

earlier this yr, BI utility supplier Alteryx Inc. released Alteryx 2018.3, which introduced more desirable visualization capabilities and set the stage for future updates that are spoke of to raise the platform's augmented intelligence and wise capabilities.

Tableau also made a move so as to add greater automation to its systems this yr with the acquisition of MIT startup Empirical methods. The startup labored on a platform to immediately model data for evaluation, and it offers predictive insights into that information -- facets that Tableau cited it will work so as to add into its personal structures.

Cognos Analytics 11.1 Cognos Analytics 11.1 allows for short comparisons of two visualizations in a brand new exploration device. more straightforward to make use of

in line with Adams, at IBM, "we know to really assist people locate insights, to truly be self-carrier ability individuals need to be immediately given insights, instantly have issues brought to their consideration, and we must have issues be definitely approachable."

That approach is mirrored in the new AI Assistant. Taking abilities of recent herbal language processing (NLP) capabilities, the assistant in Cognos Analytics eleven.1 permits clients to type in questions and instructions, comparable to a command to be a part of information sets.

"the important thing with the conversation technology, or not it's not anything that we wanted to bolt onto the aspect," Adams mentioned. "We definitely tried to inject it, so or not it's a part of your commonplace move."

advanced analytics

the new AI applied sciences in Cognos Analytics 11.1 work neatly with the newly brought advanced analytics tools, Adams talked about.

The platform now has sample detection, that can immediately find and existing relationships in facts it really is being explored.

a definite visualization, for example, the platform will immediately find linked visualizations that may be used, Adams spoke of. When opting for statistics, the equipment will additionally provide techniques for visualizations.

With a new exploration device in Cognos Analytics 11.1, users can drag and drop two visualizations for a "brief side-by using-aspect assessment" which will automatically search for relationships, Adams said.

The update also contains more advantageous predictive analytics capabilities; greater data storytelling alternatives, with the capability to create interactive narratives with sequenced visualizations and media; and simpler-to-use reporting equipment.

environment aside and searching forward

while Adams acknowledged other BI structures have currently incorporated equivalent facets, he claimed IBM's consideration to statistics governance sets Cognos Analytics eleven.1 aside.

"some of the fine issues about Cognos is its commercial enterprise-grade, relied on ambiance," he observed, adding that IBM has also added a system to instantly observe AI bias.

in addition to the replace, IBM has released a brand new subscription pricing mannequin for the platform. The model sets the platform into three tiers: lite, top class and enterprise. The Cognos Analytics eleven.1 replace is accessible for the maximum tier as of Sept. 28, and it'll be purchasable on the other tiers through the conclusion of the yr, in response to IBM.


IBM integrates company intelligence and facts science with new main update to Cognos Analytics | killexams.com Real Questions and Pass4sure dumps

greater firms are on a quest to use synthetic intelligence as a competitive skills. IBM is on a mission to make this more convenient and more attainable for any company by the use of the cloud. The enterprise has introduced a brand new version of its Cognos Analytics solution, which integrates enterprise intelligence and data science technologies into a suite of equipment attainable on IBM Cloud.

the important thing new facets of this free up are a new AI Assistant and sample detection ability. The AI Assistant makes it possible for users to make queries after which obtain effects in natural language. according to IBM, this makes it less difficult to not best look for answers, however consider where they come in.

pattern detection identifies and gifts critical relationships within records, pointing out patterns that might also now not have been regarded up to now. through detecting patterns, the system can get rid of blind spots or guesswork in analysis, the enterprise explained.

“When combined with the foundational governance and have faith capabilities constructed into Cognos Analytics, which give facts policy, relevancy, integrity and security, AI Assistant and sample detection will provide clients not most effective quickly access to insights, but delivered self belief within the outcomes,” Greg Adams, vice president of construction for business analytics at IBM, wrote in a post.

This version also provides storytelling, which allows for clients to create interactive narratives through assembling visualizations into a sequence after which enhancing it with media, web pages, photographs, shapes, and test.

wise exploration will assist clients be in a position to stronger be aware what’s in the back of their effects through examining it with computer getting to know and sample detection. The device will also recommend essentially the most helpful visualization of effects through a multi-stage recommendation system.

The gadget assembles new content right away by way of re-the use of crucial components from current dashboards.

finally, it has advanced analytics that consist of predictive analytics, the capability to identify facts patterns and variables using a undeniable influence, wise annotation, and herbal language generated insights of records.

“Analytics, records science and business intelligence, have develop into table stakes in the company world,” wrote Adams. “They’re part of the fundamental constructing blocks, or rungs within the Ladder to AI, as we discuss. And we’ve labored tough to make these refined, complex applied sciences handy to entry, convenient to use, and simple to understand for virtually any – all by way of the IBM Cloud. We feel groups internationally will see actual value in it and begin making even enhanced, more strategic selections to propel their businesses into the long run.”


