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00M-226 exam Dumps Source : IBM Smart Analytics Sales Mastery Test v1

Test Code : 00M-226
Test cognomen : IBM Smart Analytics Sales Mastery Test v1
Vendor cognomen : IBM
: 34 true Questions

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IBM IBM Smart Analytics Sales

IBM's latest cloud deal shows how it's winning company from opponents | killexams.com true Questions and Pass4sure dumps

IBM Cloud on Wednesday introduced a five-yr, multi-million dollar settlement with sane energy Water (SEW), a SaaS company for the energy and utility sector. SEW is migrating captious workloads to IBM from one other cloud company, with plans to tap into IBM's analytics, AI and IOT capabilities to pace up its utility construction. whereas IBM's cloud commerce hasn't grown as quick as Amazon's or Microsoft's, IBM says or not it's now winning enterprise from corporations relish SEW that begun their journey to the cloud in other places. 

"more and more, there may exist an issue customers are confronted with," observed Patrick Grubbs, IBM's VP of Strategic income for Watson & Hybrid Cloud Platform. "Many believe, 'If I've chosen to wobble everything my workloads to Azure or Amazon, it exist everything going to work.' The reality is, that's nowhere close the certainty.

IBM has made some large bets on a hybrid and multi-cloud future -- together with its $34 billion purple Hat acquisition -- and Grubbs recommended it exist paying off.

"I find a lot of customers who hold stated, 'we now hold decided to flux everything of their applications to' -- prefer your cloud, it can exist Google, Azure or Amazon -- they've discovered they've moved the effortless half," he noted. At that element, although, they find they may exist left with workloads that conveniently don't escape on these clouds, or "the commercial enterprise assist those other clouds purport to hold are nowhere near what they actually need."

SEW is turning to IBM to give a typical international infrastructure for its commercial enterprise web and cellular purposes, which serve more than one hundred fifty utility groups. Its purposes encompass customer engagement and mobile team of workers engagement purposes, online bill processing, energy effectivity paraphernalia and tools for real-time information collection and management.

besides helping SEW host and control its functions, Grubbs talked about IBM will aid the SaaS provider hurry up the growth of its installation base. "it's about the usage of IBM Cloud however moreover the relaxation of IBM," he mentioned, equivalent to practitioners from IBM features or its AI and IOT offerings.

IBM's stress on distinctive features and capabilities -- including hybrid and multi-cloud deployments -- is chiefly useful in the power sector, which has to pick custody of growing older infrastructure, stringent regulatory requirements and elevated client expectations. IBM Cloud additionally recently signed offers with TenneT energy, Ista UK, and Hydro Ottawa.

Grubbs observed the commerce is targeting a sizable range of industries, with recent sizable wins in the telco commerce and monetary functions. IBM has already secured greater than $3 billion in cloud deals in 2019, including a $550 million cloud features condense with Vodafone and a contend with Westpac, one in everything Australia's greatest banks. 

Prior and related insurance:

wise power Water and IBM signal Multi-yr agreement to pace Innovation on the Cloud | killexams.com true Questions and Pass4sure dumps

NEW ORLEANS, Feb. 6, 2019 /PRNewswire/ -- DISTRIBUTECH 2019 – sensible energy Water (SEW), a global energy and water cloud platform company serving over a hundred and fifty+ utilities international, has signed a multi-million greenback settlement with IBM (NYSE: IBM) to leverage the IBM Cloud for SEW platform world deployment over the subsequent five years. 

during the original contract, SEW will access the flexibleness and scalability of the IBM Cloud to give a typical international infrastructure for their commerce web and cell functions. These workloads encompass client engagement and cell team of workers engagement applications, online invoice processing, energy efficiency and demand response purposes, reward courses in addition to tools for precise-time facts collection and administration within the container. for example, a huge scale power enterprise can moreover spin to stitch to installation its complete digital consumer and group of workers engagement platform with IBM Cloud. SEW can now scale its features faster, greater conveniently and at international scale to assist the commerce meet the fluctuating demand of its clients.

