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M2040-641 - IBM Exceptional Web Experience Sales Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : M2040-641
Exam Name : IBM Exceptional Web Experience Sales Mastery Test v1
Questions and Answers : 50 Q & A
Updated On : February 20, 2019
PDF Download Mirror : Pass4sure M2040-641 Dump
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M2040-641 Questions and Answers

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M2040-641 IBM Exceptional Web Experience Sales Mastery Test v1

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M2040-641 exam Dumps Source : IBM Exceptional Web Experience Sales Mastery Test v1

Test Code : M2040-641
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

Ephox grants a fine wealthy textual content enhancing event at IBM's terrific net journey convention | killexams.com Real Questions and Pass4sure dumps

PALO ALTO, CA--(Marketwired - June 06, 2013) - Ephox (ephox.com), an extended-time IBM company accomplice, will exhibit how its rich text enhancing utility, EditLive!, can simplify the procedure of developing and contributing web content material at the 2013 IBM terrific web event experience. The conference, which specializes in how businesses can deliver engaging web experiences, takes place in Berlin, Germany, June 11-13, 2013. 

In January, Ephox unveiled EditLive! for IBM Connections, a rich text editor that lets Connections users easily create amazing content material. EditLive! for IBM Connections extends an extended partnership with IBM, during which EditLive! has empowered non-technical authors with the capability to create incredible content inside IBM web content supervisor.

"excellent content is the important thing to an outstanding internet experience," based on Tom Smith, Ephox VP of global earnings and advertising. "Ephox wealthy text modifying options help companies easily make a contribution greater content and accelerate the adoption of IBM Connections, ensuring they maximize their business outcomes."

EditLive! presents an excellent prosperous textual content enhancing experience which in turn empowers users to create fantastic content, even if or not it's in IBM Connections or IBM internet content supervisor. content authors may also be extra productive and contribute better first-rate content, therefore increasing the price of the platform and their company.

entertaining capabilities of the EditLive! prosperous textual content editor include: clean copy and paste from Microsoft office, inline spell checking and thesaurus, photograph import and enhancing, accessibility compliance checking, inline track changes and commenting, and damaged hyperlink checking.

in addition to a June 12 speakme session titled "increase ROI and Adoption through a superb editing journey," conference attendees can see a demo of EditLive! for IBM Connections and IBM net content manager on the Ephox sales space. 

About EphoxUsed by tens of millions of builders and authors global, Ephox's EditLive! prosperous textual content enhancing application specializes in reworking the internet production journey by means of improving the productiveness and great of site authoring and administration. The enterprise's expertise is licensed by means of leading net content management companies together with IBM, EMC|Documentum, and Open textual content. For more assistance, consult with ephox.com/EditLive, observe us on Twitter, or like our fb web page. 

Embedded Video accessible: http://youtu.be/caChTZQCxhQ


Sitecore® pronounces global Partnership with IBM iX to allow leading web content administration, Commerce, and marketing solutions | killexams.com Real Questions and Pass4sure dumps

Sitecore®, the  leader in digital journey administration utility, nowadays announced a new global partnership with IBM iX, some of the world's greatest digital companies and international business design companions. The partnership will make purchasable to purchasers Sitecore’s leading net content administration, commerce, and advertising options by means of IBM iX designers, know-how experts, and business strategists in 40 IBM Studios global. 

The expanded partnership brings collectively the total breadth of IBM iX’s capabilities to capitalize on the turning out to be demand for digital marketing capabilities that create totally-personalized consumer experiences across all digital touchpoints. 

Matthew sweet, world leader, IBM iX, pointed out that “client adventure is the key strategic purpose of many businesses and core to those corporations’ means to transform. i am very excited that we are expanding out our latest relationship with Sitecore into a world partnership, as they turn into an important player in our ecosystem of companions.” 

As a global Platinum accomplice within the Sitecore solution issuer program, IBM iX offers the realm-category consulting, design, development and implementation capabilities required to set up options on the Sitecore platform and deliver incredible results for consumers. Matched to Sitecore’s main digital event management capabilities, organisations can provide conclusion-consumers with seamless, omnichannel experiences to power differentiation, promote business transformation, and boost revenue and consumer lifetime value. The IBM iX and Sitecore partnership is additional empowered with top of the line practices and accelerators, as well because the skill to leverage the vigor of IBM Cloud and IBM Watson expertise. IBM iX additionally brings to endure the unparalleled talents of Bluewolf, an IBM company, creating experiences with Salesforce, with whom Sitecore has a strategic alliance. 

