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IBM IBM Business Analytics Performance

IBM's precise-Line efficiency Continues to elevate purple Flags -- Tech examine | killexams.com Real Questions and Pass4sure dumps

fb's Voice Assistant Is doubtless supposed for its own Apps and Hardware

CNBC reviews that fb (FB) is working on a new voice assistant, by means of an R&D group inside its augmented truth and digital truth community. The record comes a yr after facebook pulled the plug on M, a voice assistant that partly relied on people and would as a consequence were problematic to scale.

even though the assistant is deemed a rival to the likes of Apple's (AAPL) Siri, Alphabet's (GOOGL) Google Assistant and Amazon.com's (AMZN) Alexa, it might make much more feel for facebook to center of attention on an answer that integrates with its app family unit (core facebook, Messenger, Instagram and WhatsApp) and maybe contraptions equivalent to Oculus VR headsets and the Portal speaker, than to combat Apple, Google and Amazon's offerings head-on.

When it involves performing as the basic voice assistant for a smartphone or tablet, Siri and Google Assistant have the skills of being baked into the realm's two dominant cellular working systems. Alexa and Google Assistant, in the meantime, have tremendous mindshare in the domestic, with the structures (Alexa notably) having also developed up tremendous hardware and developer ecosystems. And all three capabilities have huge head-begins in terms of accumulating consumer facts and being optimized with the help of that data.

besides the fact that children, fb might conceivably see strong adoption for an assistant that makes it less demanding to make use of and access facets within its apps and features, which together now have over 2 billion every day clients. Such an providing may additionally -- through making it more straightforward for a person who has one fb app opened to entry an extra app -- mesh neatly with fb's lately-announced plans to integrate Messenger, WhatsApp and Instagram's messaging functions. additionally, it might bolster facebook's attempts to force the adoption of Messenger and WhatsApp as a customer assist and engagement platform for agencies.

Apple still looks to Be Taking its vehicle venture seriously

Reuters experiences Apple has held talks with as a minimum 4 organisations about supplying LIDAR sensors -- a staple for many self sustaining using techniques -- and is also working by itself LIDAR unit. At a time when LIDAR sensors are costly and somewhat cumbersome, Apple is reportedly looking for sensors "that might be smaller, more affordable and greater effortlessly mass produced" than what's at the moment purchasable.

The record is a clean signal that Apple, which continues working a fleet of self-using look at various cars in California, remains interested in commercializing some kind of car hardware offering -- even if a self-driving system or (as can be extra in response to its old mind-set) a completely-fledged motor vehicle. outdated signs consist of the hiring of trade vets reminiscent of former Tesla (TSLA) engineering chief Doug field and senior Waymo engineer Jaime Waydo, and the submitting of patents regarding things similar to car heads-up displays (HUD) and sunroof methods.

a couple of traits -- from the appearance of a mass-market for electric cars, to the creation of potent infotainment programs that includes cellular connectivity, to HUD and augmented fact advances, to the construction of self-driving techniques that (by means of removing the want for a steerage wheel, pedals and facet visibility) might force a rethink of how vehicles are designed -- act as incentives for Apple to are trying and develop into an immense automotive participant. We're still a long techniques off from seeing an Apple automobile launch (assuming one does arrive), however the business does appear to be laying the groundwork for a huge automotive investments in the coming years.IBM's proper-Line performance Continues to raise purple Flags IBM's  ( IBM)  salary boom continues trailing that of an enterprise IT sector growing at a little over three%. And the growth of its lucrative software operations continues significantly trailing that of an business application market believed to be starting to be around eight%. The business's regular focal point on buzzwords comparable to AI, cloud, IoT and analytics haven't managed to trade that.

large Blue's shares are down near three% in Wednesday buying and selling after it posted blended Q1 effects. Non-GAAP EPS of $2.25 (down eight% yearly) beat a $2.22 consensus with the support of lower-than-anticipated operating fees (each job cuts and a powerful dollar helped) and average gross margin expansion. besides the fact that children, revenue of $18.18 billion (down 4.7% yearly in dollars and zero.9% in constant forex) neglected a consensus of $18.forty seven billion.

The business is conserving its suggestions (issued in January) for 2019 EPS of "as a minimum" $13.90, and for free of charge cash flow (FCF) of about $12 billion (up a little from 2018, but below 2017's stage). On the earnings name, CFO Jim Kavanaugh forecast about 22% of full-yr EPS could be produced in Q2, and that 2d-half EPS growth could be "skewed to the fourth quarter."

IBM overhauled its business section reporting somewhat. some of the business's cloud features operations are actually grouped with what was its Cognitive solutions segment, which contains a whole lot of its application operations, in place of with IBM's normal IT capabilities operations. the brand new segment, called Cloud & Cognitive utility, posted Q1 income of $5 billion, down 2% yearly.

The international company services (GBS) phase, which covers IBM's consulting and IT outsourcing businesses, noticed its profits remain roughly flat at $4.1 billion. despite the fact, the world know-how services (GTS) segment, which covers normal IT capabilities and some cloud features, saw income drop 7% to $6.9 billion. And the systems phase, which covers hardware and working programs, noticed income drop eleven% to $1.3 billion, as mainframe cyclicality and storage share losses offset moderate growth in IBM's power server enterprise.

IBM additionally stated that the annual run cost for its "as-a-service" cloud income, which covers issues akin to cloud infrastructure capabilities, developer systems and software subscriptions, become up just 10% annually on the end of Q1 to $11.7 billion. That contrasts with the 20%-plus growth that many big cloud/SaaS application companies are nevertheless seeing, not to point out the 40%-plus boom that Amazon internet features (AWS) continues to submit.

