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00M-226 | IBM Smart Analytics Sales Mastery Test v1

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00M-226 - IBM Smart Analytics Sales Mastery Test v1 - braindump

Vendor IBM
Exam Number 00M-226
Exam Name IBM Smart Analytics Sales Mastery Test v1
Questions 34 Q & A
Recent Update March 18, 2019
Free PDF Download 00M-226 Brain Dump
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00M-226 exam Dumps Source : IBM Smart Analytics Sales Mastery Test v1

Test Code : 00M-226
Test Name : IBM Smart Analytics Sales Mastery Test v1
Vendor Name : IBM
Q&A : 34 Real Questions

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IBM IBM Smart Analytics Sales

IBM (IBM) Up three.9% since remaining profits document: Can It proceed? | killexams.com Real Questions and Pass4sure dumps

A month has gone by in view that the remaining income report for IBM (IBM). Shares have brought about three.9% in that point body, underperforming the S&P 500.

Will the contemporary high-quality fashion proceed leading up to its next income unlock, or is IBM due for a pullback? before we dive into how buyers and analysts have reacted as of late, let's take a short look on the most contemporary profits file so as to get a much better address on the vital drivers.

IBM q4 salary Beat Estimates, Revenues Decline Y/Y

international business Machines Corp delivered fourth-quarter 2018 non-GAAP earnings of $4.87 per share, which beat the Zacks Consensus Estimate of $four.81 per share. despite the fact, salary per share (EPS) reduced 5.9% from the yr-ago quarter. The year-over-yr decline in EPS can be attributed to greater tax fee.

Revenues of $21.76 billion have been almost based on the Zacks Consensus Estimate of $21.seventy four billion and declined three.5% on a yr-over-12 months groundwork. At steady foreign money (cc), revenues dipped 1%. The year-over-yr decline can basically be attributed to forex fluctuation and headwinds from IBM Z product cycle.

mainly, IBM cited that signings surged 21% on cc groundwork to $15.eight billion. functions backlog declined 1% year over year and got here in at $116 billion.

Geographic earnings details

Revenues from Americas were down four%, reflecting the headwind from the IBM Z product cycle. besides the fact that children, persisted growth in Latin the us become a favorable.

Europe, middle-East and Africa elevated 2% from the 12 months-in the past quarter exceptionally due to boom in Spain, Germany, Italy and the U.okay.

Asia-Pacific revenues declined 1% on a year-over-year foundation with modest increase in Japan.

Strategic Imperatives growth Continues

Strategic Imperatives (cloud, analytics, mobility and security) grew 5% at cc from the 12 months-in the past quarter to $eleven.5 billion. except IBM Z product cycle affect Strategic Imperatives grew 11% yr over year.

safety revenues surged 17% (apart from IBM Z product cycle have an effect on) and declined 3% on cc foundation. On a trailing 12-month foundation, Strategic Imperatives revenues had been $forty billion, up 9%.

Cloud revenues surged 6% from the 12 months-ago quarter to $5.7 billion and 19% (excluding IBM Z product cycle have an impact on). The annual run rate for cloud as-a-carrier revenues accelerated 21% at cc on a year-over-12 months foundation to $12.2 billion.

Cloud revenues of $19.2 billion on a trailing 12-month groundwork multiplied 12% year over 12 months.

Cognitive Revenues Surge

Cognitive solutions’ revenues-external elevated 2% year over year (on cc groundwork) to $5.5 billion. Revenues from Cognitive solutions (together with solutions application and transaction processing) improved basically because of increase in solutions utility, together with analytics and artificial intelligence (AI).

Segmental revenues concerning Strategic Imperatives and Cloud multiplied 9% and 5%, respectively. Cloud as-a-service salary annual run rate changed into $2 billion.

options software includes choices in strategic verticals like fitness, domain-particular capabilities like analytics and security, and IBM’s rising technologies of AI and blockchain. The section also includes offerings that handle horizontal domains like collaboration, commerce and ability. within the stated quarter, options software revenues multiplied three% year over year.

