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

Test Code : 00M-641
Test designation : IBM Exceptional Web experience Sales Mastery Test v1
Vendor designation : IBM
: 50 actual 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 actual Questions and Pass4sure dumps

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

In January, Ephox unveiled EditLive! for IBM Connections, a loaded textual content editor that lets Connections users with no difficulty 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 bizarre content material within IBM internet content material manager.

"wonderful content material is the key to a expedient internet adventure," according to Tom Smith, Ephox VP of international income and marketing. "Ephox wealthy textual content editing solutions support agencies conveniently develop a contribution more content and accelerate the adoption of IBM Connections, ensuring they maximize their commerce 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 develop a contribution bigger exceptional content material, therefore expanding the cost of the platform and their company.

interesting capabilities of the EditLive! loaded text editor include: limpid 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 expedient 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 infatuation their fb page. 

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


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

Perficient, Inc. PRFT, +0.ninety four% (“Perficient”), a leading digital transformation consulting solid serving global 2000® and different tremendous enterprise clients during North the usa, announced it has been named IBM’s 2019 Watson Commerce commerce companion of the year. The IBM Excellence Award, introduced birthright through IBM’s PartnerWorld at deem 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 portion 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 their purchasers, we’re transforming companies in a way that now not handiest drives enlarge however strengthens their usual company, and they always evolve their offerings to preserve customers on the excellent of their online game. We’re honored to be recognized by means of IBM, and we’re involved for sharing their ingenious options whole 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 stagy 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 pile of a B2B portal. With the implementation of IBM’s Sterling Order administration tackle (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 intricate order administration scenarios, superior reliability so as processing and fulfilment, and a value reduction in implementing across its enterprise. it will further enable the solid 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 slash 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 commerce to Put into result an IBM Watson Commerce retort 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 retort 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 commerce representatives to greater serve consumers each in convene 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, their consumers proceed to engage us for support with their digital commerce techniques. We’re providential 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 deem 2019

moreover its award-winning commerce retort talents, Perficient consultants are available whole 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 pile and start play in a success end-to-end digital transformations. As an IBM global Elite accomplice, one of most efficient 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, their specialists might be available birthright through IBM deem to debate 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 whole of a sudden, and firms should retain pace or puss disruption,” pointed out Hari Madamalla, vice chairman, emerging options, Perficient. “With potential and event in whole 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 portion of several Perficient subject import number experts and their consumers as they current birthright through six IBM deem sessions, together with:

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

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

About Perficient

Perficient is the main digital transformation consulting solid serving world 2000® and commercial enterprise valued clientele whole 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. Their work permits consumers to ameliorate 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 tidings release that don't seem to be merely historical statements talk about future expectations or state other ahead-looking suggestions involving fiscal effects and commerce outlook for 2018. those statements are subject to common and unknown hazards, uncertainties, and other components that could trigger the actual outcomes to vary 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 their enterprise and their business. develop sure you be watchful that these statements best mirror their predictions. precise movements or effects may additionally compass noticeably. faultfinding components that could intuition their 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 their annual file on configuration 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 commerce Intelligence (BI) Tools of 2018 | killexams.com actual questions and Pass4sure dumps

Analytics Beyond Spreadsheets

For many years, Microsoft outdo and other spreadsheets were the tools of election for commerce professionals who were looking to visualize their data. But spreadsheets had their limits for many commerce intelligence (BI)-related tasks. Even today, trying to creating charts analyzing intricate datasets in outdo can still be frustrating. Sometimes you start with the wrong benign 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 requisite has arisen for efficient tools that people of whole skill levels can exercise to develop 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 well-organized rows and columns. And, if you contain millions of rows or very sparse matrices, then the data in a spreadsheet can be painful to enter and it can be difficult to visualize your data. Spreadsheets also contain 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 selfsame spreadsheet.

A spreadsheet containing up-to-the-minute data can also be a problem, particularly if you contain exported graphics that requisite to be refreshed when the data changes. Finally, spreadsheets aren't expedient 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 develop exercise 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 panoply calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is whole about the math more than the model itself.