With IBM carrying an enterprise price (market cap plus internet debt) of below 11 instances its anticipated 2019 FCF, its stock continues to be relatively inexpensive. And if it executes well, it be possible that IBM's pending $34 billion deal to buy open-supply application large purple Hat (RHT)  will do tons to improve its universal utility efficiency.

however for now, there's nevertheless lots to be anxious about when it comes to large Blue's top-line efficiency. And for that purpose, greater options exist for tech investors hunting for low-assorted names.

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IBM Watson & Sandvik driven OptiMine Analytics positive factors Adoption | killexams.com Real Questions and Pass4sure dumps

foreign company Machines company’s IBM strong platforms to revolutionize mining, developed in collaboration with Sandvik Mining and Rock technology, are witnessing powerful adoption from extraordinary mining companies.

Sandvik Mining and Rock know-how, a business within Sandvik community, offers gadget tools, technical options and services to mining and development companies. especially, Sandvik community has a robust foothold in mining and building business by reason of strength in its rock excavation and metal reducing equipments.

IBM Watson powered Sandvik’s OptiMine Analytics advice administration solution leverages IoT (or cyber web of things) and AI (or synthetic intelligence) capabilities to synchronize mining methods.

OptiMine Analytics utilizes utility and machine records to deliver actionable insights to miners on performance, utility and working situations.

IBM anticipates powerful adoption of IBM Watson driven OptiMine Analytics to disrupt mining industry, and as a result provoke the fourth industrial revolution, or industry 4.0.

particularly, shares of IBM have won 27.5% year to this point, outperforming trade’s rally of 26.4%.

potent facets choose Adoption

In a bid to seriously change mining methodologies, IBM is incorporating embedded cognitive capabilities and imaginitive IBM Design pondering workshops to boost a robust “Cognitive price Chain for Mining.”

OptiMine Analytics asset administration answer aids in optimizing mining by helping actual-time resolution making, forecasting device failure, and offering the potential to foretell and simulate talents actions.

certainly, IoT censor facts empowers miners with precise-time visibility of the mining gadget facilitating predictive upkeep analytics and optimization techniques. This drives operational efficiency, which in flip bolsters productivity.

The robust points substantiate speedy adoption of OptiMine Analytics, which has been lately selected by way of splendid mining organizations including, Vedanta Zinc overseas, Hindustan Zinc, Petra Diamonds and Barminco.

Market prospects Abound

The omnipresent nature of expertise is disrupting just about all industries and mining is no exception to the trend. as the world inhabitants continues to raise, the rock excavation and mining trade is increasing its boundaries to cater to the rising demand for minerals.

The miners are more and more incorporating powerful technologies to simplify conversation at depth and analyze gadget failure and other pitfalls. in fact, per ResearchAndMarkets, smart mining market is expected to witness a CAGR of 9.2% between 2018 and 2024.

Analyst company IDC expects global IoT spending to be $745 billion in 2019, indicating year-over-year increase of 15.4%. IDC expects the tally to hit $1 trillion in 2022.

based on IBM Watson’s cognitive capabilities and growing clout of OptiMine Analytics, they agree with that IBM-Sandvik collaboration is neatly poised to profit from the amazing increase potentialities.

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (hang).

Some more advantageous-ranked shares in the broader technology sector are Cadence Design techniques, Inc CDNS, Synopsys, Inc. SNPS and Symantec business enterprise SYMC, every wearing a Zacks Rank #1 (powerful purchase). which you can see the complete record of today’s Zacks #1 Rank stocks right here.

Cadence Design, Synopsys and Symantec have lengthy-term earnings increase costs of 12%, 10% and seven.9%, respectively.

modern top-rated stocks from Zacks

Would you want to see the updated picks from their most excellent market-beating strategies? From 2017 through 2018, while the S&P 500 won +15.eight%, five of their screens again +38.0%, +sixty one.3%, +61.6%, +sixty eight.1%, and +98.3%.

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Story continues


IBM Counts On Its Cloud And Platform For The Turnaround | killexams.com Real Questions and Pass4sure dumps

No influence found, are attempting new key phrase!IBM has ... which encompass analytics, cybersecurity, and synthetic intelligence, fell 1.5% to $5.04 billion, but managed to beat the analyst estimates of $4.18 billion. global business features ...

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IBM Certification Guide: Overview and Career Paths | killexams.com real questions and Pass4sure dumps

The International Business Machines Corporation, better known simply as IBM, is a world leader in IT products and services. It covers IT comprehensively, encompassing enterprise applications, middleware and operating systems, computing systems, storage, cloud computing, and business analytics.

IBM became known as Big Blue, most likely because of the color of its mainframes in the late 1960s. Some of IBM's best-known product brands include B2, Lotus, Tivoli and WebSphere. Today, the company is making its name known with Watson, IBM's cognitive technology.

IBM certification program overview

A company as large, widely followed and revered as IBM is bound to have a well-developed certification program, and indeed it does. The IBM Professional Certification Program boasts almost 300 credentials.

In the past, IBM classified its certifications according to software, hardware, IBM PureSystems, solutions and "other." While individual certifications may still fall into those general categories, certification classifications have been revamped to more closely align with the company's product portfolio. IBM currently classifies its certifications across 11 main categories:

  • Analytics
  • Cloud
  • Cognitive Solutions
  • Commerce
  • Global Financing
  • Global Technology Services
  • Security
  • Systems
  • Watson
  • Watson Health
  • Watson Internet of Things
  • Certifications are aimed at IT professionals who implement, support or sell IBM products at the Associate, Professional and Advanced Professional (including Expert) tiers. Most certifications can be achieved by passing one or two exams focused on a specific IBM application, system or service, although some expert-level certs require up to five exams.