Cognos and IBM prolong Strategic Alliance to convey On Demand group of workers performance administration | killexams.com Real Questions and Pass4sure dumps

ARMONK, N.Y. & BURLINGTON, Mass.--(company WIRE)--Cognos (Nasdaq: COGN; TSX: CSN), the area leader in company intelligence and efficiency administration options, and IBM (NYSE: IBM), these days announced both groups have extended their global strategic alliance to deliver a staff performance administration solution through IBM’s world-main Human elements company Transformation Outsourcing (BTO) carrier.

under phrases of the agreement, IBM has selected Cognos 8 staff efficiency as a typical analytic and reporting solution to be delivered inside its full-provider Human Capital administration (HCM) outsourcing options portfolio. The addition of Cognos 8 staff efficiency extends IBM services to its valued clientele by teaming their HCM capabilities with useful analytic perception into group of workers trends and influencing factors inside the talent administration cycle. This comprises visibility into key efficiency areas such as group of workers power, worker turnover, compensation and incentives, talent segmentation and performance ratings.

IBM chosen Cognos’ next-technology analytic application as a result of the answer’s packaged staff content material, and its potential to right away adapt to a wide range of client and company necessities. The Cognos eight body of workers performance solution is a configurable analytic software designed to reflect a corporation’s human substances premier practices, leveraging a prosperous library of pre-packaged studies and analytics on a wide range of key staff metrics, all of which can be effectively modified or extended with the evolving assistance needs of the enterprise.

via this alliance, IBM BTO shoppers will improvement from an efficient outsourced HR features mannequin that can allow the firm to proactively identify and prioritize headcount needs, optimize and align staff investments to corporate desires, enrich worker job delight, nurture profession increase, and reduce turnover.

“IBM prides itself in delivering options and functions that release business cost, cut back chance via technical and system improvements, and admire tangible software cost savings,” said Steve Kagan, widespread manager, IBM Human substances BTO. “by way of leveraging Cognos as an embedded body of workers performance reporting and evaluation engine, IBM is surroundings a brand new HR outsourcing standard that promises improved workforce efficiency visibility that can evolve as our consumers increase the attributes of a very on demand enterprise.”

Underpinning the Cognos utility’s flexibility is an open Adaptive software Framework that raises enterprise agility whereas also decreasing costs and the over-reliance on scarce IT elements. based on the open, up to date services-oriented architecture found in Cognos eight enterprise Intelligence, Cognos 8 workforce performance allows organizations to effortlessly extract and transform transactional body of workers records into consistent, reliable and primary administration information, assisting to reduce blunders and reduce reporting cycles. Cognos 8 group of workers efficiency is controlled and automatic via a single, mannequin-driven configuration interface, which leverages underlying engines obligatory to transform the source information, populate the meta data repository and generate all reports and evaluation that are fully customized to the corporation’s certain needs.

“IBM and Cognos share a common vision for featuring comparatively cheap solutions that allow businesses to extra conveniently deal with large human components considerations such because the graying group of workers, impending capabilities gap, and extended market competition for talent,” stated Doug Barton, vice president of product advertising at Cognos. “Cognos is proud to were selected by IBM as a body of workers performance partner in assisting deliver a better and extra strategic HR feature for IBM BTO valued clientele international.”

In March 2006, Cognos and IBM introduced a world strategic alliance to support purchasers integrate their business strategies throughout the enterprise and profit better insight into business efficiency. The alliance settlement included joint construction, advertising, and revenue of SOA-based solutions to help customers optimize their analytics capabilities leveraging trade-selected optimum practices. For extra advice, please see http://www.cognos.com/news/releases/2006/0307.html.

ABOUT COGNOS:

Cognos, the area leader in company intelligence and performance management options, gives world-type commercial enterprise planning and BI software and capabilities to assist agencies plan, be aware and control monetary and operational efficiency.

Cognos brings together know-how, analytical functions, most reliable practices, and a extensive community of partners to provide valued clientele an entire efficiency device. The Cognos performance gadget is an open and adaptive solution that leverages an organization’s ERP, packaged applications, and database investments. It offers purchasers the means to reply the questions -- How are we doing? Why are we on or off track? What may still we do about it? – and allows for them to be mindful and computer screen existing efficiency while planning future company recommendations.

Cognos serves more than 23,000 purchasers in additional than a hundred thirty five nations, and its excellent one hundred enterprise shoppers always outperform market indexes. Cognos efficiency management solutions and services are also attainable from greater than three,000 worldwide partners and resellers. For greater assistance, talk over with the Cognos internet web site at http://www.cognos.com.

ABOUT IBM

For more tips about IBM, visit www.ibm.com.

Cognos and the Cognos emblem are emblems or registered trademarks of Cognos incorporated within the u.s. and/or other nations. All other names are logos or registered logos of their respective organizations.

note to Editors: Copies of old Cognos press releases and corporate and product information are available on the Cognos web website at www.cognos.com.