This is whole a long-winded way of maxim that when businesses exercise a spreadsheet, they are actively sabotaging themselves and their skill to consistently come by valuable insights from their data. BI tools are specficially designed to relieve businesses better understand their data, and can prove to be a huge capitalize to those upgrading from what a limited spreadsheet can do.

What Is commerce Intelligence?

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

BI is an umbrella term meant to cover whole 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 being and operations. The ultimate goal is being able to enlarge 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 shrink 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 result 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 know-how 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 information about the business, competitors, customers, industry, market, and suppliers, to designation just a few feasible metric targets. But as businesses grow and their information stores balloon, the capturing, storing, and organizing of information becomes too large and intricate to be entirely handled by mere humans. Early efforts to result 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 dissect raw data, today it usually refers to analytics for data mining, analytical processing, querying, reporting, and especially visualizing. The main disagreement between today's BI software and mammoth Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from small to large. mammoth Data analytics and apps manipulate 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 accepted half a decade or more ago required specialists, not just to exercise 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 come by and understand the business—the company conclusion 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 commerce 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 rush 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 current trend among current 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 conclusion 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 odds of this distributed BI model. Key tools and faultfinding data contain moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with current BI software. This results in a whole current benign of analysis becoming available to the organization, namely, that of experienced, front-line commerce people who not only know what data they requisite but how they requisite to exercise it.

The emerging crop of BI tools whole work difficult at developing front-end tools that are more intuitive and easier to exercise than those of older generations—with varying degrees of success. However, that means a key criteria in any BI tool purchasing conclusion 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 witness they're looking for their tool suites to become as ubiquitous and simple to exercise for commerce users as typical commerce collaboration tools or productivity suites, such as Microsoft Office. not anyone contain 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 occur now), and predictive (what will occur later).

What Is Data Visualization?

In the context of BI software, data visualization is a quickly and efficient routine of transferring information from a machine to a human brain. The view is to space digital information into a visual context so that the analytic output can be quickly ingested by humans, often at a glance. If this sounds infatuation 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 attain more granular information on one or more aspects incorporated in the bigger picture. For example, current 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 result not seek artistic awards but instead are designed with duty 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 ogle impressive at first glance but, if your audience needs relieve 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 commerce analyst. But I also kept in intellect the viewpoint of users who might contain no familiarity with data processing or analytics. I loaded and used the selfsame data sets and posed the selfsame queries, evaluating results and the processes involved.

My point was to evaluate cloud versions alone, as I often result analysis on the soar or at least on a variety of machines, as result 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 case of this is Tableau Desktop, a favorite tool of Microsoft outdo users who simply contain an affinity for the desktop tool (and who just lumber 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 pretense 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 unadulterated cloud player. The tool is designed to dissect 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 create them, simply to let buyers know they exist. IBM Watson Analytics is one tool with the skill to extend to highly advanced features and was also one of the easiest to exercise upfront. IBM Watson Analytics is well-suited for commerce analysts and for widespread data democratization because it requires little, if any, information of data science. Instead, it works well by using natural language and keywords to configuration queries, a characteristic that can develop it valuable to practically anyone. It's highly intuitive, very powerful, and simple 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 commerce users. However, there are several other powerful and intuitive apps in this lineup from which to choose; they whole contain 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 manipulate streaming data. For many users, that won't be a problem in the immediate future. However, for those involved with analyzing commerce 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 develop streaming data and streaming analytics a must-have feature. Many of these tools will contain to up their game accordingly so, unless you want to jump ship in a year or two, it's best to deem ahead when considering BI and the IoT.

BI and mammoth Data

Another zone in which self-service BI is taking off is in analyzing mammoth Data. This is a newer evolution in the database space but it's driving tremendous growth and innovation. The designation is an apt descriptor because mammoth Data generally refers to huge data sets that are simply too mammoth 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 designation a few) that contain become so accepted over the last several years.

Not only result these tools generate loads of current data, they also often generate a current benign 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 benign of data is cumbersome 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 benign of data comprehend the customer behavior-tracking tools you exercise 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 current information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. current 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 mammoth 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 style (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query mammoth Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query whole of your commerce data for maximum insight, then Hadoop is what you need.