IBM brought up that the combination of AI into offerings like consumer experience analytics in commerce domain helped SaaS signings to develop in double digit in the mentioned quarter.

Transaction Processing software includes application that runs mission-essential workloads, leveraging IBM’s hardware platforms. Revenues have been up 1% on a 12 months-over-12 months foundation.

IBM witnessed increase in industry verticals like fitness, key areas of analytics and security in the quarter. Watson fitness witnessed vast-primarily based growth in Payer, company, Imaging and life Sciences domains.

during the fourth quarter, IBM increased partnership with Vodafone neighborhood. Per the deal, IBM’s superior hybrid cloud platform, AI, internet of issues (“IoT”) capabilities will support Vodafone company with digital transformation initiatives.

IBM stated that analytics performed smartly within the quarter, driven through records science choices and IBM Cloud deepest for data providing.

security increase was driven by means of offerings in orchestration, records safety and endpoint management.

Story continues

In blockchain, IBM announced addition of a few new shoppers right through the quarter, which contains “work with sensible Dubai on the middle East’s first government-counseled blockchain platform.” The business also unveiled an on-prem offering during the reported quarter, the IBM Blockchain Platform for IBM Cloud private. a couple of new deal wins is also helping IBM to improve its foothold in blockchain know-how.

world company capabilities Revenues boost

Revenues from global enterprise services-exterior segment were $four.3 billion, up 4% from the 12 months-in the past quarter (up 6% at cc). The yr-over-yr increase was essentially because of growth throughout all three business areas specifically consulting, utility management and global procedure capabilities.

Segmental revenues concerning Strategic Imperatives grew 14%. Cloud practice surged 34%. Cloud as-a-service income annual run expense changed into $2.1 billion.

utility administration revenues accelerated four% from the year-ago quarter. world process services revenues climbed 5%. moreover, Consulting revenues improved 10% yr over 12 months, pushed by means of powerful efficiency from IBM’s digital business.

technology features & Cloud structures: Revenues Dip

Revenues from expertise features & Cloud structures-external reduced 3% from the 12 months-ago quarter (flat at cc) to $eight.9 billion. Segmental revenues relating Strategic Imperatives advanced 13%, pushed with the aid of hybrid cloud services. Cloud surged 22% from the yr-ago quarter. Cloud as-a-service revenue annual run expense changed into $8 billion.

Integration utility improved four% from the 12 months-in the past quarter. during the said quarter, greater than 100 agencies all over selected IBM Cloud private providing. Infrastructure capabilities revenues were flat on a 12 months-over-12 months foundation.

Technical help functions revenues decreased 3% from the yr-in the past quarter.

energy & z14 drive systems Revenues

programs revenues diminished 21% on a 12 months-over-year foundation (down 20% at cc) to $2.6 billion, essentially as a result of influence of the IBM Z product cycle. Segmental revenues bearing on Strategic Imperatives plunged 22%, whereas Cloud revenues declined 31%.

IBM Z revenues decreased 44% yr over yr. despite the fact, MIPS means has multiplied around 20%, pushed by way of extensive-based mostly adoption of the z14 mainframe.

vigour revenues extended 10% from the 12 months-in the past quarter. The upside become chiefly due to Linux and mighty adoption across the newest POWER9-based mostly architecture.

all through the fourth quarter, IBM accomplished the launch of its next technology POWER9 processors for midrange and high-conclusion methods that are designed for coping with superior analytics, cloud environments and information-intensive workloads in AI, HANA, and UNIX markets.