    IBM exams and retake policy

    IBM certification exams are delivered through Pearson VUE testing centers. An exam may last 60 to 150 minutes, include anywhere from 22 to 97 questions, and cost $100 to $200. Exam details vary greatly across certifications, so they provide only high/low information here, and exam prices may change as of the first day of any calendar year. Exam prices also vary by geography, so candidates need to check prices for their localities.

    A candidate who fails an exam the first time may retake it as soon as possible but must wait 30 days before making subsequent attempts. To help defray costs, IBM occasionally offers a Take It Again promotion in which candidates can retake an exam for free if they fail the first attempt. IBM has also been known to offer exams for free at events such as InterConnect 2017. Seating is frequently limited, so interested professionals should check out event offerings and register early.

    IBM makes navigating certification easy with webpages dedicated to its certification process, along with one-stop shopping for test information (exam name and number, associated certification, number of questions, exam length, passing score requirements, plus links to the individual exam page) and easy-to-find exam pricing.

    The following sections provide a high-level view of IBM certifications in each of the main categories.

    IBM Analytics

    Featuring more than 100 certifications, IBM Analytics is IBM's largest certification category, reflecting the growing industry use of analytics and IBM's leadership in this area. Analytics certifications are designed for professionals working with IBM solutions for performance management and business intelligence.

    IBM offers Analytics certifications in four areas: Cloud Content Services, Cognos Analytics, Collaboration Solutions and Social Business, and Platform Analytics. While there is some overlap among products, each concentration area features credentials in different portions of the IBM Analytics product portfolio:

  • Analytics Cloud Content Services: Includes credentials for Content Manager onDemand, Datacap (Datacap Taskmaster Capture), Enterprise Records, FileNet (Business Process Manager and Content Manager), Case Manager, Content Collector, Content Navigator and Case Foundation
  • Cognos Analytics: Focused entirely on Cognos-related credentials, including Cognos TM1, Cognos TM1 Data Analysis, Risk Information Governance for BI, Cognos BI, BI Reports and Multidimensional Reports, BI Data Warehouse, BI Metadata Models, BI OLAP Models, BI Scorecards, and BI Controller
  • Analytic Platform: Includes credentials for DB2, Cognos, PureData System, Informix, SPSS, InfoSphere, Big Data and Apache Spark
  • IBM Collaboration Solutions and Social Business: Credentials focus on social business solutions such as Lotus Notes and Domino, Connections and Connections Cloud, Sametime, Social Business, OpenSocial, and SmartCloud Notes Hybrid
  • IBM offers Analytics credentials from entry level to expert.

    IBM Cloud

    Formerly IBM Cloud and Mobility, the IBM Cloud certification portfolio has grown exponentially and now offers more than 70 certifications, making it IBM's second largest certification portfolio. IBM Cloud credentials focus on six primary areas:

  • Cloud Integration – Application Platform: Includes credentials for Business Process Manager, WebSphere Application Server, Worklight Foundation, PureApplication System and WebSphere Application Server Network Deployment products
  • Cloud Integration – Dev Ops: Credentials focus on Rational, SmartCloud, Tivoli, Service Management, Control Desk and Workload Automation products
  • Cloud Integration – Hybrid Integration: Provides credentials for IBM Service Management Asset Management, CICS Transaction Server, API Management, MQ, Integration Bus, WebSphere, DataPower Gateway, API Connect and WebSphere DataPower SOA Appliances Firmware
  • Cloud Integration – Process Transformation: Includes credentials for WebSphere Business Modeler, Business Process Manager, Blueworks Live, Operational Decision Manager, and IBM BPM using Process Designer product families
  • Cloud Platform: Credentials in this category target Cloud Platform and SoftLayer.
  • Cloud Solutions: Includes credentials for Cloud Reference Architecture and Cloud Platform Solution
  • This portfolio features credentials for developers, administrators and solution architects.

    IBM Cognitive Solutions

    IBM Cognitive Solutions certifications focus mainly on the Cognos, OpenPages and Algo One products. Where Cognos is geared toward business intelligence, analytics and financial strategy management, OpenPages facilitates governance, risk and compliance management. Algo One is a tool used for analyzing risk factors and models. IBM's Cognitive Solutions certifications are available at the administrator, associate and advanced developer, solution designer, and specialist levels.

    IBM Commerce

    IBM Commerce certifications are all about the customer, and how professionals working with IBM technology can help the customer conduct and grow its business. Commerce certs come in three flavors: engaging the customer, partner and supplier relationships, and digital commerce. These certs cover a wide swath of credential roles, including business analysts, solution designers, system administrators, application developers, and deployment and implementation professionals.

    The Customer Engagement category features credentials that focus on maximizing the customer product experience for WebSphere Commerce, Digital Analytics, Tealeaf, Marketing Operations, Sterling (Configuration, Order Management), Campaign and Interact.

    Partner and Supplier Engagement certifications focus on professionals managing partner and supplier relationships. Products featured include Sterling Connect Direct, WebSphere Transformation Extender, Emptoris Contract Management and Sterling B2B Integrator.

    Digital Experience credentials target professionals charged with enhancing the customer's digital experience and focus on WebSphere Portal, Web Content Manager, Digital Experience, Forms, Forms Experience Builder and Web Experience Factory.

    IBM Global Financing

    IBM offers a single Global Financing credential, the IBM Global Financing Sales Master Specialist Professional certification. A single exam is required to earn the credential.

    IBM Global Technology Services

    IBM Global Technology Services is promoted as the IBM group that builds and manages "the backbone that powers the era of enterprise hybrid IT." Simply put, GTS is all about services (both outsourced and managed services) for Watson, hardware, software and general technical support.

    IBM offers a single cert in this category: the IBM Certified Specialist – IBM U.S. Technical Support Services V2. This certification is only available in the U.S. and is geared toward business partners who provide Level 1 or 2 technical support. Candidates must pass a single exam to earn the credential.

