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70-475 Design and Implement expansive Data Analytics Solutions

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70-475 exam Dumps Source : Design and Implement expansive Data Analytics Solutions

Test Code : 70-475
Test cognomen : Design and Implement expansive Data Analytics Solutions
Vendor cognomen : Microsoft
: 42 existent Questions

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Microsoft Design and Implement Big

Microsoft (70-475): Designing and implementing massive statistics Analytics solutions | killexams.com existent Questions and Pass4sure dumps

MeasureUp’s 70-475: Designing and enforcing huge information Analytics options succeed examine is designed to aid candidates prepare for and stagger the Microsoft 70-475 examination.

The Microsoft 70-475 examination is hypothetical for statistics experts who've journey designing big data analytics options on Microsoft Azure. Candidates should silent exist in a position to design expansive records batch processing and interactive solutions in addition to expansive records actual-time processing solutions. Candidates may silent even exist in a position to design computer-researching options and create and manipulate end-to-end cloud analytics solutions.

Certification: This exam counts as credit toward privilege here certifications:specialist


Watch Microsoft's MWC 2019 reside rush amend privilege here | killexams.com existent Questions and Pass4sure dumps

MWC 2019

MWC 2019

Microsoft is making its first expansive appearance at mobile World Congress (MWC) in quite ages this yr, and you'll exist in a position to circulation the business's keynote as it happens. Set to kick off on February 24 at 9 a.m. PT (12 p.m. ET / 6 p.m. CET), the keynote will office "new innovations for the future of computing" with comments from Microsoft CEO Satya Nadella and HoloLens chief Alex Kipman.

which you can keep along with the keynote are living, tune in with the video embed above or at Microsoft's adventure web page.

As for what we're anticipating Microsoft to announce, every sole signs point to a 2d iteration of the company's HoloLens augmented truth headset. We're truly not expecting any shock mobile bulletins, however Kipman's involvement strongly tips at a HoloLens 2 exhibit. it truly is far from definite, of route, but every sole they ought to fade off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.

The middling HoloLens has more often than not loved success as an enterprise tool, bolstering manufacturing tactics, along with design and scientific purposes. The headset has even led to a lucrative $480 million compress with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.

when you esteem that the middling HoloLens launched in 2016, a followup is hotly predicted. it's already been tested that the HoloLens 2 will recreation a custom Holographic Processing Unit (HPU) with an AI coprocessor, enabling it to natively set in favor abysmal neural networks to research objects and statistics without sending the leisure to the cloud. although, they predict several other advancements, together with, potentially, a change to an ARM processor and aid for LTE.

sooner or later, we'll must wait and spot what Microsoft has in maintain for its MWC 2019 keynote.

This set up may comprise affiliate hyperlinks. notice their disclosure policy for extra particulars.


AMD Navi GPUs could notice expansive performance augment With Variable price Shading Tech | killexams.com existent Questions and Pass4sure dumps

A these days posted patent software means that AMD can exist infusing a technology called Variable price Shading (VSR) into its upcoming Navi GPU architecture, which is expected to arrive later this yr. The patent software does not really specify Navi, though it's definitely a probability, given when the patent changed into filed and subsequent date of booklet. AMD filed the patent in August 2017, and it became published the day prior to this. AMD had already been establishing its Navi GPU architecture in 2017, and had additionally begun taping out its 7-nanometer FinFET manufacturing routine around the identical time (a tape out is when a company dote AMD finalizes its semiconductor design before sending it off for manufacturing).

VSR is a rendering approach designed to diminish the tribulation on the GPU and other elements, for introduced efficiency without successful to the perceived visual constancy of a scene. It accomplishes this by means of breaking up a frame into distinctive sections and rendering handiest parts of a frame in plenary aspect, those being the enviornment(s) you are most concentrated on, and using coarser pixels across the fringe of a person's imaginative and prescient.