 


IBM iX and Sitecore® Launch world marketing functions and technology settlement | killexams.com Real Questions and Pass4sure dumps

TMCNet: IBM iX and Sitecore® Launch Global Marketing Services and Technology Agreement

IBM iX and Sitecore® Launch global advertising features and expertise settlement

Partnership to bring customer experiences that drive manufacturer loyalty and enhance competitive advantage

SAN FRANCISCO, Jan. 29, 2019 /PRNewswire/ -- Sitecore®, the international chief in digital journey administration utility, these days introduced a brand new global partnership with IBM iX, probably the most world's biggest digital agencies and international business design companions. The partnership will make attainable to valued clientele Sitecore's leading web content administration, commerce, and advertising options via IBM iX designers, technology specialists, and industry strategists in 40 IBM Studios international.

IBM iX is a confirmed leader in supplying Sitecore options, with greater than a decade's success of the two corporations working together in Europe to handle CMOs' crucial want for improved return on advertising and marketing funding. The accelerated partnership brings collectively the total breadth of IBM iX's capabilities to capitalize on the growing demand for digital advertising and marketing capabilities that create tremendously-customized customer experiences throughout all digital touchpoints.

Matthew candy, international chief, IBM iX, mentioned that "consumer experience is the important thing strategic aim of many agencies and core to tese corporations' capacity to transform. i am very excited that we're increasing out our present relationship with Sitecore into a world partnership, as they develop into an important player in our ecosystem of companions." 

As a world Platinum partner in the Sitecore solution provider software, IBM iX provides the world-classification consulting, design, construction and implementation functions required to install solutions on the Sitecore platform and carry extremely good consequences for shoppers. Matched to Sitecore's leading digital event administration capabilities, agencies can provide end-customers with seamless, omnichannel experiences to power differentiation, promote business transformation, and boost revenue and customer lifetime value. The IBM iX and Sitecore partnership is additional empowered with finest practices and accelerators, as well because the capacity to leverage the power of IBM Cloud and IBM Watson expertise. IBM iX additionally brings to bear the unparalleled knowledge of Bluewolf, an IBM enterprise, developing experiences with Salesforce, with whom Sitecore has a strategic alliance.

"IBM iX offers the strength, scale, and way of life of innovation required to deliver immersive, end-to-conclusion digital options for our joint purchasers," noted Mark Zablan, Chief income Officer for Sitecore. "Our partnership makes a mighty aggregate for agencies who want to speed up the digitization of their business and foster a consumer-centric strategy to digital transformation."

For more suggestions on IBM iX, consult with www.ibm.com/ibmix and observe @IBM_iX on Twitter.

About SitecoreSitecore is the global chief in digital event administration software that mixes content material management, commerce, and customer insights. The Sitecore journey Cloud™ empowers marketers to bring customized content in actual time and at scale throughout each channel—earlier than, during, and after a sale. more than 5,200 manufacturers––including American specific, Carnival Cruise lines, Dow Chemical, and L'Oréal––have depended on Sitecore to deliver the customized interactions that delight audiences, construct loyalty, and pressure revenue.  

ContactMatt KrebsbachSr. Director, Public & Analyst members of the family at Sitecorematt.krebsbach@sitecore.com

Sitecore Media RelationsWE CommunicationsTeamSitecore@we-global.com

Sitecore®, personal the event®, Sitecore adventure Cloud™, Sitecore xConnect™, Sitecore Cortex™, Sitecore® experience Platform™, Sitecore experience manager™ and Sitecore® journey Database™ are registered emblems or trademarks of Sitecore corporation A/S in the united states and different nations. All different company names, product names or trademarks belong to their respective holders.

brand - https://mma.prnewswire.com/media/334493/sitecore_logo.jpg

source Sitecore

[ Back to the Next Generation Communications Community's Homepage ]


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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 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.

    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.

    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

  • Ask HN: Is it possible for someone to not be cut out for software engineering? | killexams.com real questions and Pass4sure dumps

    I'm pretty new to software engineering (~1 year out of college as a CS major) and am having some serious problems improving my skillset (particularly with interviews).

    Background:

    - I worked as a linux sysadmin at a startup before going to school for CS. After 3 years, I graduated with a decent GPA (>3.6) and a couple internships at a large tech company.

    - In college, I always felt like I had to work a harder than most of my classmates in order to get the same result. I would regularly spend 60 - 80 hours per week on schoolwork.

    - Upon graduation, I accepted a job with the company I interned with and have worked there for ~1.3 years. However, I've been prepping to jump to a different role for a few months now.

    - Recently (past 1.5 months), I've spent 1 - 3 hours per day on tech interview prep using CLRS, Cracking the Coding Interview, etc. My current target is a role at one of the "big four" tech companies or a high-quality startup doing either webdev or infrastructure engineering.

    - So far, I have interviewed for and been rejected by no less than 10 different roles. I was also rejected by approximately 20 companies during college. I always fail during tech portions.