COG-500 IBM Cognos 8 Analytic Applications(R) Developer

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com COG-500 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



COG-500 exam Dumps Source : IBM Cognos 8 Analytic Applications(R) Developer

Test Code : COG-500
Test Name : IBM Cognos 8 Analytic Applications(R) Developer
Vendor Name : IBM
Q&A : 60 Real Questions

Shortest question are included in COG-500 question bank.
Ive visible severa subjects publicized adage utilize this and rating the exceptional however your gadgets were completely incredible as contrasted with others. Im able to cross back speedy to buy greater examine aids. I really wanted to mention a debt of gratitude is so as regarding your amazing COG-500 have a look at guide. I took the exam this week and finished soundly. Not anything had taught me the mind the manner killexams.Com Questions & solutions did. I solved 90 5% questions.


Worked hard on COG-500 books, but everything was in this study guide.
I surpassed the COG-500 examination thanks to this package deal. The questions are correct, and so are the topics and take a look at guides. The layout is very convenient and allows you to have a look at in distinctive formats - training on the trying out engine, reading PDFs and printouts, so you can training session the fashion and balance thats right for you. I individually cherished training on the checking out engine. It completely simulates the exam, that is specifically critical for COG-500 examination, with all their particular question sorts. So, its a bendy but dependable manner to reap your COG-500 certification. ill be using killexams.com for my subsequent stage certification exams, too.


simply attempted COG-500 query financial institution as soon as and i'm convinced.
hi there there fellows, simply to inform you that I surpassed COG-500 exam a day or two ago with 88% marks. sure, the examination is hard and killexams.com Q&A and examination Simulator does make lifestyles less difficult - a amazing deal! I think this unit is the unrivaled motive I surpassed the exam. As a matter of first importance, their exam simulator is a present. I typically loved the inquiry and-answer agency and exams of various types in light of the truth that that is the maximum best approach to study.


Dont forget about approximately to strive those actual exam questions questions for COG-500 examination.
Overall impression was very good but i failed in one assignment but succeeded in COG-500 second assignment with killexams.com team very fast. exam simulator is good.


Is there any way to pass COG-500 exam at first attempt?
The material was generally organized and efficient. I could without much of a stretch remember numerous answers and score a 97% marks after a 2-week readiness. Much thanks to you folks for decent arrangement materials and helping me in passing the COG-500 exam. As a working mother, I had constrained time to make my-self get ready for the exam COG-500. Thusly, I was searching for some exact materials and the killexams.com dumps aide was the right decision.


Is there COG-500 examination new sayllabus to be had?
Your question financial institution is want of the hour. Ive were given 89.1% inside the COG-500 examination. Super needs to your specialists. Thanks group. So extremely joyful to easy this examination. Your look at fabric was extraordinarily useful, clear, consise, protecting whole material and suberb stacking of questions to make one strong guidance. Thank you over again to you and your crew.


Shortest questions that works in real test environment.
preparing for COG-500 books can be a complicated process and 9 out of ten probabilities are that you may fail if you do it without any appropriate guidance. Thats in which first-class COG-500 ebook comes in! It affords you with green and groovy records that not only enhances your training but also gives you a clean reduce threat of passing your COG-500 download and moving into any university with none depression. I organized via this terrific software and that i scored forty two marks out of 50. i can assure you that itll by no means assist you to down!


what's easiest way to skip COG-500 exam?
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Augmented analytics, automated tools facilitate data analysis | killexams.com real questions and Pass4sure dumps

Augmented analytics sounds futuristic -- and BI and advanced analytics vendors are increasingly moving to make it a here-and-now reality, along with automated machine learning, search-based querying and other technologies that have a common goal to simplify and streamline the analytics process for users.

In particular, augmented analytics and its cohorts are designed to make it easier for business users and the growing population of so-called citizen data scientists -- business analysts and other workers who lack formal data science training but are building predictive models themselves -- to drill down into data sets and gain valuable insights from BI and analytics applications.

These emerging technologies do arrive at the same point, but they come at it from different directions. Augmented analytics -- or augmented intelligence, as some vendors characterize it -- embeds machine learning algorithms and other advanced analytics functionality into BI tools to point users toward relevant data, help them prepare it for analysis and aid in creating data visualizations. On the other hand, automated machine learning platforms provide under-the-hood functionality to help analysts with limited expertise build and train machine learning models for more-advanced data mining and predictive analytics uses.

But while the simplification push encompasses a broad set of tools and approaches, it all boils down to enabling users -- especially less tech-savvy ones -- to uncover information faster than before. These technologies also pave the way for more people to get involved in analytics initiatives and can be "a big productivity booster" for organizations, TDWI analyst Fern Halper wrote in a September 2017 report on advanced analytics trends.

According to Gartner, augmented analytics includes a combination of natural language processing (NLP), augmented data preparation, automated analytics and visual-based data discovery. As a whole, those capabilities have yet to become a mainstream factor in software buying decisions, Gartner said in its 2018 "Magic Quadrant" report on BI and analytics platforms.

But the consulting and market research firm predicted that augmented analytics, by 2020, "will be a dominant driver of new purchases" of BI tools, embedded analytics software, and data science and machine learning platforms. And by then, Gartner expects 50% of analytical queries to be automatically generated or created through searches, NLP applications or voice commands instead of conventional coding.

Longer term, Gartner sees technology-assisted analytics becoming even more prevalent. "Ten years from now, probably, you won't find a single business application that isn't enabled in some way by machine learning and AI, and analytics is no different," Gartner analyst Rita Sallam said in a recent webinar on augmented analytics.