    IBM Security

    In today's cognitive era, IT infrastructure security is essential to ensuing IT services are not only predictable but compliant and secure as well. An individual with an IBM Security certification has proven skills in designing, implementing, configuring, testing and/or supporting specific IBM security products. Many IBM Security certifications target professionals who process and analyze specialized data geared toward law enforcement, government agencies and municipalities (utility-related).

    IBM security products include IBM Security Guardium, Security QRadar, i2 Analysts Notebook, Security SiteProtector, Endpoint Manager, MobileFirst Protect and many others. The IBM Security certification portfolio offers IT professionals 32 different options to hone and validate their security skills.

    Depending on the certification, available credentials include administrator and associate administrator, analyst and associate analyst, associate, deployment professional, integration professional, solution advisor, security intelligence, and specialist. Deployment professional certs make up approximately one-third of the available security credentials (11 total).

    IBM Systems

    IBM currently offers 29 different IBM Systems certifications aimed at users, advanced users, implementers and sellers. IBM Systems certs fall into five categories:

  • Power Systems: IBM offers five Power Systems certs. The three Sales credentials include certified sales specialist and technical sales specialist for professionals working with solutions for Power Systems with POWER8. Two certified system administrator credentials focus on either AIX or IBM i. Candidates for any of the IBM certified system administrator certifications should have two or more years of experience working with Power Systems server running AIX 7 or IBM i 7.1/7.2, and are required to pass a single exam.
  • Storage Software: There are nine different Storage Software certs, six of them targeting Spectrum products (Storage Management, Cloud, Protect, Scale, Computing and Storage Family Solutions). The three remaining certs focus on Tivoli Storage Manager and Cloud Object Storage. Candidates may earn administrator, deployment professional or solution advisor credentials.
  • Storage Systems: There are nine certified specialist credentials in this category, with solutions targeting midrange and enterprise storage systems, as well as flash technical solutions, XIV storage systems and high-end tape technical solutions.
  • z Systems: IBM offers four certified specialist credentials for z Systems. Two are focused on sales, while two target technical support for z Systems.
  • Other: This category encompasses certs for systems that don't readily meet the criteria for the other areas. There are two credentials in this category: Certified Database Administrator – IMS and Certified System Programmer – IMS.
  • IBM Watson

    You may remember the now-famous bout where IBM's Watson supercomputer won the "Jeopardy" game show challenge, ushering in a new era in cognitive computing, big data and analytics. Since that time, IBM kick-started the IBM Watson Group with a healthy $1 billion in startup funding. Today, IBM Watson is rapidly becoming an industry leader for technology platforms that leverage the power of natural language and machine learning.

    Eighteen IBM Watson certs are available. Seven certifications focus on Maximo Asset Management solutions, while the remaining certs are divided between TRIRIGA, Collaborative Lifecycle Management, Rhapsody, Predictive Maintenance and Quality, and Rational. Available credentials include associate, consultant, solutions (specialist, expert and advisor), deployment (infrastructure and advanced) and application developer.

    IBM Watson Health

    A brainchild of The Watson Group, Watson Health is one of the newest rising stars at IBM. Watson Health boasts numerous partnerships with healthcare industry leaders.

    There are currently two Watson Health certs available: IBM Certified Application Developer – Curam V604 and IBM Certified Associate Business Process Analyst – Curam V605. The Certified Application Developer – Curam is an intermediate credential that targets Java professionals who also work as Curam developers. The Associate Business Process Analyst – Curam is an entry-level credential for testers, business analysts or consultants seeking to establish foundation-level Curam skills or implement Curam projects. A single test is required for each certification. Note: IBM Business Partner candidates who've purchased a Value Package qualify for IBM's You Pass, They Pay (YPWP) Program. Under the YPWP program, candidates are reimbursed for exams and some preparatory courses.

    IBM Watson: Internet of Things

    The internet of things, or IoT, is certainly changing the way they connect, interact and conduct business. The IBM Watson: Internet of Things portfolio is a new addition to the IBM family. Like IBM Watson certs, the IBM IoT certs focus primarily on Maximo Asset Management, Rational and TRIRIGA products. Credentials include specialists, associates, deployment professionals and more.

    Related jobs, careers and training resources

    You'll find IBM-certified professionals employed in a variety of job roles that span all industry sectors. A simple job search for "IBM professionals" on Simply Hired turned up more than 40,000 job postings. For example, they found these postings:

  • A major telecom company looking for a senior data scientist experienced in building predictive modeling tools, including IBM SPSS
  • A company in Illinois seeking a certified WebSphere system administrator
  • An Arizona-based solution team with an opening for an experienced IBM sales and marketing account executive
  • A New Jersey healthcare company seeking a systems analyst
  • Other common job roles included developers, business analysts, technical solution engineers, platform support engineers and integration leads. Career choices are limited only by your personal interests and aspirations.

    IBM maintains a list of training resources for each exam on the individual exam webpage. Here, candidates will find test objectives, sample exams and assessment tests, as well as links to more formal training and other resources. Additional training materials, including books and e-books, may be purchased from the IBM Professional Certification Marketplace. On the IBM Training and Skills webpage, candidates will find links to blogs, articles, learning opportunities, events, Redbooks and PartnerWorld.


    Presto crypto: IBM releases gruntier, faster Z14 mainframe | killexams.com real questions and Pass4sure dumps

    IBM has launched its latest, newest, biggest, baddest mainframe, the z14 system.