IBM also delivered new choices optimizing both hardware and utility for AI. administration believes that items like PowerAI vision and PowerAI commercial enterprise will support drive new client adoption.

although, storage hardware revenues declined owing to susceptible performance within the mid-latitude conclusion, partly offset via strong growth in All Flash Arrays. IBM mentioned that pricing power within the immensely competitive storage market is hurting revenues. The company announced its new FlashSystems with next era NVMe expertise right through the stated quarter.

operating methods utility revenues declined 3%, whereas techniques Hardware slumped 23% from the year-ago quarter.

ultimately, international Financing (includes financing and used gadget revenue) revenues decreased eleven% yr over 12 months and 9% at cc to $402 million.

operating particulars

Non-GAAP gross margin remained unchanged from the yr-in the past quarter at 49.5%. The gross margin benefited basically via one hundred ninety foundation points (bps) growth in features margin. despite the fact, adverse combine in IBM Z product cycle absolutely offset this enlargement.

operating expense declined 5.three% year over year, because of attention of acquisition synergies and enhancing operational efficiencies. IBM continues to put money into impulsively becoming fields like hybrid cloud, synthetic intelligence (AI), protection and blockchain.

Pre-tax margin from carrying on with operations improved 50 bps on a year-over-year basis to 23.1%.

Cognitive solutions and world business features segment pre-tax margins accelerated 290 bps and 520 bps, respectively, on a year-over-yr groundwork. youngsters, technology services & Cloud structures section pre-tax margin shriveled 20 bps.

Non-GAAP operating margins from continuing operations shrunk 90 bps and came in at 20.three%.

stability Sheet & cash movement details

IBM ended fourth-quarter 2018 with $eleven.99 billion in complete money and marketable securities in comparison with $14.70 billion at the conclusion of third-quarter 2018. total debt (together with existing portion) changed into $45.8 billion, down from $forty six.9 million from the outdated quarter.

IBM said money move from operations (apart from international Financing receivables) of $7.three billion and generated free money circulation of $6.5 billion in the quarter beneath overview.

within the stated quarter, the business back $three.5 billion to shareholders via dividends and share repurchases. The company lower back greater than $10 billion to shareholders through dividends and share repurchases for the total fiscal year.

on the conclusion of the 12 months, the business had $3.three billion remaining beneath current buyback authorization.

Fiscal 2018 Highlights

IBM reported fiscal 2018 non-GAAP income of $13.81 per share, the place as revenues came in at $seventy nine.6 billion, up 1% each and every yr over yr.

Revenues from Cognitive solutions, global company capabilities, technology functions & Cloud platforms, methods and international Financing got here in at $18.48 billion, $sixteen.82 billion, $34.46 billion, $8.03 billion and $1.fifty nine billion, respectively.

tips

IBM expects non-GAAP EPS forecast for 2019 to be at the least $13.ninety.

IBM nonetheless anticipates 2019 free cash circulation of $12 billion.

How Have Estimates Been moving on account that Then?

It turns out, clean estimates flatlined throughout the past month.

VGM scores

at present, IBM has a subpar increase score of D, however its Momentum rating is doing plenty improved with a B. Charting a somewhat identical course, the stock was allotted a grade of A on the value facet, putting it within the suitable quintile for this funding method.

typical, the inventory has an combination VGM ranking of B. in case you are not concentrated on one approach, this ranking is the one make sure you be attracted to.

Outlook

IBM has a Zacks Rank #three (dangle). We predict an in-line return from the stock within the next few months.

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No effect discovered, are trying new keyword!Synergy analysis estimates the cloud computing market is already $250 billion in dimension, and starting to be even faster, 32% in 2018, with materials of the business posting 50% earnings ... wise and bold circulate ...

IBM Furthers investment In business Analytics With sensible Analytics Cloud | killexams.com Real Questions and Pass4sure dumps

throughout IBM’s Q3 earnings call a few weeks in the past, IBM CFO Mark Loughridge highlighted company analytics as a sector the place massive Blue is investing significant quantities of money. The enterprise lately obtained statistics analytics enterprise SPSS for $1.2 billion and company analytics enterprise RedPill. Tonight, IBM is unveiling a new inside analytics product that the business is touting as the “biggest deepest cloud computing environment for company analytics on this planet,” which launches internally with more than a petabyte of tips. together with this internal product, IBM will launch a companion product for purchasers to build upon this cloud-based architecture, referred to as IBM sensible Analytics Cloud.

