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00M-641 - IBM Exceptional Web Experience Sales Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : 00M-641
Exam Name : IBM Exceptional Web Experience Sales Mastery Test v1
Questions and Answers : 50 Q & A
Updated On : December 17, 2018
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00M-641 IBM Exceptional Web Experience Sales Mastery Test v1

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00M-641 exam Dumps Source : IBM Exceptional Web Experience Sales Mastery Test v1

Test Code : 00M-641
Test Name : IBM Exceptional Web Experience Sales Mastery Test v1
Vendor Name : IBM
Q&A : 50 Real Questions

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IBM IBM Exceptional Web Experience

Is Nagios The way forward for Monitoring For IBM i? | killexams.com Real Questions and Pass4sure dumps

November 12, 2018 Alex Woodie

For decades, expertise professionals grew to become to established frameworks from IBM Tivoli, CA, Hewlett Packard, and BMC (“The big four”) to computer screen their hardware and utility stacks. however the open supply world has caught up with these closed monitoring environments, and a challenge dubbed Nagios is poised to be the go-to platform for IT monitoring, including on IBM i.

Nagios, in case you don't seem to be universal, is a free and open source application product that offers monitoring and alerting for servers, network gear, applications, and the array of services that corporations more and more depend on. The software –backed by means of the necessary recursive acronym “Nagios Ain’t Gonna Insist On Sainthood” – was in the beginning developed through Ethan Galstad lower back in 2002 to run and monitor Linux systems, which turned into just starting to make headway in commercial enterprise facts centers.

through the years, the C-based mostly application has been extended to guide an array of object varieties, including host components like processor load, disk and RAM usage, and equipment logs for a majority of normal operating techniques, including IBM i. The vigorous Nagios community has created a wide array of Nagios brokers which have prolonged the framework to an estimated 5,000 gadgets.

Nagios is an open supply tool that allows for administrators to monitor their IT equipment and functions.

On the network aspect of issues, Nagios Core (because the open supply product is established) monitors routers, switches, and different gear, and supports SMTP, POP3, FTP, and SNMP protocols, amongst others. All far flung monitoring can be encrypted, and it additionally presents a plug-in that lets clients create their own displays in lots of languages.

facts collected with the aid of lively and passive Nagios plug-ins is routed into the Nagios server, where the records is sorted and alerts are despatched out. users can eat indicators in a variety of approaches, together with via a web browser, via SMS pagers, and thru email. With an easy glance at the Nagios dashboard on a mobile, a techniques admins can see if important servers, manner, and capabilities are alive or dead.

The Nagios Core server software also can work with a “circular-robin database” (RRD) to simplify the storage and querying of time-sequence statistics. With RRD, admins can dive deeper into Nagios facts and consider graphs to look efficiency tendencies. And with a little setup forward of time, Nagios can be configured to automatically respond to hobbies as they happen, or expand indicators to other people.

companies that want greater than what Nagios Core has to present can discover it from Nagios enterprises, the industrial outfit behind the open supply mission. The St. Paul, Minnesota-primarily based business, which employs Galstad, extends Nagios Core with a number of offerings, including Nagio XI, which features a greater extensible entrance-end and a MySQL-based again-end. Nagios XI additionally brings a higher consumer journey with extra powerful graphs and more suitable dashboards.

IBM i shoppers can use Nagios to display screen 15 metrics with the Nagios Plug-Ins for IBM i beta.

sadly, Nagios agencies doesn’t assist IBM i. but that you may get a Nagios distribution for IBM i from a little outfit known as overseas enterprise Machines. In 2017, huge Blue shipped a beta edition of an IBM i plug-in designed to feed records from the IBM i sever into the open supply Nagios Core product or Nagios XI.

in line with a developerWorks web page, the IBM i Nagios plug-in collects 15 distinct records elements from IBM i servers, together with issues like CPU and disk utilization, ASP utilization, number of energetic jobs, and lengthy-operating SQL jobs, amongst others. Some of these services are supported by the use of SNMP, while others require the open source JTOpen Java toolbox to be put in.

The Nagios server itself doesn’t run on IBM i – at the least now not yet. With the Nagios on IBM i plug-in, IBM gives clients with instructions on a way to installation Nagios Core or Nagios XI on a Linux server. Technically, there should still be nothing stopping from a user installing Nagios in a Linux LPAR on the vigour methods server – or even for IBM to port the software to the IBM i OS by means of PASE – but that has now not came about yet.

