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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

Ephox grants an exceptional wealthy text enhancing adventure at IBM's tremendous net journey conference | killexams.com Real Questions and Pass4sure dumps

PALO ALTO, CA--(Marketwired - June 06, 2013) - Ephox (ephox.com), a protracted-time IBM business associate, will show how its prosperous textual content editing utility, EditLive!, can simplify the procedure of developing and contributing web content on the 2013 IBM great internet experience adventure. The convention, which makes a speciality of how businesses can bring enticing internet experiences, takes area in Berlin, Germany, June eleven-13, 2013. 

In January, Ephox unveiled EditLive! for IBM Connections, a rich textual content editor that lets Connections users with no trouble create top notch content. EditLive! for IBM Connections extends a protracted partnership with IBM, throughout which EditLive! has empowered non-technical authors with the capability to create fantastic content material within IBM internet content material manager.

"wonderful content material is the key to a good internet adventure," according to Tom Smith, Ephox VP of international income and marketing. "Ephox wealthy textual content editing solutions support agencies conveniently make a contribution more content and accelerate the adoption of IBM Connections, ensuring they maximize their business outcomes."

EditLive! presents a fine prosperous text modifying journey which in flip empowers users to create notable content material, whether or not it's in IBM Connections or IBM net content supervisor. content authors can also be greater productive and make a contribution bigger exceptional content material, therefore expanding the cost of the platform and their company.

interesting capabilities of the EditLive! rich text editor include: clear copy and paste from Microsoft office, inline spell checking and thesaurus, photo import and enhancing, accessibility compliance checking, inline track alterations and commenting, and broken hyperlink checking.

moreover a June 12 speakme session titled "increase ROI and Adoption through a good modifying adventure," conference attendees can see a demo of EditLive! for IBM Connections and IBM net content material supervisor at the Ephox sales space. 

About EphoxUsed by using millions of developers and authors global, Ephox's EditLive! wealthy textual content enhancing software focuses on reworking the web construction journey via bettering the productivity and best of website authoring and management. The enterprise's technology is licensed by using main web content administration carriers including IBM, EMC|Documentum, and Open text. For greater suggestions, consult with ephox.com/EditLive, observe us on Twitter, or like our fb page. 

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


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5 collaboration tools that raise Microsoft office

Perficient Named IBM 2019 Watson Commerce business associate of the 12 months | killexams.com Real Questions and Pass4sure dumps

Perficient, Inc. PRFT, +0.ninety four% (“Perficient”), a leading digital transformation consulting firm serving global 2000® and different tremendous enterprise clients during North the usa, announced it has been named IBM’s 2019 Watson Commerce business companion of the year. The IBM Excellence Award, introduced right through IBM’s PartnerWorld at think 2019, recognizes Perficient’s ongoing boom and relationships with key shoppers, and notion management across the IBM Watson client Engagement Commerce platform as an imperative part for digital transformation.

“Our approach to commerce is concentrated on crafting a experience, connecting with customers, and supplying a seamless customer experience throughout channels and during the business, imperatives in today’s purchaser-pushed world,” spoke of Steve Gatto, countrywide revenue director, Commerce options, Perficient Digital. “together, with our purchasers, we’re transforming companies in a way that now not handiest drives increase however strengthens their usual company, and we always evolve our offerings to preserve customers on the excellent of their online game. We’re honored to be recognized by means of IBM, and we’re eager for sharing our ingenious options all the way through IBM suppose 2019.”

Perficient Digital Takes Commerce options beyond Transactions to radically change the client Lifecycle for a world diverse brand

With branded producers and distributors under drive from the dramatic shift to on-line deciding to buy, a global varied company sought to digitally radically change its commerce company. In partnership with Perficient Digital, the two establishments delivered optimized customer sales, updated product suggestions (PIM), and streamlined the ordering process via building of a B2B portal. With the implementation of IBM’s Sterling Order administration equipment (OMS), and Perficient’s expertise, the diverse manufacturer is future-proofing its company to align with trade tendencies and market alternatives.

additionally, the enterprise’s OMS will give them stronger flexibility in managing complex order administration scenarios, superior reliability so as processing and fulfilment, and a value reduction in implementing across its enterprise. it will further enable the firm to convey carrier enhancements to its valued clientele, optimize its pricing, promoting and basic provide chain, boost income as a result of enhanced stock visibility, and cut back fees via improved efficiencies in order visibility.

Perficient Digital Enhances the on-line customer experience for a leading fabric Retailer

In a market that has traditionally trusted brick-and-mortar experiences, a number one textile and craft retailer become challenged with extending the customer experience online. Perficient partnered with the business to put into effect an IBM Watson Commerce answer that supplied up-to-date visibility of its inventory and improved monitoring of its product amount, region, and availability. applying IBM Order management, Perficient additional more desirable the answer through cloud migration that presents a single view of supply and demand, orchestrates order fulfillment strategies across buy on-line Pickup In store (BOPIS) and Ship-from-save (SFS), and empowers business representatives to greater serve consumers each in call facilities and in-keep engagements.