The inner product, dubbed Blue perception, will deliver 200,000 personnel in IBM’s earnings and building branch with the potential to extract information and counsel to make decisions and benefit extra perception on the factor of sale. Blue perception will gather counsel from virtually 100 different assistance warehouses and information outlets, offering analytics on greater than a petabyte (1,000 terabytes or 1,000,000 gigabytes) of facts. as an instance, revenue execs may additionally use customizable queries of true time data to be aware salary opportunities and the way many income in their location are closing to assist enrich prediction. Or a producing technique engineer can evaluate precise-time facts on the plant ground to establish trends and records to enrich yield and in the reduction of cargo beginning instances.

IBM wise Analytics Cloud providing for purchasers will in a similar way deliver potent company intelligence by the use of the scalable, inner most cloud. The product will lets the client import information and than transform this counsel into insights to develop innovations and choices. The carrier sill offer the capability to create stories, analysis, dashboards, and scorecards to display screen enterprise efficiency and measure results.

IBM has been shifting its focal point towards utility and features as adversarial to hardware and massive Blue’s strong revenue are a demonstration that this method is paying off in a big way. It’s now not surpising that business is continuing to invest in R&D in cloud computing and business analytics, which looks to be the future for each big Blue and different commercial enterprise-concentrated tech giants like Oracle.


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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.

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  • 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.

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  • 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).

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  • 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

  • Good teams come clean about their IT competence | killexams.com real questions and Pass4sure dumps

    At some point, every one of us is incompetent at something. It's not a bad thing. It's just the way it is. We start out as a dummy, and then, over time, we get smarter. Except for the gifted few, the road to mastery starts in a state of not knowing.

    That's the good news. The bad news is that, when it comes to business, incompetence is unacceptable. As an IT professional, knowing every skill your company expects you to have 100% of the time is challenging, as the demands for IT competence are always changing. The rule of thumb is that, in tech, the stack changes every five years -- if not more frequently. Back in 2014, for example, there weren't a whole lot of Kubernetes experts in the world.

    What to do when you're stumped

    Today's resident container, automation or Agile experts didn't get there by magic. For a good chunk of that time, they didn't know what they were doing. And during that time, more than a few people depended on them, regardless of their expertise.

    When it comes to IT competence, it's OK to admit that you don't know what you're doing, as long as you understand why. Was it the natural result of trying to learn something new? Or was it due to an unyielding pressure from the business -- or your boss -- to learn something because nobody else was available?

    Kubernetes is one IT competence that's seen growth over time Interest in Kubernetes has grown dramatically over the last five years, according to Google Trends.

    There's a difference between arriving at incompetence and being driven to it. It's a difference that matters, according to Beth Power and Logan Daigle at CollabNet. They help teams improve Agile and DevOps effectiveness and emphasize company culture. In a supportive environment -- sometimes referred to as a generative culture -- employees are likely to speak freely about IT competence and incompetence. At these workplaces, workers feel free to admit what they don't know and ask for help.

    In pathological and bureaucratic cultures, workers tend to be too fearful to admit their shortcomings. That makes it much harder to overcome incompetence. Sociologist Ron Westrum detailed the characteristics of generative, pathological and bureaucratic types of workplaces in a 2004 paper, "A Typology of Organizational Cultures."

    westrum model Westrum argues that organization culture shapes many aspects of performance. What corporate culture means

    The rule of thumb is that, in tech, the stack changes every five years -- if not more frequently.

    When your workplace is generative in nature, you can speak up about what you don't know. The organization might provide time and training to fill in a particular gap in IT competence. Or work might be reassigned to someone better suited for the task at hand.

    In less supportive workplace cultures, odds are you're not going to get crucial external support. Some people do well in this type of circumstance, where it's either sink or swim. If you can prevail and demonstrate IT competence even under the most dire of conditions, you have my admiration. If you need a more supportive environment, you have some serious decisions to make.

    Incompetence is an amazing opportunity to grow. If you work in an environment where you've been driven to incompetence and then left on your own to make things better, you might do well to ask if your current workplace is indeed where you want to spend a third of your waking hours.

    Remember, the road to IT competence starts where you don't know what you're doing. A supportive company understands this and will help you to help yourself.