You can download and implement Hadoop itself to fulfill your queries, but it's typically easier and more efficient to exercise 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, whole support this. However, each requires varying levels of configuration or even add-on tools to result so—with IBM, Microsoft, and Tableau offering exceptionally abysmal capabilities. However, both IBM and Microsoft will still anticipate customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

Finding the birthright BI Tool

Given the issues spreadsheets can contain when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the birthright BI tool isn't a simple process. Unlike spreadsheets, BI tools contain 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 develop exercise of their features. Finally, to develop matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to pretense 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 contain 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 contain been taking odds 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 well-known to develop sure that your employees actually know how to exercise these BI platforms so that you can develop sure your investment was worthwhile. There are many ways of approaching this, but using the birthright online learning platform might be a expedient space to start looking.

Given the wide cost compass of these products, you should segment your analytics needs before you develop 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 develop it simple to come by started. Plus, they tend to contain 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 confederate network, though both IBM Watson Analytics and Microsoft Power BI are catching up fast.

IBM Watson Analytics scored the highest, and Microsoft Power BI and Tableau Desktop scored the next highest in their roundup. However, whole three products received their Editors' election award. Tableau Desktop may contain a mammoth cost tag depending on which version you pick but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to pledge some exertion to it. Microsoft Power BI and Tableau Desktop also contain large and growing collections of data connectors, and both Microsoft and Tableau contain their own sizable communities of users that are vocal about their wants and needs. This can carry a lot of weight with the vendors' evolution teams so it's a expedient view to spend some time looking through those community forums to come by an view where these companies are headed.

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

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

    Bottom Line: Zoho Reports is a solid option for generic commerce 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 quickly analytics. Robust natural language querying.

    Cons: Unable to result real-time streaming analytics.

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

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

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

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

    Read Review
  • Pros: colossal collection of data connectors and visualizations. User-friendly design. Impressive processing engine. ripen 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 ripen 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 commerce 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 accepted for experienced BI users. It may Fall short for beginners, however.

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

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

    Bottom Line: Domo isn't for newcomers but for companies that already contain commerce 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 designation in website and mobile app intelligence. It has a steep learning curve but it is an awesome commerce intelligence tool.

    Read Review
  • Pros: Designed with generic commerce users in mind. Solid return on investment.

    Cons: The data you can exercise 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 develop 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 develop 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 commerce already uses SAP's HANA database platform or some of its other back-end commerce platforms, then SAP Analytics Cloud is a powerful, well-priced choice. But be warned that there's a steep learning curve and a renowned dependence on other SAP products for full functionality.

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

    Cons: Poorly designed user interface. Steep learning curve.

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

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

    Cons: Very expensive. Not for small teams.

    Bottom Line: Looker is a distinguished self-service commerce intelligence (BI) tool that can relieve unify SQL and mammoth Data management across your enterprise.

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

    Cons: intricate pricing is a deterrent.

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

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

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

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

    Read Review
  • Pros: Very simple to come by started. Nice team management and collaboration features.

    Cons: The cloud version has a subset of features create 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 commerce intelligence (BI) tool Tibco Spotfire is a distinguished way to start visualizing your outdo data.

    Read Review
  • Pros: Excellent analytical support for Intuit QuickBooks. Very simple 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 commerce intelligence (BI) tool for in-depth analysis of your Intuit QuickBooks files, though you'll requisite to ogle elsewhere for broader BI tasks.

    Read Review

  • Ask HN: Is it feasible for someone to not be slash out for software engineering? | killexams.com actual questions and Pass4sure dumps

    I'm pretty current 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 pair internships at a large tech company.

    - In college, I always felt infatuation I had to work a harder than most of my classmates in order to come by the selfsame 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 contain 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 contain 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 atrocious 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 further a point where it just isn't worth it to continue trying to be a software engineer who can come by into top-tier companies / projects?

    2.) How can I find some positive reinforcement in interviewing / interview prep even if I constantly come by rejected? I result fulfill 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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