Some users are already on board. In a TDWI survey conducted in June 2017, 27% of 222 respondents said their organizations were using analytics tools that suggest data visualizations to users. And in another survey done by TDWI in January 2018, 16% of 387 respondents said their organizations had deployed automated predictive modeling software, while an additional 40% said they expected to start using it in the next few years.

Giving machine learning the gas

Chevron Corp. is an early adopter of Google's Cloud AutoML technology, which is designed to help users with limited machine learning expertise build and train analytical models. Earlier this year, a seismic processing and imaging team at Chevron used the alpha version of an AutoML Vision image analysis tool to help analyze internal documents as part of the process of evaluating new opportunities for oil drilling, according to Laura Bandura, a research geophysicist who works on the team.

Bandura, speaking during a keynote session at July's Google Cloud Next '18 conference in San Francisco, said Chevron has amassed millions of documents related to oil exploration in different areas -- far too many to search manually. As a result, finding relevant information about an area that's being assessed for possible drilling "can be a needle-in-a-haystack type of challenge," she explained.

To pinpoint the documents that might have useful geological info about locations in the Gulf of Mexico, the Chevron team first did a search on the term geologic map to find documents with embedded map images. It next ran an analytical model that was built with AutoML Vision and trained to recognize more than 60 geologic labels against the map images, then used Google's pre-trained Cloud Natural Language API to classify the data found by the model and make the information searchable.

Trends in business intelligence

Thanks to the use of AutoML, Bandura said that she and other Chevron explorationists, as they're called in oil industry parlance, can now find pertinent documents about the geology of the gulf "in seconds instead of weeks." That enables more-timely decisions on possible drilling spots, without involving data scientists in the model development and training process, she added.

AutoML Vision became available as an alpha technology last January and was released in a beta version in July along with companion products for text analysis and language translation. Also in July, Google launched a beta version of BigQuery ML, which lets users build machine learning models inside the company's BigQuery cloud data warehouse with standard SQL commands instead of using more-advanced languages like R, Python and Scala.

A wider analytics embrace

Other top BI and analytics vendors are also embracing augmented analytics and other forms of automation. Tableau Software, for example, offers a recommendation engine that suggests relevant data sources to users of its self-service BI and data visualization software. It also built AI-based fuzzy clustering algorithms that help users group together related data sets into the Tableau Prep data preparation tool released in April.

In June, Tableau acquired AI startup Empirical Systems, developer of an analytics engine that automatically models data for analysis and identifies trends and outliers in the data. It plans to integrate the Empirical engine into its BI software in the hope of providing users with better insights into their data sets.

Tableau's chief product officer, Francois Ajenstat, said the company is also looking to use NLP technology so business users can intuitively ask questions about data to be analyzed without seeking help from a data scientist. Ajenstat wouldn't specify when that capability will be available, saying only that Tableau hopes to incorporate it "soon."

Qlik, one of Tableau's top self-service BI rivals, is likewise working to incorporate augmented analytics capabilities into its Qlik Sense software. Updates to Qlik Sense earlier this year included the addition of a feature that recommends chart designs based on the data fields selected by analysts and an Insight Advisor tool that can direct users to data that fits analytics applications they want to run.

Not to be outdone, Tibco Software is adding the ability to use NLP queries to navigate through data sets and get AI-driven visualization recommendations to its Spotfire self-service BI software. Those features, which join an existing AI function that automatically identifies relationships between data elements, are part of a new Spotfire X release announced last month and due out sometime in the fall.

Augmenting AI's cache

Other tools that offer augmented analytics capabilities include Salesforce Einstein and IBM Watson Analytics. In addition, IBM last month detailed an update to its traditional BI platform, Cognos Analytics, that includes natural language querying, automatic pattern detection and visualization recommendation features. Among other BI vendors adopting the concept, Sisense Inc. is using NLP technology to power a free chatbot application that lets users upload structured data, which the tool analyzes for basic pattern and anomaly detection and other insights (see "Say hello to your friendly little chatbot data analyst").

Augmented and automated analytics technologies offer a path for making AI more prevalent -- and more useful -- in user organizations, according to Guy Levy-Yurista, vice president of strategic growth and innovation at Sisense. "People all get excited about AI, but very few people know what it can do," he said. "Analytics should be embedded in your day-to-day life, and you should be consuming analytics without really thinking about it."

In addition to Google, automated machine learning platforms are available from vendors such as DataRobot and H2O.ai. Microsoft also added some automated functionality to its cloud-based Azure Machine Learning platform last month, enabling the software to identify algorithms that will run applications efficiently and optimize the performance of analytical models for users.

And there are plenty of startups working on augmented tools, like Ople.ai, which offers a platform that can be used to build AI models for accelerating data science applications, as well as the many companies that are part of the MIT Startup Exchange -- about 70 of which are working on AI projects. One such startup is Feature Labs; its software helps automate feature engineering by using automated techniques to turn raw data into machine learning algorithms.