    - I am REALLY bad at algorithm problems and experience serious performance anxiety. Concentrated studying and mass interviewing haven’t led to positive results so far. I can solve most problems correctly given enough time (usually 2x+ longer than interviews).

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    1.) Does there ever come a point where it just isn't worth it to continue trying to be a software engineer who can get into top-tier companies / projects?

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    3.) If my goal isn't an impossibility, how can I efficiently progress towards it? Would a mentor be helpful?


    Data Center Linux | killexams.com real questions and Pass4sure dumps

    In recent years, the adoption of Linux in the data center has progressed beyond infrastructure services such as e-mail and file, print, and Web serving. Today, Linux is widely used as a business application server and is moving deeper into the data center as a database and content server. Given a vibrant development community and innovative solution providers, Linux continues its advance toward becoming the enterprise-computing platform of choice.

    The benefits of Linux to data center customers are well known: superior economics, no vendor lock-in, reliability, and increasing independent software vendor (ISV) and system vendor acceptance and support.

    However, with success comes higher expectations. User requirements for Linux in the data center continue to escalate. IT professionals are always looking for more performance, manageability, and other features that they've come to expect from an enterprise operating system. However, the user requirements list is as varied as it is long. Lacking a "center of gravity" where developers, users, and vendors can all look at Linux capabilities and requirements together, the necessary advances will come more slowly.

    Enter OSDL Data Center Linux Working Group (DCL). The DCL is a virtual center-of-gravity where interested parties can come together to accelerate the development and adoption of Linux in the enterprise. Made up of a number of OSDL member companies and other interested individuals, the DCL focuses on services, databases, applications, and mid-tier and high-end multiprocessor servers used for a variety of mission-critical applications.

    Accelerating Linux Adoption Overall, the OSDL DCL focuses on all the things you'd expect from an organization trying to accelerate the deployment of any enterprise software, namely:
  • Meet or exceed performance and availability currently expected for data center applications
  • Scale to enterprise-class server hardware environments
  • Meet or exceed currently accepted security standards
  • Provide standards that encourage end-user, ISV, and third-party software adoption
  • Yield a high degree of usability for activities requiring human interaction
  • In this early phase of the DCL's work, it is taking a two-part approach to accomplishing its objective. First is to identify capabilities required for accelerated Linux adoption in larger organizations. Second, recommend technical approaches to meet those requirements.

    Periodically, the DCL working group also publishes capabilities requirements documents for public review. The 1.0 version of the OSDL DCL Capabilities Document can be found on OSDL's Web site under DCL Documents. The intent of this and future DCL documents is to stimulate discussion and review, then drive toward future, specific technical requirements from the list of capabilities. Thus, the capabilities document is neither a list of requirements nor a specification, but is an analysis of the enterprise usage needs of Linux, thus providing a starting place for driving efforts that facilitate building solutions to satisfy those needs.

    The DCL identifies two levels of technical priorities for Linux in the data center: Priority One Capabilities are considered the most important for data center readiness for Linux, while Priority Two Capabilities are those intended to stimulate thought and discussion.

    Beyond general priorities of importance, the DCL working group examines Linux enterprise capabilities in the traditional functional areas:

  • Scalability
  • Performance
  • Reliability, availability, serviceability (RAS)
  • Manageability
  • Clusters
  • Security
  • Usability
  • Standards
  • The standards category references specifications controlled outside DCL. This category includes only capabilities with standards related to adopting Linux in data centers.

    At this stage, the OSDL DCL investigation is focused on enterprise-level usage models. The technical discussions center around data center issues such as security, storage networking, and file system performance. Because the objective of the DCL is to accelerate the maturation of Linux as an enterprise operating system, investigation of the other features on this list concentrates on server functionality and data center administration needs. Clearly, most of the features on this list are equally important to all who develop, use, or administer the system. But these nonserver-related issues are addressed in other OSDL initiatives such as the Desktop Linux working group.

    In the latter half of 2004 the DCL Working Group will move on to its second objective: documenting the requirements necessary to advance Linux further in the data center, and recommending technical approaches to meet those requirements.

    Call for Participation The mission of the Data Center Linux (DCL) Working Group is to provide a forum for industry leaders to accelerate adoption and deployment of Linux in data centers.

    The working group has completed its first pass at identifying Linux capabilities in the data center, and has developed a prioritized list of capabilities needed. The Data Center Linux Technical Capabilities v1.0 document is available for public review on the OSDL Web site. We encourage any interested person to review it and provide input. Together we can all help Linux become the enterprise operating system of choice.

    For more information on how you can participate in the DCL, please visit www.osdl.org/lab_activities/data_center_linux.

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    IBM M2040-641 Exam (IBM Exceptional Web Experience Sales Mastery Test v1 ) Detailed Information



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