This agreement comes as SEW appears to grow its company and tap into superior choices relish analytics, synthetic Intelligence (AI), computing device getting to know and cyber web of issues (IoT). by means of hosting these workloads on the IBM Cloud, SEW can benefit more charge from its statistics and hurry up the development of recent paraphernalia that may advocate enrich efficiency and hold interaction customers in original and customized methods.

The energy and utilities trade is grappling with an growing faded infrastructure, growing customer demand for competitively priced, legit and environmentally sustainable electrical energy, in addition to stringent executive mandates on power efficiency and water conservation. These challenges, coupled with the growing to exist expectations of coincident tech-savvy consumers, hold pushed a speedily need for trade and are forcing many utilities to create original commerce systems and faucet original applied sciences to solidify a aggressive knowledge.

"buyers are open to original techniques of engaging with their utility," renowned Harman Sandhu, President sane power Water. "in conjunction with IBM, they can bring options at significant scale that assist utilities lessen the cost-to-serve by relocating customers from the cognomen heart to lessen-cost digital channels, customise service to raise medium customer delight, and target purchasers for the commandeer opportunities for value-added classes and capabilities."

The SEW and IBM settlement moreover provides a platform to jointly extend accretive and complementary go-to-market efforts globally.

"sensible energy Water is awaiting the fundamental affect linked technologies will hold on their consumers enterprise," mentioned Brad Gammons, international managing director, IBM power, atmosphere and Utilities. "via investing in information, their employees can result their job greater than ever earlier than and purchasers will hold entry to self-carrier capabilities that may advocate enlarge client provider."

From managing renewables' fluctuating power ranges to bringing original efficiency paraphernalia to buyers, utility suppliers are increasingly turning to hybrid multicloud solutions to assist meet the demands of a extra sustainability-concentrated, digital society. TenneT power, Ista UK, and Hydro Ottawa have currently became to the IBM Cloud to advocate them construct original customer services, reclaim cash, and generate income.

For more counsel about IBM Cloud's drudgery with energy and utilities seek recommendation from: https://www.ibm.com/industries/energy

ContactJeannine KilbrideIBM Communications                                                               jkilbri@us.ibm.com860-997-6277

View medium content to download multimedia:http://www.prnewswire.com/information-releases/smart-energy-water-and-ibm-sign-multi-yr-agreement-to-speed-innovation-on-the-cloud-300790504.html

supply IBM

Copyright (C) 2019 PR Newswire. everything rights reserved


IBM Furthers funding In company Analytics With smart Analytics Cloud | killexams.com true Questions and Pass4sure dumps

all over IBM’s Q3 profits summon a few weeks ago, IBM CFO track Loughridge highlighted company analytics as a sector the region massive Blue is investing tremendous quantities of cash. The company currently bought information analytics enterprise SPSS for $1.2 billion and enterprise analytics enterprise RedPill. Tonight, IBM is unveiling a original interior analytics product that the commerce is touting because the “biggest private cloud computing environment for enterprise analytics on the planet,” which launches internally with greater than a petabyte of counsel. together with this inside product, IBM will launch a companion product for shoppers to build upon this cloud-primarily based structure, known as IBM sane Analytics Cloud.

The inside product, dubbed Blue perception, will supply 200,000 personnel in IBM’s revenue and development department with the means to extract records and tips to manufacture selections and benefit extra insight at the point of sale. Blue insight will gather suggestions from well-nigh 100 different assistance warehouses and information stores, featuring analytics on more than a petabyte (1,000 terabytes or 1,000,000 gigabytes) of information. for instance, sales professionals may additionally employ customizable queries of trusty time statistics to exist vigilant earnings alternatives and the passage many income of their vicinity are closing to aid enhance prediction. Or a producing procedure engineer can evaluate true-time information on the plant ground to determine tendencies and facts to enrich submit and reduce back cargo start instances.