Software that automates advanced analytics processes "allows people with very little data science training to become data scientists," said Marcus Dahllöf, program director at the MIT Startup Exchange. Hence, the purpose of augmented analytics: Workers who aren't skilled data scientists can at least act like they are.

Senior Executive Editor Craig Stedman contributed to this story.


Edge computing: The state of the next IT transformation | killexams.com real questions and Pass4sure dumps

edge-computing-intro-header.jpg Image: metamorworks, Getty Images/iStockphoto

In recent years, computing workloads have been migrating: first from on-premises data centres to the cloud and now, increasingly, from cloud data centres to 'edge' locations where they are nearer the source of the data being processed. The goal? To boost the performance and reliability of apps and services, and reduce cost of running them, by shortening the distance data has to travel, thereby mitigating bandwidth and latency issues.

That's not to say that on-premises or cloud centres are dead -- some data will always need to be stored and processed in centralised locations. But digital infrastructures are certainly changing. According to Gartner, for example, 80 percent of enterprises will have shut down their traditional data centre by 2025, versus 10 percent in 2018. Workload placement, which is driven by a variety of business needs, is the key driver of this infrastructure evolution, says the analyst firm:

With the recent increase in business-driven IT initiatives, often outside of the traditional IT budget, there has been a rapid growth in implementations of IoT solutions, edge compute environments and 'non-traditional' IT. There has also been an increased focus on customer experience with outward-facing applications, and on the direct impact of poor customer experience on corporate reputation. This outward focus is causing many organizations to rethink placement of certain applications based on network latency, customer population clusters and geopolitical limitations (for example, the EU's General Data Protection Regulation [GDPR] or regulatory restrictions).

There are challenges involved in edge computing, of course -- notably centering around connectivity, which can be intermittent, or characterised by low bandwidth and/or high latency at the network edge. That poses a problem if large numbers of smart edge devices are running software -- machine learning apps, for example -- that needs to communicate with central cloud servers, or nodes in the intervening 'fog'. Solutions are on the way, however.

With edge computing sitting at the peak of Gartner's 2018 Hype Cycle for Cloud Computing, there's plenty of scope for false starts and disillusionment before standards and best practices are settled upon, and mainstream adoption can proceed. This introduction to ZDNet's special report looks to set the scene and assess the current state of play.

Definitions

Edge computing is a relatively new concept that has already been associated with another term, 'fog computing', which can lead to confusion among non-specialist observers. Here are some definitions that will hopefully clarify the situation.

Futurum ResearchUnlike Cloud Computing, which depends on data centers and communication bandwidth to process and analyze data, Edge Computing keeps processing and analysis near the edge of a network, where the data was initially collected. Edge Computing (a category of Fog Computing that focuses on processing and analysis at the network node level)...should be viewed as a de facto element of Fog Computing.

State of the Edge 2018The delivery of computing capabilities to the logical extremes of a network in order to improve the performance, operating cost and reliability of applications and services. By shortening the distance between devices and the cloud resources that serve them, and also reducing network hops, edge computing mitigates the latency and bandwidth constraints of today's Internet, ushering in new classes of applications. In practical terms, this means distributing new resources and software stacks along the path between today's centralized data centers and the increasingly large number of devices in the field, concentrated, in particular, but not exclusively, in close proximity to the last mile network, on both the infrastructure and device sides.

451 Research/OpenFog Consortium[Fog] begins on one 'end' with edge devices (in this context, we define edge devices as those devices where sensor data originates, such as vehicles, manufacturing equipment and 'smart' medical devices) that have the requisite compute hardware, operating system, application software and connectivity to participate in the distributed analytics Fog. It extends from the edge to 'near edge' functions, such as local datacenters and other compute assets, multi-access-edge (MEC) capabilities within an enterprise or operator radio access network, intermediate computing and storage capabilities within hosting service providers, interconnects and colocation facilities, and ultimately to cloud service providers. These locations have integrated or host 'Fog nodes', which are devices capable of participating in the overall distributed analytics system.

David Linthicum (Chief Cloud Strategy Officer at Deloitte Consulting)"With edge, compute and storage systems reside at the edge as well, as close as possible to the component, device, application or human that produces the data being processed. The purpose is to remove processing latency, because the data needn't be sent from the edge of the network to a central processing system, then back to the edge...Fog computing, a term created by Cisco, also refers to extending computing to the edge of the network. Cisco introduced its fog computing in January 2014 as a way to bring cloud computing capabilities to the edge of the network...In essence, fog is the standard, and edge is the concept. Fog enables repeatable structure in the edge computing concept, so enterprises can push compute out of centralized systems or clouds for better and more scalable performance."