IBM makes the case for IBM i stores to use Nagios in a PDF presentation on Nagios created with the aid of IBM i company architect Tim Rowe and Wang Yun, who developed the Nagios for IBM i plug-in. whereas Navigator for i gives first rate insurance of IBM i-centric queues and metrics, that product doesn’t supply customers with any visibility of what’s taking place outdoor the IBM i equipment.

valued clientele who buy Nagios XI can video display IBM i metrics graphically.

Nagios, however, offers directors and operators with a single centralized view of all IT assets, Rowe and Yun write. in addition to the core IBM i metrics that at the moment are obtainable by the use of the Nagios plug-in, IBM i clients can get trend and ancient facts, as well because the flexibility to configure metrics to their liking, no longer to mention the potential to create their personal customized plug-ins.

IBM isn’t the handiest business doing Nagios work on IBM i. a few years ago, ShapPin Cheng launched a GitHub task called check_as400 that moved statistics from IBM i server logs into Nagios. considering that the extensibility and openness of Nagios, it wouldn’t be mind-blowing to peer independent software providers within the IBM i group undertake the technology for their personal programs monitoring choices.

For now, IBM offers the Nagios plug-in as a beta. Don’t be surprised to see it turn into a completely supported offering in the months to come, peculiarly on account that the love affair that IBM has with open supply application this present day.

connected reviews

IBM i performance secrets revealed

What’s Cooking With IBM i On GitHub?

ASCI Job Scheduler gets Hooks for Nagios


SAP joins a starting to be checklist of utility giants making large acquisitions in 2018: right here's a full rundown | killexams.com Real Questions and Pass4sure dumps

  • 2018 was the 12 months legacy tech groups bolstered their cloud organizations via significant acquisitions of application agencies.
  • SAP, IBM, Microsoft, Adobe and a couple of others all spent billions to purchase groups that supplied cloud, security and utility construction tools to their offerings.
  • beneath is a roundup of all of the largest offers thus far this year.
  • Legacy tech corporations have been scooping up smaller cloud and utility suppliers in big deals all the way through 2018.

    The consolidation comes as older commercial enterprise tech businesses strive to stay vital against the juggernaut of Amazon AMZN internet capabilities, as well as Google GOOGL 's smartly-funded cloud play and a rising crop of nimble project-backed beginning-ups.

    On Sunday, SAP grew to be the newest to enter the fray, paying $8 billion in money for survey utility business Qualtrics almost immediately before it planned to head public.

    SAP is in good enterprise. listed here are one of the crucial other large offers performed by way of large business gamers this yr:

    IBM purchased crimson Hat for $34 billion

    IBM IBM made a $34 billion all-money buy of open-source utility enterprise red Hat ultimate month . It became the third-largest U.S. tech merger ever and IBM's greatest acquisition.

    pink Hat will aid raise IBM in the turning out to be market for "hybrid cloud," where businesses run crucial business software in each their local data facilities and in hosted environments from the huge cloud suppliers like Amazon and Microsoft. In selected, crimson Hat's edition of the open-supply Linux working gadget runs on many clouds, together with these from Google and Microsoft, on the way to help IBM preserve bringing in revenue as businesses step into the cloud.

    Adobe bought Marketo for $4.seventy five billion

    Adobe ADBE purchased Marketo, a advertising utility company, for $four.seventy five billion in September . Adobe observed it will use Marketo's product for its Adobe experience Cloud platform for direct-to-customer advertising.

    Cisco purchased Duo protection for $2.35 billion

    Cisco CSCO announced in August that it will purchase cybersecurity business Duo safety in a $2.35 billion deal. It was Cisco's 2nd-biggest protection-related acquisition ever, and may support bolster the enterprise's starting to be safety enterprise.

    Broadcom purchased CA technologies for $18.9 billion

    Chip-maker Broadcom introduced it would buy commercial enterprise application enterprise CA technologies for $18.9 billion in July . The circulate came after Broadcom's $117 billion bid for rival Qualcomm became blocked through the Trump administration over security issues in March. The deal simply closed remaining week.

    Microsoft purchased GitHub for $7.5 billion

    Microsoft MSFT purchased the software development platform GitHub for $7.5 billion in inventory in June .

    The acquisition will aid Microsoft courtroom developers as it moves past windows and purchaser items to advertise its cloud and online functions offerings. Microsoft has considered its as soon as-dominant windows unit slip in market share in fresh years, spurring a cloud-first reorganization and sending Microsoft trying to find alternative routes to appeal to developers.