IBM sensible Analytics Cloud providing for clients will similarly carry efficient company intelligence by the employ of the scalable, deepest cloud. The product will lets the client import statistics and than transform this counsel into insights to enlarge concepts and decisions. The carrier sill present the capability to create experiences, evaluation, dashboards, and scorecards to display screen company performance and measure outcomes.

IBM has been shifting its heart of attention towards utility and features as adversarial to hardware and massive Blue’s incredible revenue are an illustration that this strategy is paying off in a huge manner. It’s now not surpising that commerce is continuing to invest in R&D in cloud computing and company analytics, which appears to exist the future for both large Blue and different commercial enterprise-concentrated tech giants relish Oracle.


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IBM Smart Analytics Sales Mastery Test v1

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The Best Self-Service commerce Intelligence (BI) Tools of 2018 | killexams.com true questions and Pass4sure dumps

Analytics Beyond Spreadsheets

For many years, Microsoft surpass and other spreadsheets were the tools of preference 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 involved datasets in surpass can silent exist frustrating. Sometimes you start with the wrong kind of data, for example, or you may not know how to exploit 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 efficient tools that people of everything skill levels can employ to manufacture sense of the wealth of information created by businesses every separate day.

Spreadsheets moreover tumble down when the data isn't well-structured or can't exist sorted out in methodical rows and columns. And, if you hold millions of rows or very sparse matrices, then the data in a spreadsheet can exist painful to enter and it can exist hard to visualize your data. Spreadsheets moreover hold 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 moreover exist a problem, particularly if you hold exported graphics that need to exist refreshed when the data changes. Finally, spreadsheets aren't propitious 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 manufacture employ of tables of numbers, they are really acting in different arenas with different purposes. A spreadsheet is first and foremost a passage to store and display calculations. While some spreadsheets can create very sophisticated mathematical models, at their core it is everything about the math more than the model itself.

This is everything a long-winded passage of saw that when businesses employ a spreadsheet, they are actively sabotaging themselves and their competence to consistently fetch valuable insights from their data. BI tools are specficially designed to assist businesses better understand their data, and can prove to exist a huge benefit 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 employ it, it can start to sound vague and nebulous. After all, many different kinds of software present analytics features, and everything businesses want to improve. Understanding what a BI is or isn't can exist unclear.

BI is an umbrella term meant to cover everything of the activities necessary for a company to spin 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 condense 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 expertise 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 constant progression towards more in-depth knowledge about the business, competitors, customers, industry, market, and suppliers, to cognomen 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 involved to exist 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 unavoidable operations or software buckets. This was the case unless IT took on the job of integrating various silos, typically through painstaking and highly manual processes.

While BI software silent 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 dissimilarity between today's BI software and sizable Data analytics is mostly scale. BI software handles data sizes typical for most organizations, from small to large. sizable 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 celebrated half a decade or more ago required specialists, not just to employ but moreover to interpret the resulting data and conclusions. That led to an often inconvenient and fallible filter between the people who really needed to fetch 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 chain of "standard" reporting that analysts would escape on a scheduled basis instead of more ad hoc intelligence gathering and interpretation, which can exist highly valuable in fast-moving situations.

This problem has led to a growing original trend among original 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 exist 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 utensil arsenal, but IT no longer acts as a bottleneck to every query and report request.

As a result, users can pick edge of this distributed BI model. Key tools and captious data hold moved from a centralized and difficult-to-access architecture to a decentralized model that merely requires access credentials and familiarity with original BI software. This results in a entire original kind of analysis becoming available to the organization, namely, that of experienced, front-line commerce people who not only know what data they need but how they need to employ it.