“Perficient has been deploying IBM Commerce solutions for essentially 20 years, presenting conclusion-to-end digital commerce solutions that embody dissimilar channels, and deliver seamless and efficient experiences throughout their total enterprise,” spoke of Sameer Peera, frequent supervisor, Perficient’s commerce observe. “With the recent information that HCL took over construction of IBM WebSphere Portal, IBM internet content material management and web event factory, our consumers proceed to engage us for support with their digital commerce techniques. We’re happy to be their go-to companion as they navigate the changing market landscape and deliver for his or her clients.”

Perficient potential in action at IBM think 2019

moreover its award-winning commerce answer talents, Perficient consultants are available all over the IBM believe 2019 convention in sales space #320 to talk about its experience and potential across the IBM portfolio , especially cloud, cognitive, statistics, analytics, DevOps, IoT, content management, BPM, connectivity, commerce, cell, and consumer engagement.

while IBM has announced its plans to sell its commerce portfolio, the information of its acquisition of red Hat additionally signaled the criticality cloud building and start play in a success end-to-end digital transformations. As an IBM global Elite accomplice, one of most effective seven companions with that reputation globally, and a pink Hat Premier companion, Perficient is well located to work with each corporations through this transition. And, our specialists might be available right through IBM consider to discuss how to navigate the cloud market, share key client success reviews, and supply strategic skills on the opportunities ahead for clients.

“know-how is changing so all of a sudden, and firms should retain pace or face disruption,” pointed out Hari Madamalla, vice chairman, emerging options, Perficient. “With potential and event in all features of the commerce journey, to main cloud, internet hosting, managed capabilities and aid options, enterprises turn to Perficient as a go-to associate for their digital transformations.”

be part of several Perficient subject count number experts and our consumers as they current right through six IBM think sessions, together with:

As a Platinum IBM business partner, Perficient holds greater than 30 awards throughout its 20-year partnership historical past. The business is an award-profitable, certified software price Plus solution issuer and one of the few companions to receive dozens of IBM knowledgeable degree software competency achievements.

For updates all over the adventure and after, join with Perficient specialists on-line with the aid of viewingPerficient and Perficient Digital’s blogs, or follow us on Twitter@Perficient and @PRFTDigital.

About Perficient

Perficient is the main digital transformation consulting firm serving world 2000® and commercial enterprise valued clientele all over North the us. With unparalleled information know-how, management consulting, and artistic capabilities, Perficient and its Perficient Digital agency convey vision, execution, and cost with spectacular digital adventure, enterprise optimization, and trade options. Our work permits consumers to improve productivity and competitiveness; develop and reinforce relationships with shoppers, suppliers, and companions; and reduce prices. Perficient's experts serve purchasers from a community of workplaces throughout North the us and offshore places in India and China. Traded on the Nasdaq world opt for Market, Perficient is a member of the Russell 2000 index and the S&P SmallCap 600 index. Perficient is an award-successful Adobe Premier companion, Platinum stage IBM company associate, a Microsoft country wide service company and Gold CertifiedPartner, an Oracle Platinum companion, an advanced Pivotal in a position partner, a Gold Salesforce Consulting associate, and a Sitecore Platinum companion. For more tips, visitwww.perficient.com.

safe Harbor remark

one of the statements contained during this news release that don't seem to be merely historical statements talk about future expectations or state other ahead-looking suggestions involving fiscal effects and business outlook for 2018. those statements are subject to common and unknown hazards, uncertainties, and other components that could trigger the actual outcomes to differ materially from these contemplated with the aid of the statements. The ahead-looking counsel is in keeping with management’s current intent, belief, expectations, estimates, and projections regarding our enterprise and our business. make sure you be aware that these statements best mirror our predictions. precise movements or effects may additionally range noticeably. critical components that could cause our precise results to be materially distinctive from the forward-looking statements encompass (but aren't constrained to) those disclosed beneath the heading “chance components” in our annual file on form 10-k for the year ended December 31, 2017.

View supply edition on businesswire.com: https://www.businesswire.com/news/domestic/20190212005973/en/

supply: Perficient, Inc.

Ann Higby, PR manager, Perficient, 314-219-8836ann.higby@perficient.com

Copyright company Wire 2019


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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).

    Questions:

    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?

    2.) How can I find some positive reinforcement in interviewing / interview prep even if I constantly get rejected? I do perform post mortems on every interview in order to find areas to improve.

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


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