Here's how the OpenFog Consortium visualises the relationship between data-generating 'things' at the network edge, cloud data centres at the core, and the fog infrastructure in between:

openfogdiagram.png

openfogdiagram.png

Image: OpenFog Consortium Market estimates

According to B2B analysts MarketsandMarkets, the edge computing market will be worth $6.72 billion by 2022, up from an estimated $1.47bn in 2017 -- a CAGR (Compound Annual Growth Rate) of 35.4 percent. Key driving factors are the advent of the IoT and 5G networks, an increase in the number of 'intelligent' applications, and growing load on cloud infrastructure:

Edge computing market dynamics

Drivers
  • Growing load on cloud infrastructure
  • Wide range of applications in different industries
  • Increase in the number of intelligent applications
  • Restraints
  • Security and privacy concerns related to edge computing
  • Opportunities Challenges
  • Inadequate industry standards and interoperability issues
  • [Data: MarketsandMarkets]

    Among the vertical segments considered by MarketsandMarkets, Telecom and IT is expected to have the biggest market share during the 2017-2022 forecast period. That's because enterprises faced with high network load and increasing demand for bandwidth will need to optimise and extend their Radio Access Network (RAN) in order to deliver an efficient Mobile (or Multi-access) Edge Computing (MEC) environment for their apps and services.

    The fastest-growing segment of the edge computing market during the forecast period is likely to be retail, says MarketsandMarkets: high volumes of data generated by IoT sensors, cameras and beacons that feed into smart applications will be more efficiently collected, stored and processed at the network edge, rather than in the cloud or an on-premises data centre.

    Grand View Research takes a more conservative view, estimating that the edge computing market will be worth $3.24 billion by 2025, although that's still a 'phenomenal' CAGR of 41 percent over the 2017-2025 forecast period. Regionally, North America will lead the market due to increasing penetration of IoT devices in the US and Canada, said the research firm, while the vertical segment with the highest CAGR will be healthcare and life sciences, thanks to "storage capabilities and real-time computing offered by edge computing solutions". SMEs will witness the highest CAGR (46.5%) over the forecast period, said Grand View Research, thanks to the ability of edge computing solutions to reduce operating costs.

    The most optimistic growth estimate comes from 451 Research, in an October 2017 study -- Size and Impact of Fog Computing Market -- commissioned by the OpenFog Consortium. This wide-ranging research puts the market opportunity for fog computing at $18.2 billion by 2022, up from $1.03bn in 2018 and $3.7bn in 2019 -- a CAGR of 104.9 percent between 2018 and 2022.

    451-openfogmarket-forecast-1.png

    451-openfogmarket-forecast-1.png

    Data: 451 Research & OpenFog Consortium / Chart: ZDNet

    According to 451 Research, the leading verticals for fog computing in 2022, in terms of market share, will be utilities, transportation, healthcare, industrial and agriculture.

    451-openfogmarket-forecast-22.png

    451-openfogmarket-forecast-22.png

    Image: 451 Research & OpenFog Consortium

    When it comes to the fog computing ecosystem in 2022, 451 Research breaks down the components like this:

    451-openfogmarket-forecast-3.png

    451-openfogmarket-forecast-3.png

    Data: 451 Research & OpenFog Consortium / Chart: ZDNet

    Hardware components are well out in front, with a 42.1 percent slice of the 2022 pie, followed by fog applications/platforms (21.5%) and fog services (20.4%). No wonder hardware vendors and cloud application/services providers are queueing up to get involved in the fast-developing edge/fog market.

    Despite their different emphases, these forecasts make it clear that the 'perfect storm' for edge computing is being created by a rapidly increasing number of internet-connected devices and the imminent advent of high-bandwidth, low-latency 5G networks. Ericsson's June 2018 Mobility Report summarises the expected developments in these areas.

    Whereas PCs, laptops, tablets and (to a lesser extent) mobile phones show flat growth between 2017 and 2023, IoT devices are taking off: those with wide-area connections will see 30 percent CAGR, with short-range IoT devices showing significant but slower growth (17% CAGR). This results in an almost 80 percent (79.4%) increase in the number of connected devices between 2017 (17.5 billion) and 2023 (31.4 billion):

    ericsson-connected-devices.png

    ericsson-connected-devices.png

    * Cellular IoT devices are a subset of Wide-area IoT devices.

    Data: Ericsson Mobility Report, June 2018 / Chart: ZDNet

    As far as 5G is concerned, Ericsson expects the first data-only devices from the second half of 2018 and the first 5G smartphones in 2019. By 2023, following the advent of third-generation chipsets in 2020, the company forecasts that 1 billion 5G devices will be connected worldwide.

    ericsson-5g-devices.png

    ericsson-5g-devices.png

    CPE/FWT: Customer-Provided Equipment/Fixed Wireless Terminal

    Image: Ericsson Mobility Report, June 2018

    The first module-based 5G IoT devices, supporting ultra-low latency communications for industrial process monitoring and control, are expected during 2020, says Ericsson.

    Standards & organisations

    Any new IT initiative requires standards and best practices, and the early stages are often characterised by multiple groups and consortia with different agendas (despite often significant overlap in membership). Edge/fog computing is no exception.

    Fog computing, a term coined by Cisco, is backed by the OpenFog Consortium, which was founded in 2015 by Arm, Cisco, Dell, Intel, Microsoft and the Princeton University Edge Laboratory. Its mission statement reads (in part):

    Our efforts will define an architecture of distributed computing, network, storage, control and resources that will support intelligence at the edge of IoT, including autonomous and self-aware machines, things, devices, and smart objects. OpenFog members will also identify and develop new operational models. Ultimately, our work will help to enable and drive the next generation of IoT.