The emerging crop of BI tools everything drudgery hard at developing front-end tools that are more intuitive and easier to employ than those of older generations—with varying degrees of success. However, that means a key criteria in any BI utensil purchasing conclusion will exist to evaluate who in the organization should access such tools and whether the utensil is appropriately designed for that audience. Most BI vendors attest they're looking for their utensil suites to become as ubiquitous and simple to employ for commerce users as typical commerce collaboration tools or productivity suites, such as Microsoft Office. not anything hold gotten quite that far yet in my estimation, but some are closer than others. To that end, these BI utensil suites mind 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 speedily and efficient method of transferring information from a machine to a human brain. The idea is to region digital information into a visual context so that the analytic output can exist quickly ingested by humans, often at a glance. If this sounds relish 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, original values can exist added that will change the visualization on the fly, or the visualization is actually built on rapidly changing data that can spin 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 contemplate impressive at first glance but, if your audience needs assist 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 present 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 moreover third-party and even free data visualization tools that can exist 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 moreover kept in intellect the viewpoint of users who might hold 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 point was to evaluate cloud versions alone, as I often result analysis on the Fly 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 example of this is Tableau Desktop, a favorite utensil of Microsoft surpass users who simply hold an affinity for the desktop utensil (and who just wobble to the cloud long enough to partake 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 utensil over a web utensil anyway." Again, I don't doubt Microsoft's title but that does seem eerie 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 utensil preference is a bit more personal than fact-based, but to each his own.

Then there's Google Analytics, a sheer cloud player. The utensil 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 organize them, simply to let buyers know they exist. IBM Watson Analytics is one utensil with the competence to extend to highly advanced features and was moreover one of the easiest to employ upfront. IBM Watson Analytics is well-suited for commerce 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 figure queries, a characteristic that can manufacture it valuable to practically anyone. It's highly intuitive, very powerful, and simple to learn. Microsoft Power BI is a sturdy second as it, too, is powerful while moreover 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 everything hold their own pros and cons. We'll exist 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 exist 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 exist invaluable. Also, the Internet of Things (IoT) will drive this issue in the near future and manufacture streaming data and streaming analytics a must-have feature. Many of these tools will hold to up their game accordingly so, unless you want to jump ship in a year or two, it's best to mediate ahead when considering BI and the IoT.

BI and sizable Data

Another zone in which self-service BI is taking off is in analyzing sizable Data. This is a newer development in the database space but it's driving tremendous growth and innovation. The cognomen is an apt descriptor because sizable Data generally refers to huge data sets that are simply too sizable to exist 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 convivial media tools (to cognomen a few) that hold become so celebrated over the eventual several years.

Not only result these tools generate loads of original data, they moreover often generate a original 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 hefty on text (even free-form text) while moreover 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 employ to contemplate 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 convivial 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 original information at all. Traditional data warehouse tools generally weren't designed to either manage or query unstructured data. original data storage innovations such as data lakes are emerging to solve for this need, but organizations silent 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 sizable 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 mode (meaning, in your data center, the cloud, or both). Not only does Hadoop let you query sizable Data, it lets you simultaneously query both unstructured as well as traditional structured data. In other words, if you want to query everything of your commerce data for maximum insight, then Hadoop is what you need.

You can download and implement Hadoop itself to accomplish your queries, but it's typically easier and more efficient to employ 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, everything advocate 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 silent await customers to utilize additional tools around aspects such as data governance to ensure optimal performance.

Finding the perquisite BI Tool

Given the issues spreadsheets can hold when used as ad hoc BI tools and how firmly ingrained they are in their psyches, finding the perquisite BI utensil isn't a simple process. Unlike spreadsheets, BI tools hold major differences when it comes to how they consume data inputs and outputs and exploit their tables. Some tools are better at exploration than analysis, and some require a fairly abrupt learning curve to really manufacture employ of their features. Finally, to manufacture matters worse, there are dozens if not hundreds of such tools on the market today, with many vendors willing to title the self-serve BI label even if it doesn't quite fit.