VR headsets stand to the profit the most from this tech, the plot your eyes are concentrated on the core. That talked about, VSR might conceivably exist used in alternative routes for gaming on the desktop. This may especially exist really useful at greater resolutions which are greater taxing on a GPU, equivalent to 4K, and eventually 8K if they want to gape that far down the street. AMD is not by myself in seeing the handicap of VSR. NVIDIA carried out a edition of the technology into its Turing GPU architecture. here's NVIDIA's explanation of VSR:

Variable cost Shading is a brand new, facile to set into effect rendering routine enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by passage of applying varying quantity of processing power to distinctive areas of the picture. VRS works with the aid of various the variety of pixels that may moreover exist processed through a sole pixel shader operation. sole pixel shading operations can now exist applied to a screen of pixels, permitting applications to effectively disagree the shading price in diverse areas of the monitor. Variable rate Shading can moreover exist used to render extra efficaciously in VR through rendering to a surface that closely approximates the lens corrected picture it is output to the headset monitor. This avoids rendering many pixels that could otherwise exist discarded earlier than the photograph is output to the VR headset.

there is actually widespread activity in VSR. concerning that, Microsoft moreover owns a VSR patent and is working to implement it into the DirectX API. no matter if it extends past VR headsets and into the realm of desktop monitors, notwithstanding, they are able to exigency to wait and spot.

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Design and Implement expansive Data Analytics Solutions

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Reporting & Analytics - expansive Data Developer# 125072 | killexams.com existent questions and Pass4sure dumps

eFinancialCareers jobs: Reporting & Analytics - expansive Data Developer# 125072 in Credit Suisse -, Raleigh, NC, USA
  • Salary: Competitive
  • Location: Raleigh, NC, USA
  • Job Type: Permanent, plenary time
  • Company: Credit Suisse -
  • Updated on: 02 Mar 19
  • We OfferCredit Suisse is seeking a Data Engineer in the Enterprise Services -

    SMO - Enterprise Services are liable for providing innovative ways through maintaining scholarship of industry yardstick methodology, technology trends and product availability. Developing business and technical requirements through consultation with key client business partners and ensuring consistency to corporate standards for risk and security compliance.

    The Analytics & Reporting team works as partake of a globally distributed team that will exist the primary provider of IT Operational Reports to the firm. At any given time you will partner with team members across every sole regions and time zones while reporting to a line manager in fresh York. You will collaborate with key clients to develop dashboards that will drive business decisions across the firm.

    Duties:

  • Design/Maintain/Support Bigdata environment, including ETL.
  • Work directly with key business partners to understand data requirements and deliver on timely manner.
  • Analyze big data sets.
  • Design very involved SQL statements, including Impala.
  • Scripting automation solutions for Data and Reports.
  • Adhere with Agile methodology and succeed the process to maintain/ support and to document the details.
  • Ability to easily communicate involved problem solving ideas to senior management and peers
  • Find opportunities on automation for any manual process.
  • In this function, you will exist designing and implementing data solutions via Bigdata and python. You will moreover build automation scripts to serve minimize manual involvement when structure reports or dashboards. You will moreover serve migrate existing involved SQL tables into Bigdata, and support reporting team to integrate the migrated tables in Tableau.

    You should moreover exist well versed with data design and occupy ETL process experience.

    Credit Suisse maintains a Working Flexibility Policy, theme to the terms as set forth in the Credit Suisse United States Employment Handbook.

    You Offer

  • You occupy a degree or higher education in Information Technology or equivalent travail experience
  • You occupy at least 3 to 4 years of tang structure Bigdata environment
  • You occupy demonstrable tang with Data Engineering
  • You occupy abysmal scholarship in Bigdata impala/Hadoop
  • You occupy skills in Python for structure functions using Pyspark and other packages related to Python
  • Database architect for bigdata environment on how the ETL process works will exist an advantage
  • Good interpersonal skills as you will exist involved in the interaction with clients to understand or present the requirements
  • Experience in Service Now ITIL process would exist an added advantage
  • For more information visit Technology Careers . eFinancialCareers
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  • eFinancialCareers is a DHI service. DHI is a publicly-traded company listed on the fresh York Stock Exchange. (Ticker: DHX)

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    Big data's growing pains | killexams.com existent questions and Pass4sure dumps

    Technology

    By Lisa Harrington, Dr. Zac Rogers, Dale Rogers, Richard Sharpe and Susan Lacefield | From the Quarter 1 2019 issue

    Big data analytics implementations are up, but satisfaction with those implementations is down, according to a recent survey. Why are companies frustrated with their attempts to harness the power of big sets of supply chain data?