    Finisar Delivers Enhanced WaveShaper Programmable Optical Processors for Optical R&D and Test Applications | killexams.com real questions and Pass4sure dumps

    By Marketwired .

    Article Rating:

    September 14, 2009 05:00 AM EDT

    Reads:

    984

    SUNNYVALE, CA -- (Marketwire) -- 09/14/09 -- Finisar Corporation (NASDAQ: FNSR), a global technology leader for fiber optic subsystems and components for communication applications, today introduced the new WaveShaper S-Series extending the flexibility and performance of its family of Programmable Optical Processors. These enhanced optical processors provide advanced arbitrary filter generation and switching capabilities to simplify and increase the precision of optical system development and production test. They are well-suited for telecom equipment and systems manufacturers, R&D laboratories, and universities.

    WaveShaper optical processors offer the ideal tool for DWDM system and component testing, optical pulse shaping and control, and high-speed optical research and development. The new WaveShaper S-Series extends this already powerful platform with:

    -- Enhancements to the Liquid Crystal on Silicon (LCoS) optical engine to provide even higher optical resolution for improved control of filter shape and dispersion; -- New software, including drivers for LabVIEW and Linux, to simplify integration of the WaveShaper into complex test systems; -- Smaller form factor to free up valuable space within test systems.

    "With the new S-Series, Finisar continues to demonstrate its leadership in optical filtering and Wavelength Selective Switch technology," said Simon Poole, Director of New Business Ventures for Finisar. "By leveraging our extensive optical expertise, we have again delivered unmatched control, precision, and flexibility to our customers."

    The WaveShaper is the only fully programmable Optical Filter and Wavelength Selective Switch available on the market. Its unique ability to control both the amplitude and phase characteristics of filters and switches not only simplifies testing of high-speed optical systems and subsystems at 40 and 100 Gb/s, but also makes possible research and development in entirely new areas. For example, with the WaveShaper, researchers are able to perform Optical Fourier Domain Waveform Manipulation as required for femtosecond laser pulse generation.

    "The WaveShaper has a unique capability to generate optical transfer functions with arbitrary amplitude and phase," said Professor Ben Eggleton, at the University of Sydney. "The ability to easily and precisely implement Optical Fourier Domain Waveform transformations has opened up whole new areas of optical communications research based on the WaveShaper's precise control of pulse shape and timing."

    The new WaveShaper 1000S and WaveShaper 4000S are available now. Finisar will demonstrate both enhanced WaveShaper S-Series Programmable Optical Processors next week in booth #640 at ECOC in Vienna.

    About Finisar

    Finisar Corporation (NASDAQ: FNSR) is a global technology leader for fiber optic subsystems and components that enable high-speed voice, video and data communications for networking, storage, wireless, and cable TV applications. For more than 20 years, Finisar has provided critical optics technologies to system manufacturers to meet the increasing demands for network bandwidth and storage. Finisar is headquartered in Sunnyvale, California, USA with R&D, manufacturing sites, and sales offices worldwide. For additional information, visit www.finisar.com.

    SAFE HARBOR UNDER THE PRIVATE SECURITIES LITIGATION REFORM ACT OF 1995

    The statements contained in this press release that are not purely historical are forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended. All forward-looking statements included in this press release are based upon information available to Finisar as of the date hereof, and Finisar assumes no obligation to update any such forward-looking statements. Forward-looking statements involve risks and uncertainties which could cause actual results to differ materially from those projected. Examples of such risks include those associated with: the uncertainty of customer demand for Finisar's products; the rapidly evolving markets for Finisar's products and uncertainty regarding the development of these markets; Finisar's historical dependence on sales to a limited number of customers and fluctuations in the mix of products and customers in any period; ongoing new product development and introduction of new and enhanced products; the challenges of rapid growth followed by periods of contraction; and intensive competition. Additional risks include the potential impact of pending civil litigation arising from the investigation of Finisar's historical option granting practices. Further information regarding these and other risks relating to Finisar's business is set forth in Finisar's annual report on Form 10-K (filed July 9, 2009).

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