Getting the overall workflow down with these tools will pick some study and discussion with the people you'll exist 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 hold 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 tarry there.

In recent years, companies hold been taking edge 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 distinguished to manufacture confident that your employees actually know how to employ these BI platforms so that you can manufacture confident your investment was worthwhile. There are many ways of approaching this, but using the perquisite online learning platform might exist a propitious region to start looking.

Given the wide charge range of these products, you should segment your analytics needs before you manufacture 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 manufacture it simple to fetch started. Plus, they mind to hold large ecosystems of add-ons and partners that can exist a cost-effective replacement for doing BI inside a spreadsheet. Tableau Desktop silent 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, everything three products received their Editors' preference award. Tableau Desktop may hold a sizable charge tag depending on which version you select but, as previously mentioned, it has an exceptionally large and growing collection of visualizations plus a manageable learning curve if you're willing to consecrate some application to it. Microsoft Power BI and Tableau Desktop moreover hold large and growing collections of data connectors, and both Microsoft and Tableau hold 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 propitious idea to disburse some time looking through those community forums to fetch an idea where these companies are headed.

  • Pros: Extremely user-friendly. grotesque automatic report generation. Impressive advocate availability.

    Cons: Automated reports can quickly become defaults. abrupt learning curve that might fuddle beginners.

    Bottom Line: Zoho Reports is a solid option for common commerce users who might not exist knowledgeable in analytics software. It's moreover available at an attractive price.

    Read Review
  • Pros: Accessible user interface. Smart guidance features. Impressively speedily 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 sturdy 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' preference 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' preference deference for its impressive usability, top-notch data visualization capabilities, and superior compatibility with other Microsoft Office products.

    Read Review
  • Pros: mammoth collection of data connectors and visualizations. User-friendly design. Impressive processing engine. age product with a large community of users.

    Cons: complete mastery of the platform will require substantial training.

    Bottom Line: Tableau Desktop is one of the most age 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 exist too difficult for self-service commerce intelligence (BI). Analytics process silent needs to exist ironed out. Natural language capability can exist limited.

    Bottom Line: Sisense is a complete platform that should exist celebrated for experienced BI users. It may tumble short for beginners, however.

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

    Cons: User interface is not intuitive. abrupt learning curve. Unwelcoming to original analysts.

    Bottom Line: Domo isn't for newcomers but for companies that already hold commerce intelligence (BI) relish in their organization. Domo's a powerful BI utensil with a lot of data connectors and solid data visualization capabilities.

    Read Review
  • Pros: Exceptional platform for website and mobile app analytics.

    Cons: Customer advocate has passage too much automation. Focus on marketing and advertising can exist 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 cognomen in website and mobile app intelligence. It has a abrupt learning curve but it is an awesome commerce intelligence tool.

    Read Review
  • Pros: Designed with common commerce users in mind. Solid recur on investment.

    Cons: The data you can employ 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 manufacture it an attractive offering.

    Read Review
  • Pros: Real-time analytics for Internet of Things (IoT) and streaming data features. Massive ecosystem with ample extenders. Responsive pages manufacture 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 exist warned that there's a abrupt learning curve and a renowned dependence on other SAP products for complete functionality.

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

    Cons: Poorly designed user interface. abrupt learning curve.

    Bottom Line: Chartio excels at pile a powerful analytics platform that experienced commerce intelligence (BI) users will appreciate. Those original 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 much self-service commerce intelligence (BI) utensil that can assist unify SQL and sizable Data management across your enterprise.

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

    Cons: involved pricing is a deterrent.

    Bottom Line: Qlik Sense Enterprise Server is a self-service commerce intelligence (BI) utensil that delivers the best collection of user access roles among the BI tools they tested, and moreover 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 crucible available. Training webinars can exist costly.

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

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

    Cons: The cloud version has a subset of features organize in Windows version. Online documentation could exist improved.