    It features the next generation of IBM's CMOS mainframe chip technology, with 10-core processors using 14nm silicon-on-insulator technology, and running at 5.2GHz, claimed to be the fastest processor in the industry. Each core has hardware accelerated encryption implementing a CP Assist for Cryptographic Function (CPACF). The CPU also has 1.5 times more on-chip cache per core compared to the z13. There can be up to 32TB of memory, three times the z13 maximum, and its IO is three times faster as well.

    A compression co-processor in each core has been improved to use fewer CPU cycles for compression/de-compression and DB2 will take advantage of that in the future.

    IBM says the z14 has 10 per cent more performance per core than z13, and there are up to 170 configurable cores, meaning up to 35 per cent more total capacity in a single footprint compared to a z13.

    IBM_z14_PR_shot

    IBM z14 PR shot

    The z14 CPU has new instructions in the single instruction, multiple data (SIMD) facility that speeds traditional decimal operation workloads (i.e. COBOL 6.2, PL/I 5.2) and analytics (ie, Apache Spark for z/OS) beyond that provided by the faster processor.

    IBM claims the z14 can run Java workloads 50 per cent faster than x86 alternatives. FICON SAN access features 10 times lower latency than the z13 with the zHyperLink Express, enabling application response time to be cut in half.

    The z14 has a scalable system structure that delivers up to a 35 per cent capacity increase for traditional workloads and an up to a 35 per cent capacity increase for Linux workloads compared to the previous generation z13.

    There is coming z/OS software that will provide capabilities for private cloud service delivery, that will include support of workflow extensions for IBM Cloud Provisioning and Management for z/OS and real-time SMF analytics infrastructure support.

    Encryption

    The z13 was launched in 2015. At the time they wrote it was grunty enough to do “real time encryption of all mobile transactions at any scale” up to a claimed 2.5 billion transactions a day. The z14 is even gruntier and bumps that up almost five fold to 12 billion encrypted transactions per day.

    Big Blue burbles it can run the world's largest MongoDB instance with 2.5x faster NodeJS performance compared to paltry x86-based platforms. It supports 2,000,000 Docker containers and 1,000 concurrent NoSQL databases, it's claimed.

    The system has an encryption engine, has a 7x increase in cryptographic performance over the z13, with a 4x increase in silicon dedicated to cryptographic algorithms. It protects encryption keys with so-called tamper responding hardware which invalidates keys at any sign of meddling, and IBM says they can be later restored safely. This capability can be extended outside the z14 to storage systems and servers in the cloud.

    A Secure Service Container protects is claimed to protect against insider threats from contractors and privileged users, providing automatic data and code encryption in-flight and at-rest, and tamper-resistance during installation and runtime. Information has to be decrypted before it is processed, of course.

    This mainframe responds to API calls from cloud services and z14 developers can call any cloud service. These APIs can be encrypted nearly three times faster than x86 systems, claims IBM.

    The z14 can "pervasively encrypt data associated with any application, cloud service or database all the time," including IBM's Cloud Blockchain service. The company has set up IBM Cloud Blockchain data centres in Dallas, London, Frankfurt, Sao Paolo, Tokyo and Toronto, which are secured using the capital Z mainframe as the encryption engine.

    Ross Mauri, general manager IBM Z, gave out an (unencrypted) canned quote saying: "The vast majority of stolen or leaked data today is in the open and easy to use because encryption has been very difficult and expensive to do at scale. They created a data protection engine for the cloud era to have a significant and immediate impact on global data security."

    There are new container pricing models:

  • Microservices and applications can be co-located to optimize qualities of services priced competitively (IBM claims) with public cloud and on-premises systems,
  • Application development and test - customers can triple capacity with no increase in their monthly licence charge,
  • Pricing based on the payments volume a bank processes, not the available capacity,
  • These pricing models are scalable within and across logical partitions (LPARs) and provide better metering, capping and billing.

    Our thoughts

    The two biggest threats to IBM's continued mainframe revenue stream are x86 servers and the public cloud, hence IBM's Z release being littered with "better than x86" statements and claims. The pervasive encryption should encourage security-conscious CIOS to keep mainframe apps on the mainframe and in-house, helping to stem the dykes walling off the x86 server and public cloud seas threatening to breach its proprietary mainframe profit centre.

    The pricing changes are intended to fend off the public cloud as well.

    An analysis from Toni Sacconaghi Jr for Bernstein Research says IBM's hardware business is in secular decline, and, while mainframe hardware contributes just 3 per cent of revenues, the overall mainframe platform accounted for nearly a quarter of total IBM revenues and an estimated 40 per cent of profits in 2016.

    Sacconaghi said he has seen a decline in mainframe hardware revenues from a historic $3bn - $4bn a year to $2bn in fiscal 2016, attributable to fewer new workloads or volumes moving to the mainframe, and some potential migration of Linux workloads off the mainframe.

    The overall mainframe platform revenues come from IBM's base and not from new hardware sales. Consequently the z14's prime role is to support and continue this installed base revenue stream.

    Container pricing for IBM Z is planned to be available by year-end 2017 and enabled in z/OS V2.2 and z/OS V2.3. Get a z14 datasheet here. ®

    Sponsored: Becoming a Pragmatic Security Leader


    The Best Self-Service Business Intelligence (BI) Tools of 2018 | killexams.com real questions and Pass4sure dumps

    Analytics Beyond Spreadsheets

    For many years, Microsoft Excel and other spreadsheets were the tools of choice for business professionals who were looking to visualize their data. But spreadsheets had their limits for many business intelligence (BI)-related tasks. Even today, trying to creating charts analyzing complex datasets in Excel can still be frustrating. Sometimes you start with the wrong kind of data, for example, or you may not know how to manipulate the spreadsheet to create the data visualization{{/ZIFFARTICLE} you need. On the other hand, the rising tide of data democratization is giving everyone in an organization access to corporate data. The need has arisen for effective tools that people of all skill levels can use to make sense of the wealth of information created by businesses every single day.