    We are at the nascence of a supply chain technological revolution. Everything—from pallets and trucks to a bag of Romaine lettuce—has the potential to collect, store, and transmit information. If only companies could recall this data and feed it into analytics and synthetic intelligence systems, they could build better, faster decisions than ever before, driving down waste and increasing value. That, at least, is the vision of what expansive data analytics could achieve.

    But now, reality is nascence to set in. As more companies try to wrap their arms around their "big data" and implement more involved analytic tools, they are nascence to realize that achieving this vision is difficult work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are moreover finding that the key data that they exigency is often missing or inaccurate. The vow of expansive data analytics is coming, but perhaps not as easily as initially thought. That was the main message conveyed by the results of the "Second Annual expansive Data Analytics Study" conducted by the analytics company Competitive Insights LLC; the consultancy lharrington group; CSCMP's Supply Chain Quarterly; and two prominent supply chain management schools, Arizona situation University and Colorado situation University.

    This annual study is designed to provide companies with a benchmark that they can exercise to understand the current situation of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of expansive data. The intent is to exhibit the levels of progress that companies are making in addition to the obstacles impeding that progress.

    A survey was conducted in both 2017 and 2018 with readers of Supply Chain Quarterly, subscribers to a newsletter produced by Competitive Insights, and a contact list generated by Arizona situation and Colorado situation University researchers. A total of 125 usable responses were compiled for the 2018 survey, comparable to 2017's total of 133 usable responses.

    Here are the findings and some suggested best practices that could serve companies overcome their initial frustration and gain positive momentum with their expansive data analytics implementations.

    More implementations, less satisfaction

    A comparison of 2018's and 2017's results uncover an bright story. In both 2017 and 2018, they asked people: "How would you characterize your supply chain organization's maturity in esteem to its exercise of expansive data analytics?" (See motif 1.) In general, the survey results exhibit that more companies occupy begun implementing expansive data analytics initiatives. There was a 14-percent augment in the number of expansive data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at every sole (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, motif 1 moreover shows that there were fewer people reporting that their implementations were "transformational" or "advanced." Instead, the majority of respondents were in the "early" or "developing" stages of adoption, indicating that most companies are either silent conducting proof-of-concept testing or occupy only rolled out initial implementations.

    Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? They accomplish not believe that firms are necessarily less successful with their expansive data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may occupy evolved since ultimate year. As more companies implement expansive data analytics in earnest, they are developing a better understanding of what it entails and how much farther they occupy to go. In other words, companies now occupy a more realistic assessment of where they actually tumble on the maturity curve and the extent of the obstacles they must overcome. The widespread excitement of 2017 has been replaced by the realization that, dote almost every sole fresh technologies, it takes a lot of travail to build expansive data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.

    We moreover notice this perspective in respondent satisfaction with the data they accomplish have. In general, companies are less satisfied with the trait and availability of their data in 2018 than they were in 2017. On average, respondents in 2018 were 4 percent less satisfied with their data availability, 8 percent less satisfied with data usability, and 7 percent less satisfied with their data's integrity than respondents in 2017. Satisfaction with data reliability moreover dropped 5 percent between 2018 and 2017. It seems unlikely that data trait actually dropped from 2017 to 2018. Rather, they believe that as companies accumulate deeper into their expansive data analytics implementations, they are becoming more awake of their existing data issues and occupy a better understanding of the magnitude of the endeavor and commitment required to address them.

    It stands to judgement that if respondents are less satisfied with the trait of their data, they are going to exist less satisfied with their data analytics results as well. Indeed, the 2018 survey showed a slight drop in respondents' assessment of the realized benefits of their expansive data analytics efforts in 2018 relative to 2017. In both years, survey respondents were asked to exercise a seven-point scale to quantify the repercussion they occupy already realized from expansive data analytics in a variety of areas, such as profitability, inventory management, and visibility to total cost-to-serve. A score of 1 equals no impact, and 7 equals a transformative impact. motif 2 shows a slight drop in perceived repercussion across every sole of the potential benefits in 2018 versus 2017.