    Bottom Line: While Tibco is silent making the transition from a desktop to a cloud software vendor, its self-service commerce intelligence (BI) utensil Tibco Spotfire is a much passage to start visualizing your surpass data.

    Read Review
  • Pros: Excellent analytical advocate for Intuit QuickBooks. Very simple setup.

    Cons: Installation and setup is a bit of chore. No advocate for Intuit QuickBooks' online versions.

    Bottom Line: Clearify QQube is the best self-service commerce intelligence (BI) utensil for in-depth analysis of your Intuit QuickBooks files, though you'll need to contemplate elsewhere for broader BI tasks.

    Read Review

  • Winning the #ArtificialIntelligence War | @ExpoDX #IoT #DigitalTransformation | killexams.com true questions and Pass4sure dumps

    There is a war a-brewin’, but this war will exist fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will exist the ruler of the world,” the press and analysts hold created hysteria regarding the ramifications of synthetic intelligence on everything from public education to unemployment to healthcare to Skynet.

    Note: synthetic intelligence (AI) endows applications with the competence to automatically learn and adjust from relish via interacting with the surroundings / environment. contemplate the blog “Artificial Intelligence is not Fake Intelligence” for a more circumstantial explanation on synthetic intelligence and machine learning.

    The speedily Company article “How to stop Worrying and savor the much AI War of 2018,” projected that the AI battle would ultimately seethe down between the “AI sizable 6”:  Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

    But what are the characteristics of organizations that will exist the ultimate winners in this much AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

    I believe that the AI winners will hold the following characteristics:

  • Users, not purveyors, of AI technology
  • Embrace open source for technology agility (independence)
  • Mastery of sizable Data (and no, sizable Data is not dead)
  • Let me situation my case.

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in figure 1 offers distinguished clues as to which organizations will likely exist the AI winners. What will set these organizations apart will exist not the selling of technology, but their competence leverage AI for “value capture.”

    Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

    By the way, I mediate Kleiner Perkins was faineant in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

  • Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?
  • Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?
  • Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.
  • Finally, I’m not vigilant of any AI or data technologies that Facebook sells to the common market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?
  • Mastering Value Capture. Just having the technology is not sufficient; it’s how you employ the technology to derive and then drive original sources of customer, business, operational, and monetary value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in figure 1 are trying to dominate markets, not technology. For example:

  • Apple (#1) seeks to dominate personal communications
  • Google/Alphabet (#2) seeks to dominate digital media, advertising and personal communications
  • Amazon (#4) seeks to dominate online commerce
  • Facebook (#5) seeks to dominate convivial media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into original markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are moreover pathetic aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on pile an AI platform (see “JPMorgan Takes AI employ to the Next Level”) that will allow JPMC to dominate monetary trading. And GE (#16) has made a strategic ante with their Predix platform (see “GE’S sizable ante on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

    Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and convivial media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

    #2 Embrace Open Source for Technology Agility (Independence)AI leaders will exploit open-source commerce models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to hearten the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

    Open Source Leaders. The Global AI winners are significant contributors to the synthetic intelligence and machine learning open source communities. This includes developments such as Amazon Machine Learning, Google TensorFlow, Facebook Caffe2, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, Facebook GraphQL, and Facebook Torch.

    The leadership role that the “Great AI War” combatants are playing can exist seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

  • Research Scientist @ Facebook
  • Senior Software Engineer @ Twitter
  • Research Scientist @ Google DeepMind
  • Research Engineer @ Facebook
  • Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build original businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

    Avoiding Technology Lock-in.  But equally distinguished is that these AI leaders are seeking to avoid technology and architecture lock-in. They hold watched faded school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a onerous annual maintenance fees (33% of list charge means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the much AI War.

    #3 Mastery of sizable DataEveryone knows about the astounding growth of sizable data over the eventual decade as organizations focused on capturing circumstantial customer, product, operational and market data. Initially fueled by commerce, web and convivial media data, sizable data has accelerated with the growth of video, wearables, and the Internet of Things. (See figure 2).