    Spreadsheets also fall down when the data isn't well-structured or can't be sorted out in neat rows and columns. And, if you have millions of rows or very sparse matrices, then the data in a spreadsheet can be painful to enter and it can be hard to visualize your data. Spreadsheets also have issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases, or when multiple users try to maintain and collaborate on the same spreadsheet.

    A spreadsheet containing up-to-the-minute data can also be a problem, particularly if you have exported graphics that need to be refreshed when the data changes. Finally, spreadsheets aren't good for data exploration; trying to spot trends, outlying data points, or counterintuitive results is difficult when what you are looking for is often hidden in a long row of numbers.

    While spreadsheets and self-service BI tools both make use of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a way to store and display calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is all about the math more than the model itself.

    This is all a long-winded way of saying that when businesses use a spreadsheet, they are actively sabotaging themselves and their ability to consistently get valuable insights from their data. BI tools are specficially designed to help businesses better understand their data, and can prove to be a huge benefit to those upgrading from what a limited spreadsheet can do.

    What Is Business Intelligence?

    Defining BI is tricky. When you examine what it does and why companies use it, it can start to sound vague and nebulous. After all, many different kinds of software offer analytics features, and all businesses want to improve. Understanding what a BI is or isn't can be unclear.

    BI is an umbrella term meant to cover all of the activities necessary for a company to turn raw information into actionable knowledge. In other words, it's a company's efforts to understand what it knows and what it doesn't know of its own existence and operations. The ultimate goal is being able to increase profits and sharpen its competitive edge.

    Framed that way, BI as a concept has been around as long as business. But that concept has evolved from early basics [like Accounts Payable (AP) and Accounts Receivable (AR) reports and customer contact and contract information] to much more sophisticated and nuanced information. This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance. Separately tracking such metrics is something most businesses can do regardless of the tools employed. Combining them, especially disparate results from metrics normally not associated with one another, into understandable and actionable information, well, that's the art of BI. The future of BI is already shaping up to simultaneously broaden the scope and variety of data used and to sharpen the micro-focus to ever finer, more granular levels.

    BI software has been instrumental in this steady progression towards more in-depth knowledge about the business, competitors, customers, industry, market, and suppliers, to name just a few possible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and complex to be entirely handled by mere humans. Early efforts to do these tasks via software, such as customer relationship management (CRM) and enterprise resource planning (ERP), led to the formation of "data silos" wherein data was trapped and useful only within the confines of certain operations or software buckets. This was the case unless IT took on the task of integrating various silos, typically through painstaking and highly manual processes.

    While BI software still covers a variety of software applications used to analyze raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main difference between today's BI software and Big Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from small to large. Big Data analytics and apps handle data analysis for very large data sets, such as silos measured in petabytes (PBs).

    Self-Service BI and Data Democratization

    The BI tools that were popular half a decade or more ago required specialists, not just to use but also to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to get and understand the business—the company decision makers—and those who were gathering, processing, and interpreting that data—usually data analysts and database administrators. Because being a data specialist is a demanding job, many of these folks were less well-versed in the actual workings of the business whose data they were analyzing. That led to a focus on data the company didn't need, a misinterpretation of results, and often a series of "standard" reporting that analysts would run on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can be highly valuable in fast-moving situations.

    This problem has led to a growing new trend among new BI tools coming onto the market today: that of self-service BI and data democratization. The goal for much of today's BI software is to be available and usable by anyone in the organization. Instead of requesting reports or queries through the IT or database departments, executives and decision makers can create their own queries, reports, and data visualizations through self-service models, and connect to disparate data both within and outside the organization through prebuilt connectors. IT maintains overall control over who has access to which tools and data through these connectors and their management tool arsenal, but IT no longer acts as a bottleneck to every query and report request.

    As a result, users can take advantage of this distributed BI model. Key tools and critical data have moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with new BI software. This results in a whole new kind of analysis becoming available to the organization, namely, that of experienced, front-line business people who not only know what data they need but how they need to use it.

    The emerging crop of BI tools all work hard at developing front-end tools that are more intuitive and easier to use than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing decision will be to evaluate who in the organization should access such tools and whether the tool is appropriately designed for that audience. Most BI vendors indicate they're looking for their tool suites to become as ubiquitous and easy to use for business users as typical business collaboration tools or productivity suites, such as Microsoft Office. None have gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI tool suites tend to focus on three core types of analytics: descriptive (what did happen), prescriptive (what should happen now), and predictive (what will happen later).

    What Is Data Visualization?

    In the context of BI software, data visualization is a fast and effective method of transferring information from a machine to a human brain. The idea is to place digital information into a visual context so that the analytic output can be quickly ingested by humans, often at a glance. If this sounds like those pie and bar charts you've seen in Microsoft Excel, then you're right. Those are early examples of data visualizations.

    But today's visualization forms are rapidly evolving from those traditional pie charts to the stylized, the artistic, and even the interactive. An interactive visualization comes with layered "drill downs," which means the viewer can interact with the visual to reach more granular information on one or more aspects incorporated in the bigger picture. For example, new values can be added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can turn a static visual into an animation or a dashboard.

    The best visualizations do not seek artistic awards but instead are designed with function in mind, usually the quick and intuitive transfer of information. In other words, the best visualizations are simple but powerful in clearly and directly delivering a message. High-end visuals may look impressive at first glance but, if your audience needs help to understand what's being conveyed, then they've ultimately failed.

    Most BI software, including those reviewed here, comes with visualization capabilities. However, some products offer more options than others so, if advanced visuals are key to your BI process, then you'll want to closely examine these tools. There are also third-party and even free data visualization tools that can be used on top of your BI software for even more options.