    Edge computing is promoted by the EdgeX Foundry, an open-source project hosted by The Linux Foundation. EdgeX Foundry's goals include: building and promoting EdgeX as a common platform unifying IoT edge computing; certifying EdgeX components to ensure interoperability and compatibility; providing tools to quickly create EdgeX-based IoT edge solutions; and collaborating with relevant open-source projects, standards groups and industry alliances.

    According to EdgeX Foundry, "The project's sweet spot is edge nodes such as embedded PCs, hubs, gateways, routers, and on-premises servers to address key interoperability challenges where 'south meets north, east, and west' in a distributed IoT fog architecture".

    EdgeX Foundry's technical steering committee includes representatives from IOTech, ADI, Mainflux, Dell, The Linux Foundation, Samsung Electronics, VMWare and Canonical.

    There are two other industry bodies in this area: the Japan-focused EdgeCross Consortium, which was founded in November 2017 by Omron Corporation, Advantech, NEC, IBM Japan, Oracle Japan and Mitsubishi Electric; and the Industrial Internet Consortium, founded in 2014 by AT&T, Cisco, General Electric, Intel, and IBM.

    What the surveys say

    Edge Computing Index: From Edge to Enterprise

    futurumreport-cover.png

    futurumreport-cover.png

    Futurum Research surveyed over 500 North American companies (ranging from 500 to 50,000 employees) in late 2017 to discover their position on edge computing -- adoption and deployment, investment intent, and more. All respondents exerted influence on edge computing investment decisions, said Futurum, with 41.8 percent being 'operational staff' and 25.6 percent at 'director, manager, team lead' level; only 8.6 percent were classed as 'executive, C-suite, owner, partner' though.

    Futurum reported that nearly three-quarters (72.7%) of companies had already implemented an edge computing strategy, or were in the process of doing so. Furthermore, almost all (93.3%) intended to invest in edge computing in the next 12 months:

    futuruminvestmentoutlookchart.png

    futuruminvestmentoutlookchart.png

    Data: Futurum Research / Chart: ZDNet

    Futurum also curates a general Digital Transformation Index, which in 2018 put 68 percent of companies in the 'leaders' and 'adopters' categories. So the fact that 72.7 percent of respondents are already investing in edge computing shows that this is a hot topic for tech-savvy businesses. However, Futurum also noted that "the eagerness of 93.3% of businesses to invest in edge computing in the next 12 months does not speak to the size of their investment".

    The positive vibes among Futurum's respondents continued when they were asked about the importance of edge computing data streams in their business processes, with 71.8 percent describing these as 'critically' (22.2%) or 'very' (49.6%) important:

    futurumimportancechart.png

    futurumimportancechart.png

    Data: Futurum Research / Chart: ZDNet

    What were the key drivers of this enthusiasm for edge computing? For Futurum's respondents, it was 'improved application performance', followed by 'real-time analytics/data streaming':

    futurumuse-caseschart.png

    futurumuse-caseschart.png

    Data: Futurum Research / Chart: ZDNet

    The analyst firm interpreted these priorities as a reflection of the need for operational efficiency, suggesting that the relatively low ranking for IoT strategy -- often quoted as a canonical edge computing use case -- "will likely increase in the coming years".

    Only 15.6 percent of Futurum's respondents aimed to keep edge computing and cloud computing separate -- a decision often driven by data and system security concerns, and a focus on compartmentalised operations, the research firm said. That leaves nearly 64 percent (63.9%) who had already deployed (28.3%) or were seeking (35.6%) combined edge/data centre analytics solutions, plus 20.5 percent who were unsure whether to combine these functions or keep them separate:

    futurumintegrationchart.png

    futurumintegrationchart.png

    Data: Futurum Research / Chart: ZDNet

    The 'unsure' and 'seeking' responses amount to 56.1 percent of the survey sample, which clearly represents a significant opportunity for edge computing providers.

    2018 Outlook for Fog Computing

    openfogsurvey-cover.png

    openfogsurvey-cover.png

    The OpenFog Consortium surveyed its 61 member organisations on the state of fog computing in December 2017, finding that an impressive 70 percent of CEOs were aware of the fog computing initiatives happening on their watch.

    Budgets for fog computing in 2018 were generally increasing (40%) or staying the same (51%), with just 5 percent of respondents reporting a decrease. Initiatives were primarily based in the R&D department (51%) and overwhelmingly had IoT applications as their primary focus area (70%).

    Security was the number-one concern among OpenFog respondents (32%), followed by worries about early/unproven technology, interoperability and unclear ROI. The main drivers of interest in fog computing were latency and bandwidth issues. Respondents expected manufacturing, smart cities and transportation to be the top industry segments adopting fog computing, followed by energy, healthcare and smart homes.

    Key vendors

    Edge/fog computing can pull workloads away from cloud data centres, so it's no surprise to see the cloud giants taking steps to prevent those workloads from escaping their orbit.