    However, organizations hold struggled to monetize this wealth of data. Enter synthetic intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. synthetic intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional commerce Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace abysmal Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between sizable data and AI, let’s contemplate at autonomous vehicles (AV). AV require mammoth quantities of data to feed the AV machine learning algorithms. It would pick tens of thousands of hours of real-world driving data across a variety of driving scenarios to educate cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to assist generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the true World”).

    Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in pile the machine learning and synthetic intelligence models (see “Data Lake commerce Model Maturity Index” for more details on data lake commerce model maturation).

    Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can exist used simultaneously at near-zero marginal cost across an sempiternal commerce and operational employ cases. Understanding the trusty economic value of the organization’s data can assist to prioritize technology and commerce investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

    Conclusion: How to Become an AI WinnerAs has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving commerce Strategies with Data Science,” AI winners will ultimately exist those organizations that are the most efficient at leveraging data and analytics to power their commerce models (see figure 3).

    Figure 3: How efficient Is Your Organization at Leveraging Data and Analytics to Power Your commerce Models?

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key commerce and operational employ cases (see “Use Case Identification, Validation and Prioritization”).
  • Avoid technology and architecture lock-in and create technology independence via an open source technology strategy
  • Mastery of sizable Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake commerce Model Maturity Index”).
  • So in conclusion, let’s hold some fun with this blog and mediate outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would exist the commerce model ramifications to GE if they were to open source Predix and present Predix training to universities and third party developers?
  • What would exist the commerce model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would exist the commerce model ramifications if IBM moved out of the technology purveyor commerce and instead acquired companies in monetary services and healthcare where their Watson AI platform could create market dominance?
  • As the world prepares for the impending much AI war, now is not the time for organizations to exist coy or to cling to old, outdated commerce models.

    Fortune Favors the Brave.

    Sources

    Figure 1: ScoopNest “2017 global market capitalization leader board: tech is 40% of top 20 companies and 100% of top 5” and Consultancy UK “Market capitalisation of world’s 100 biggest companies hits $17.4 trillion”

    The post 3 Keys to Winning the much synthetic Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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    Proposal – IBM blockchain to manage marijuana supply chain in British Columbia | killexams.com true questions and Pass4sure dumps

    Bob Marley marijuanaProbably not a headline you ever thought you’d read on diginomica, but the times they are a changin’.  The idea is probably not as sizable a pot-pipe dream as it sounds.

    Canada is in the process of legalizing marijuana by July 2018, fulfilling one of the key campaign promises of Prime Minister Justin Trudeau. Ottawa is leaving it up to provincial governments to regulate its legal sale and distribution.

    When the government of British Columbia asked for feedback on the best passage to manage its legal marijuana market, IBM quickly proffered a regulatory filing, arguing that the province should employ the distributed ledger technology called blockchain to manage its legal marijuana market.

    In the filing, the Armonk, NY-based software giant said:

    IBM suggests Blockchain is an exemplar mechanism in which BC can transparently capture the history of cannabis through the entire supply chain, ultimately ensuring consumer safety while exerting regulatory control – from seed to sale.

    Blockchain is rapidly becoming a world leading technology enabling the assured exchange of value in both digital and tangible assets, while protecting privacy and eliminating fraud.  Blockchain offers a shared ledger that is updated and validated in true time with each network participant. It enables equal visibility of activities and reveals where an asset is at any point in time, who owns it and what condition it’s in.

    As every schoolboy now knows, blockchains were originally conceived as a public, shared ledger that keeps track of payments in the Bitcoin network.

    Bitcoin’s volatility has panicked off a lot of potential mainstream adoption of blockchain as a payment network.

    But companies quickly realized that the core concept of the blockchain could exist repurposed for many other chains of custody applications, ranging from diamonds and tomatoes to global supply chains. Or, as in this case, the growth, distribution, sale, regulation, and taxing of your next favorite pastime substance.