    Products and Testing

    In this review roundup, I tested each product from the perspective of a business analyst. But I also kept in mind the viewpoint of users who might have no familiarity with data processing or analytics. I loaded and used the same data sets and posed the same queries, evaluating results and the processes involved.

    My aim was to evaluate cloud versions alone, as I often do analysis on the fly or at least on a variety of machines, as do legions of other analysts. But, in some cases, it was necessary to evaluate a desktop version as well or instead of the cloud version. One example of this is Tableau Desktop, a favorite tool of Microsoft Excel users who simply have an affinity for the desktop tool (and who just move to the cloud long enough to share and collaborate).

    I ended up testing the Microsoft Power BI desktop version, too, on a Microsoft representative's recommendation because, as the rep said, "the more robust data prep tools are there." Besides, said the rep, "most users prefer the desktop tool over a web tool anyway." Again, I don't doubt Microsoft's claim but that does seem weird to me. I've heard it said that desktop tools are preferred when the data is local as the process feels faster and easier. But seriously, how much data is truly local anymore? I suspect this odd desktop tool preference is a bit more personal than fact-based, but to each his own.

    Then there's Google Analytics, a pure cloud player. The tool is designed to analyze website and mobile app data so it's a different critter in the BI app zoo. That being the case, I had to deviate from using my test data set and queries, and instead test it in its natural habitat of website data. Nonetheless, it's the processes that are evaluated in this review, not the data.

    While I didn't test any of these tools from a data scientist's role, I did mention advanced capabilities when I found them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the ability to extend to highly advanced features and was also one of the easiest to use upfront. IBM Watson Analytics is well-suited for business analysts and for widespread data democratization because it requires little, if any, knowledge of data science. Instead, it works well by using natural language and keywords to form queries, a characteristic that can make it valuable to practically anyone. It's highly intuitive, very powerful, and easy to learn. Microsoft Power BI is a strong second as it, too, is powerful while also familiar, certainly to any of the millions of Microsoft business users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they all have their own pros and cons. We'll be adding even more in the coming months.

    One thing to watch out for during your evaluations of these products is that many don't yet handle streaming data. For many users, that won't be a problem in the immediate future. However, for those involved with analyzing business processes as they happen, such as website performance metrics or customer behavior patterns, streaming data can be invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and make streaming data and streaming analytics a must-have feature. Many of these tools will have to up their game accordingly so, unless you want to jump ship in a year or two, it's best to think ahead when considering BI and the IoT.

    BI and Big Data

    Another area in which self-service BI is taking off is in analyzing Big Data. This is a newer development in the database space but it's driving tremendous growth and innovation. The name is an apt descriptor because Big Data generally refers to huge data sets that are simply too big to be managed or queried with traditional data science tools. What's created these behemoth data collections is the explosion of data-generating, tracking, monitoring, transaction, and social media tools (to name a few) that have become so popular over the last several years.

    Not only do these tools generate loads of new data, they also often generate a new kind of data, namely "unstructured" data. Broadly speaking, this is simply data that hasn't been organized in a predefined way. Unlike more traditional, structured data, this kind of data is heavy on text (even free-form text) while also containing more easily defined data, such as dates or credit card numbers. Examples of apps that generate this kind of data include the customer behavior-tracking tools you use to see what your customers are doing on your e-commerce website, the piles of log and event files generated from some smart devices (such as alarms and smart sensors), and broad-swath social media tracking tools.

    Organizations deploying these tools are being challenged not only by a sudden deluge of unstructured data that quickly strains storage resources [think beyond terabytes (TB) into the PB and even exabyte (EB) range] but, even more importantly, they're finding it difficult to query this new information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. New data storage innovations such as data lakes are emerging to solve for this need, but organizations still relying exclusively on traditional tools while deploying front-line apps that generate unstructured data often find themselves sitting on mountains of data they don't know how to leverage.

    Enter Big Data analysis standards. The golden standard here is Hadoop, which is an open-source software framework that Apache specifically designed to query large data sets stored in a distributed fashion (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query Big Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query all of your business data for maximum insight, then Hadoop is what you need.

    You can download and implement Hadoop itself to perform your queries, but it's typically easier and more effective to use commercial querying tools that employ Hadoop as the foundation of more intuitive and full-featured analysis packages. Notably, most of the tools reviewed here, including Chartio, IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop, all support this. However, each requires varying levels of configuration or even add-on tools to do so—with IBM, Microsoft, and Tableau offering exceptionally deep capabilities. However, both IBM and Microsoft will still expect customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

    Finding the Right BI Tool

    Given the issues spreadsheets can have when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the right BI tool isn't a simple process. Unlike spreadsheets, BI tools have major differences when it comes to how they consume data inputs and outputs and manipulate their tables. Some tools are better at exploration than analysis, and some require a fairly steep learning curve to really make use of their features. Finally, to make matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to claim the self-serve BI label even if it doesn't quite fit.

    Getting the overall workflow down with these tools will take some study and discussion with the people you'll be designating as users. Tableau Desktop and Microsoft Power BI, for example, will start users out with the desktop version to build visualizations and link up to various data sources. Once you have this together, you can start sharing those results online or across your organization's network. With others, such as Chartio or Google Analytics, you start in the cloud and stay there.

    In recent years, companies have been taking advantage of the wide selection of online learning platforms out there to train their employees on using these platforms. As intuitive as these platforms may be, it is important to make sure that your employees actually know how to use these BI platforms so that you can make sure your investment was worthwhile. There are many ways of approaching this, but using the right online learning platform might be a good place to start looking.