    AmazonIntroduced at Amazon's 2016 re:Invent developer conference, AWS Greengrass builds on the company's existing IoT and Lambda (serverless computing) offerings to extend AWS to intermittently connected edge devices.

    "With AWS Greengrass, developers can add AWS Lambda functions to a connected device right from the AWS Management Console, and the device executes the code locally so that devices can respond to events and take actions in near real-time. AWS Greengrass also includes AWS IoT messaging and synching capabilities so devices can send messages to other devices without connecting back to the cloud," said Amazon. "AWS Greengrass allows customers the flexibility to have devices rely on the cloud when it makes sense, perform tasks on their own when it makes sense, and talk to each other when it makes sense -- all in a single, seamless environment."

    aws-greengrass.png

    aws-greengrass.png

    Image: Amazon Web Services

    These are 'smart' edge devices, of course: Greengrass requires at least 1GHz of compute (either Arm or x86), 128MB of RAM, plus additional resources for OS, message throughput and AWS Lambda execution. According to Amazon, "Greengrass Core can run on devices that range from a Raspberry Pi to a server-level appliance".

    MicrosoftIntroduced at Microsoft's BUILD 2017 developer conference and generally available since June 2018, Azure IoT Edge allows cloud workloads to be containerised and run locally on smart devices ranging from a Raspberry Pi to an industrial gateway.

    Azure IoT Edge comprises three components: IoT Edge modules; the IoT Edge runtime; and IoT Hub. IoT Edge modules are containers that run Azure services, third-party services or custom code; they are deployed to IoT Edge devices and execute locally. The IoT Edge runtime runs on each IoT Edge device, managing the deployed modules, while IoT Hub is a cloud-based interface for remotely monitoring and managing IoT Edge devices.

    Here's how the different Azure IoT Edge elements fit together:

    azure-iot-edge.png

    azure-iot-edge.png

    Images: Microsoft

    With general availability, Microsoft added new capabilities to Azure IoT Edge, including: open-source support; device provisioning, security and management services; and a simplified developer experience.

    google-edge-tpu.jpg

    google-edge-tpu.jpg

    Google's Edge TPU ASIC, compared to a 1 cent coin.

    Image: Google

    GoogleIn July 2018, Google announced two products for developing and deploying smart connected devices at scale: Edge TPU and Cloud IoT Edge. Edge TPU is a purpose-built small-footprint ASIC chip designed to run TensorFlow Lite machine-learning models on edge devices. Cloud IoT Edge is the software stack that extends Google's cloud services to IoT gateways and edge devices.

    Cloud IoT Edge has three main components: a runtime for gateway-class devices (with at least one CPU) to store, translate, process and extract intelligence from edge data, while interoperating with the rest of Google's Cloud IoT platform; the Edge IoT Core runtime that securely connects edge devices to the cloud; and the Edge ML runtime, based on TensorFlow Lite, that performs machine-learning inference using pre-trained models.

    google-cloud-iot-edge.png

    google-cloud-iot-edge.png

    Image: Google

    Both Edge TPU and Cloud IoT Edge are at the alpha testing stage at the time of writing (September 2018).

    Outlook

    The edge/fog computing transformation is one of those shifts in focus that happen periodically in computing -- from mainframes to desktop PCs, to on-premises data centres, to cloud data centres, for example. Now we're looking at a mix of existing elements, along with billions of smart IoT devices, bound together by an intervening 'fog' of gateways and nodes. Device connectivity has been a bottleneck holding back this transformation, but that's about to get a huge boost with the advent of 5G mobile networks.

    Any industry sector that can derive benefit from the timely analysis of IoT data streams -- and that's pretty much all of them -- will be interested in edge/fog computing. That's why there are huge opportunities for vendors at all levels of the technology stack -- standards, networking, compute, storage, applications and services.

    With ever more data being generated, processed and stored in ever more locations, issues surrounding infrastructure management and data security, privacy and governance will become even more important than they are today. Let's hope those issues are addressed sooner rather than later.

    RECENT AND RELATED CONTENT

    Understanding Edge ComputingThe edge is that theoretical space where a data center resource can be accessed in the minimum amount of time. The location could be in the data center, on the desktop, or wherever there's a need.

    Microsoft Azure gets new tools for edge computing and machine learning (TechRepublic)Announced at the Microsoft Ignite conference today, these new features are designed to streamline the collection, processing and analysis of large volumes of data.

    VMware taps IoT to extend hybrid and multi-cloud environments to the edge (TechRepublic)At VMworld 2018, VMware unveiled its extended edge computing strategy to better control, secure, and scale customers' edge and IoT applications and solutions.

    IT leader's guide to edge computing (Tech Pro Research)Companies of all sizes and across various industries are moving to edge computing to generate, collect, and analyze data so they can take immediate action on that information. This guide looks at the pros and cons of edge computing and how its real-world usage has been working out.


    International Business Machines (IBM) Q3 2017 Results - Earnings Call Transcript | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!This quarter, our Cloud revenue was up 20%, and over the last 12 months has grown to a $15.8 billion revenue base, which represents 20% of IBM’s revenue ... where we had triple-digit SaaS growth led b...


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