    Salesforce purchased Mulesoft for $6.5 billion

    Salesforce CRM got software company Mulesoft in a $6.5 billion money and inventory deal in March . Mulesoft makes software that helps company sew collectively lots of cloud functions. Salesforce will use Mulesoft's items to support clients sync information throughout apps observed in different locations, including quite a few cloud services and on-premises data facilities.

    Ari Levy and Jordan Novet contributed to this story.

    more From CNBC


    IBM Cloud continues to show design excellence | killexams.com Real Questions and Pass4sure dumps

    The IBM Cloud design crew is overjoyed to announce that two of our products, IBM facts Science event (DSX) and IBM App join, have got an iF Design Award 2018 within the Communications — software software category. each 12 months, the realm’s oldest impartial design corporation, iF international forum Design GmbH, organizes the iF Design Award. For sixty five years, the iF Design Award has been recognized as an arbiter of quality for superb design.

    IBM DSX and IBM App connect received over the sixty three-member jury, made of impartial specialists from far and wide the area, with their user-focused journey design for commercial enterprise software. The competition became severe: over 6,400 entries had been submitted from 54 countries in hopes of receiving the seal of great.

    This award is an extra fulfillment to 2 other design awards these days given to IBM Cloud, the red Dot Award and the Spark Award, and serves as an unequivocal indicator of the great of work produced via IBM Design and the way a great deal design has come to affect the total organization.

    The successful products

    IBM App join is a cloud-primarily based answer for business users that rapidly connects apps to automate initiatives and raise productivity. in its place of manually performing high-contact, repetitive integration tasks, it gives users a simple, enterprise-oriented interface to create workflows and get notifications, sync facts, music effects and extra. business determination makers that need to combine their business apps and combine their records commonly don’t have an easy option to accomplish that. They both write customized code or use complex integration platforms, which is time consuming, contains lots of software and requires deep middleware competencies. IBM App join changed into designed to permit users to seamlessly connect and integrate with again-conclusion techniques, internet, and mobile functions anyplace they live — no matter if on-premises or in a private or public cloud.

    we are enthusiastic about developing mind-blowing experiences for our clients, and it’s stunning being part of an eclectic group working collectively to make this a truth!” — Sarah Burwood, Design Lead & UX designer

    From a design point of view, the IBM App connect challenge provided an exhilarating opportunity to re-think about the combination event for brand spanking new clients, and follow design considering to this business context.

    “My proudest recollections of our App join experience naturally centre around the designers. As clients reacted to what we’d made, they accompanied and listened; then the entire IBM group moved as one to iterate and boost that user event.” — Brian Peaston, IBM Design supervisor, utility Integration

    IBM App join

    IBM statistics Science experience (DSX) is a cloud-based, imaginative, end-to-end journey for facts scientists to analysis, create, and collaborate across multidisciplinary groups. DSX merges a magnificent set of open source and legacy equipment with an online neighborhood for information scientists. at the time of its inception, there wasn’t a unified platform purchasable to information practitioners. Our simple mission turned into to make records essential and obtainable through our user-centered design procedure. The facts science box is complicated and continuously changing. DSX also continues to expand with the container, incorporating new, richer technologies as records scientists’ workflow and tools shift.

    “facts Science event is a true innovation, merging an impressive set of equipment with a web neighborhood. There are not any obstacles between creating, learning and sharing.” — David Townsend, Director of Design (IBM Analytics)

    IBM information Science eventIBM Design Takes a number one function in enterprise software Experiences

    enterprise utility is regularly looked at as the roach hotel of complex and clunky consumer experiences, a long way faraway from the modern, elegant and intuitive user experiences attainable in lots of consumer apps. although, “the times they are a-changin” as corporations are beginning to know how UX can really be a key differentiator in the enterprise application trade. There’s a growing to be consciousness that agencies should present enterprise and business utility that works as neatly and as intuitively because the other apps that their personnel use in their each day lives.

    enterprise software needs product experiences that enhance employee engagement, simplify complicated workflows, reduce turnover charges, eliminate the need for practising, and most significantly, boost person satisfaction and employee productivity.

    As a frontrunner within the enterprise software container, IBM has the capability to set a worldwide precedent of how design can affect products, users, and businesses on a huge scale. At IBM Design, we are supplying on that promise to convey sexy lower back to commercial enterprise utility.