    Given the wide price range of these products, you should segment your analytics needs before you make any buying decision. If you want to start out slowly and inexpensively, then the best route is to try something that offers significant functionality for free, such as Microsoft Power BI. Such tools are very affordable and make it easy to get started. Plus, they tend to have large ecosystems of add-ons and partners that can be a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop still has the largest collection of charts and visualizations and the biggest partner network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

    IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, all three products received their Editors' Choice award. Tableau Desktop may have a big price tag depending on which version you choose but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to devote some effort to it. Microsoft Power BI and Tableau Desktop also have large and growing collections of data connectors, and both Microsoft and Tableau have their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' development teams so it's a good idea to spend some time looking through those community forums to get an idea where these companies are headed.

  • Pros: Extremely user-friendly. Fantastic automatic report generation. Impressive support availability.

    Cons: Automated reports can quickly become defaults. Steep learning curve that might confuse beginners.

    Bottom Line: Zoho Reports is a solid option for general business users who might not be knowledgeable in analytics software. It's also available at an attractive price.

    Read Review
  • Pros: Accessible user interface. Smart guidance features. Impressively fast analytics. Robust natural language querying.

    Cons: Unable to do real-time streaming analytics.

    Bottom Line: IBM Watson Analytics is an exceptional business intelligence (BI) app that offers a strong analytics engine along with an excellent natural language querying tool. This is one of the best BI platforms you'll find and easily takes their Editors' Choice honor.

    Read Review
  • Pros: Extremely powerful platform with a wealth of data source connectors. Very user-friendly. Exceptional data visualization capabilities.

    Cons: Desktop and web versions divide data prep tools. Refresh cycle is limited on free version.

    Bottom Line: Microsoft Power BI earns their Editors' Choice honor for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: Enormous collection of data connectors and visualizations. User-friendly design. Impressive processing engine. Mature product with a large community of users.

    Cons: Full mastery of the platform will require substantial training.

    Bottom Line: Tableau Desktop is one of the most mature offerings on the market and that shows in its feature set. While it has a steeper learning curve than other platforms, it's easily one of the best tools in the space.

    Read Review
  • Pros: Bottlenecks are eliminated thanks to in-chip processing. Impressive natural language query in third-party applications.

    Cons: Might be too difficult for self-service business intelligence (BI). Analytics process still needs to be ironed out. Natural language capability can be limited.

    Bottom Line: Sisense is a complete platform that should be popular for experienced BI users. It may fall short for beginners, however.

    Read Review
  • Pros: Wide range of connectors. Impressive sharing features. Limitless data storage.

    Cons: User interface is not intuitive. Steep learning curve. Unwelcoming to new analysts.

    Bottom Line: Domo isn't for newcomers but for companies that already have business intelligence (BI) experience in their organization. Domo's a powerful BI tool with a lot of data connectors and solid data visualization capabilities.

    Read Review
  • Pros: Exceptional platform for website and mobile app analytics.

    Cons: Customer support has way too much automation. Focus on marketing and advertising can be frustrating to users. Relies mostly on third parties for training.

    Bottom Line: Due to its brand recognition and the fact that it's free, Google Analytics is the biggest name in website and mobile app intelligence. It has a steep learning curve but it is an awesome business intelligence tool.

    Read Review
  • Pros: Designed with general business users in mind. Solid return on investment.

    Cons: The data you can use is limited. Needs additional platform to connect.

    Bottom Line: The Salesforce Einstein Analytics Platform is designed for customer, sales, and marketing analyses, although it can server other needs, too. Its powerful analytics capabilities along with its solid natural language querying functionality and a wide array of partners make it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with plentiful extenders. Responsive pages make mobile publishing easiest. Impressive storytelling paradigm. Centralized view with consolidated analytics.

    Cons: Data prep features are lacking. Confusing toolbar design. Not friendly for beginners.

    Bottom Line: If your business already uses SAP's HANA database platform or some of its other back-end business platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But be warned that there's a steep learning curve and a noted dependence on other SAP products for full functionality.

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  • Pros: Impressive processing engine. Powerful query optimization on SQL. Entirely web-based. Complex queries are handled very well.

    Cons: Poorly designed user interface. Steep learning curve.

    Bottom Line: Chartio excels at building a powerful analytics platform that experienced business intelligence (BI) users will appreciate. Those new to BI, however, will find it very difficult to use.

    Read Review
  • Pros: Very deep SQL modeling ability. Uses Git for version management and collaboration.

    Cons: Very expensive. Not for small teams.

    Bottom Line: Looker is a great self-service business intelligence (BI) tool that can help unify SQL and Big Data management across your enterprise.

    Read Review
  • Pros: Custom access roles. Solid collection of public data online.

    Cons: Complex pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service business intelligence (BI) tool that delivers the best collection of user access roles among the BI tools they tested, and also demonstrates a promising start towards integrating Data-as-a-Service (DaaS).

    Read Review
  • Pros: One of the largest collections of data connectors. Many granular access roles.

    Cons: No free trial available. Training webinars can be costly.

    Bottom Line: The company's Focus query language is showing its age but Information Builders' self-service business intelligence (BI) tool WebFocus nevertheless has some powerful analysis features.

    Read Review
  • Pros: Very easy to get started. Nice team management and collaboration features.

    Cons: The cloud version has a subset of features found in Windows version. Online documentation could be improved.

    Bottom Line: While Tibco is still making the transition from a desktop to a cloud software vendor, its self-service business intelligence (BI) tool Tibco Spotfire is a great way to start visualizing your Excel data.

    Read Review
  • Pros: Excellent analytical support for Intuit QuickBooks. Very easy setup.

    Cons: Installation and setup is a bit of chore. No support for Intuit QuickBooks' online versions.

    Bottom Line: Clearify QQube is the best self-service business intelligence (BI) tool for in-depth analysis of your Intuit QuickBooks files, though you'll need to look elsewhere for broader BI tasks.

    Read Review


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