    Basically, a blockchain is nothing more than a distributed database that’s maintained by a peer-to-peer network of companies involved in almost any shared marketplace. Every company manages and maintains its own copy of the shared ledger.

    In the case of British Columbia’s pot initiative, “each party in the commerce network” (growers, processors, distributors, retail locations, and regulators) would fetch its own ledger copy showing everything transactions. The shared ledger would exist accessible for anyone with the perquisite credentials to see, allowing regulators to conduct spot audits of every marijuana transaction in the province.

    Retailers could identify which farm a particular batch of marijuana came from and what safety inspections were conducted along the way. Reads the IBM pitch:

    The Blockchain shared ledger is updated and validated in true time with each network participant. This enables equal visibility of activities and reveals where an asset/product is at any point in time, who owns it and what condition or situation it is in. This kind of transparency would bring a original flat of visibility and control to the provincial regulators and provide assurance to the army of cautious stakeholders regarding the passage the management of a cannabis supply chain is rolled out within British Columbia.

    IBM’s brief proposal to the government of British Columbia identifies key benefits for each of the parties in a feasible blockchain agreement:

    BC Government: Blockchain can assist the Provincial Government pick control of sourcing, selling and pricing of products, therefore can reduce or eradicate black market sales completely.

    Producers: Blockchain can assist producers with real-time inventory management, greater projections of supply and demand, and moreover elicit trends of consumption through data analytics.

    Retailers: Although the Government of BC hasn’t confirmed what end-user distribution model will exist used, they anticipate that it is likely that government itself will play a role in that process. An interconnected Blockchain network can assist retailers identify supply/demand gaps ways to mitigate those gaps, providing feedback mechanisms to producers, and employ data to create predictive insights.

    IBM has been promoting blockchain aggressively in recent months.  In March, the company released IBM Blockchain, which it says is the first enterprise-ready blockchain service based on the Linux Foundation’s open source Hyperledger project.

    IBM’s blockchain services are designed to assist developers create, deploy and manage blockchain networks on the IBM Cloud and a number of clients hold already done so, ranging from the startup Everledger, which bills itself as the digital vault of the future, to monetary services leaders relish Bank of Tokyo-Mitsubishi UFJ, Postal Savings Bank of China and Northern Trust.

    A blockchain approach to supply chain management has a number of useful commerce advantages. Transactions can exist asynchronous—individual buyers and sellers can deal directly with each other.

    The Hyperledger stack has cryptographic features that can confine one vendor from spying on another vendor’s transactions.  Hyperledger can moreover exist combined with Internet-of-things technology, attaching a tracking device to products in transit and automatically generating data about their progress as they wobble through the supply chain.

    Using its Watson IoT Platform, IBM makes it feasible to employ information from devices such as RFID-based locations, barcode-scan events, or device-reported data with IBM’s Blockchain. Devices will exist able to communicate to blockchain-based ledgers to update or validate smart contracts.

    Built to scale to thousands of users quickly, IBM says its cloud will allow production blockchain networks to exist deployed in minutes, running signed, certified and tested Docker images with dashboards and analytics as well as support.

    A blockchain is only as propitious as the security of its infrastructure and IBM has spent millions of dollars on teams of security experts, cryptographers, hardware experts, and researchers to manufacture unavoidable its blockchain and cloud services are tamper-resistant and free of back door vulnerabilities that allow unauthorized access.

    With so much buzz about blockchain and so many projects underway at the moment, we’re about to find out whether the technology is the true thing or, in the case of BC, one toke over the line. I’m becoming a believer.

    This just leaves me with two sizable questions – what would Bob Marley hold said? Who gets to test the supply chain in the true world? Somehow I can’t contemplate the sizable Blue consultants out in the field, so to speak. 🙂

    Image credit - public images



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