    The 4 design awards for IBM Cloud items in the past 12 months, together with these two from iF Design, display the wonderful strides that IBM has made as a design-driven company, as well because the fundamental function that design performs in IBM’s cloud items. We accept as true with that persisted and unwavering focal point on our clients, and on the design satisfactory and consumer event of our products will enable IBM Cloud to proceed to excel, set precedents and differentiate by design.

    Awardees:

    IBM DSXDirector of Design: David Townsend (IBM Analytics, San Francisco)DSX Design group: Caroline law, Renee Mascarinas, Jessica Gore, Leila Johannesen (IBM Analytics, San Francisco)

    IBM App ConnectDesign supervisor: Brian PeastonApp connect Design team: Sarah Burwood, Gary Thornton, Rob Breeds, Danny Skinner, Lee Chase and Steve Haskey.


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    00M-641 exam Dumps Source : IBM Exceptional Web Experience Sales Mastery Test v1

    Test Code : 00M-641
    Test Name : IBM Exceptional Web Experience Sales Mastery Test v1
    Vendor Name : IBM
    Q&A : 50 Real Questions

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    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 our 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 our roundup. However, all three products received our 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 our 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 our 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.

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  • 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.

    Read Review
  • 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 we 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

  • Marketo® Once Again Named a Leader in The 2017 Gartner Magic Quadrant for CRM Lead Management Report | killexams.com real questions and Pass4sure dumps

    By PR Newswire

    Article Rating:

    September 15, 2017 09:00 AM EDT  

    SAN MATEO, Calif., Sept. 15, 2017 /PRNewswire/ -- Marketo, Inc., the leading provider of engagement marketing software and solutions, announced today that the company had been named a leader for the sixth consecutive year in the 2017 Gartner Magic Quadrant for CRM Lead Management1 report.   

    "We are thrilled and honored to be named a leader for the sixth consecutive year in Gartner's Magic Quadrant for CRM Lead Management report," said Matthew Zilli, group vice president, product and solution marketing, Marketo. "Marketo's only focus is to build innovative solutions that will enable marketers to engage in more meaningful ways with their buyers in today's digitally connected world. We believe Marketo's leadership position is justification that we are delivering on that commitment with superior technologies, product, and market strategy, and a partner ecosystem that will position marketers to win."

    Marketers continue to recognize the many innovative features Marketo possess including:

  • Marketo provides a scalable, open, unified, Engagement Platform with a robust set of applications including email, web, mobile, social, marketing automation, account-based marketing, and predictive content. Companies of all sizes have the ability to leverage these solutions to effectively engage with their customers across all channels.
  • Marketers continue to recognize Marketo's ability to react quickly to market trends and deliver innovative solutions that create competitive advantage for our customers, such as Marketo ABM. Earlier this year, Marketo, along with key partners, established the Alliance for ABM Success to help customers plan and execute their ABM strategy.
  • Marketo remains committed to providing access to the industry's most comprehensive and advanced set of capabilities for creating lead workflows. Additionally, Marketo's best-of-breed partner ecosystem, LaunchPoint®, integrates leading solutions into the Marketo Engagement Platform, enhancing the functionality of activities. 
  • According to Gartner, customer relationship management (CRM) lead management applications facilitate marketing and sales operations through a variety of steps. Initially, lead management applications manage the process of acquiring unqualified contacts and opportunities from a variety of sources, including: web session data, web registration pages, direct mail campaigns, email marketing campaigns, multichannel campaigns, database marketing and third-party lists, social media, and offline interactions such as tradeshows.

    For more information, click here to download the full Gartner Magic Quadrant for CRM Lead Management report.

    Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

    1Gartner, Magic Quadrant for CRM Lead Management, Jason Daigler, Ilona Hansen, August 22, 2017

    About MarketoMarketo, Inc., offers the leading Engagement Platform that empowers marketers to create lasting relationships and grow revenue. Consistently recognized as the industry's innovation pioneer, Marketo is the trusted platform for thousands of CMOs thanks to its scalability, reliability, and openness. Marketo is headquartered in San Mateo, CA, with offices around the world, and serves as a strategic partner to large enterprise and fast-growing organizations across a wide variety of industries. To learn more about the Marketo Engagement Platform, LaunchPoint® partner ecosystem, and the vast community that is the Marketing Nation®, visit www.marketo.com.

    Marketo logo. (PRNewsFoto/Marketo) (PRNewsFoto/)

    View original content with multimedia:http://www.prnewswire.com/news-releases/marketo-once-again-named-a-leader-in-the-2017-gartner-magic-quadrant-for-crm-lead-management-report-300520141.html

    SOURCE Marketo, Inc.

    Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

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