    The nature of analytics matters

    When assessing the benefits that can exist expected from a expansive data analytics implementation, it is crucial to understand that there are many types of analytics. For the survey, they defined the different types of data analytics as follows:

  • Descriptive—what is happening/has happened
  • Diagnostic—why it's happening
  • Predictive—what will happen
  • Prescriptive—what should exist done
  • Cognitive—uses machine learning to uncover what should or could exist done
  • Survey respondents were again asked to select from a seven-point scale (where a score of 1 equals no use, and a score of 7 equals cumbersome use) the extent that their company currently uses each of those types of analytics to support supply chain decision making. On average, respondents gave descriptive analytics a score of 4.61 (indicating between "some use" and "regular use"), diagnostic analytics received a score of 4.02, predictive analytics a score of 3.16 (indicating between "some use" and "occasional use"), 3.56 for prescriptive analytics, and 2.27 (or "infrequent use") for cognitive analytics. (See motif 3.) These scores are similar or slightly less than what was seen ultimate year. The survey results bespeak that more sophisticated types of analytics are silent used less than more rudimentary methods in many real-life supply chains.

    Further investigation using regression analyses on the responses identified a significant correlation between the nature of analytics that firms exercise and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider purview of benefits. motif 4 shows that the exercise of descriptive analytics is only correlated with improvements in customer service. This finding makes sense, as knowing what is happening in their supply chain can serve companies better inform their customers of the problems they are facing before these issues become visible to them. While exercise of diagnostic analytics was slightly associated with require planning and highly correlated with collaboration, no correlation could exist institute with any of the other realized benefits. (By "slight," they involve that they are greater than 90 percent inescapable there is a correlation, instead of greater than 95 percent confident there is a correlation.) Predictive analytics, however, could be linked to many benefits, such as improvements in require planning, risk management, and collaboration.

    Somewhat counterintuitively, prescriptive analytics was not positively related with any of these realized benefits. In fact, it was slightly negatively correlated to improvements in require planning. It's possible that by the time firms accumulate to the even of prescriptive analytics, they not only occupy made some improvements to their require planning but moreover are more awake of their issues. Without the capabilities of more advanced analytics, they may occupy cataclysm addressing these issues which they now occupy more insight into. In many ways, this negative correlation is a microcosm of the trends they notice throughout the 2018 report. Firms are nascence to implement more sophisticated protocols, and, in the process, are nascence to realize the obstacles they occupy yet to overcome.

    Finally, they institute that the most helpful nature of analytics was cognitive, with a sturdy correlation to risk management and productivity and a slight correlation to customer service, visibility, and collaboration. In other words, the more sophisticated and forward-looking the nature of analytics that are being used, the more benefits companies are realizing.

    Similar results can exist observed when considering the types of software tools that companies are employing to achieve their analytics. Survey respondents were again asked to exercise a seven-point scale (where a score of 1 equals "no use," a score of 4 equals "some use," and a score of 7 signifies "heavy use") to measure the extent that their company currently uses a variety of analytics tools. Those tools included: Microsoft surpass or similar spreadsheet programs; operational point solutions (OPS) such as warehouse management systems and transportation management systems; advanced analytical tools associated with enterprise resource planning (ERP) systems; and business intelligence (BI) tools. As was the case in ultimate year's survey results, surpass spreadsheets continue to carry the day and are by far the most widely used analytics implement with an middling score of 5.80, indicating "frequent use." OPS received an middling score of 4.64, and ERP and business intelligence tools received scores of 3.97 and 3.88 respectively.

    The survey team moreover ran multiple analyses to notice if there were any links between realized tangible expansive data analytical benefits and the exercise of these platforms. (See motif 5.) While Microsoft surpass was the most widely used of these platforms, it moreover was the least useful. In fact, no correlation was established between the exercise of either surpass or an OPS as the primary expansive data analysis platform and any tangible improvement in customer service, require planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing expansive data are ineffective. They may exist useful for maintaining the status quo, but they are unlikely to lead to many benefits. Without a change in methods, it is unlikely firms will notice a change in results.

    Conversely, although ERP and BI systems were the least used platforms, the regression analysis suggests that they were the most beneficial. ERP exercise was significantly correlated with realized benefits in customer service, require planning, risk management, inventory management, visibility, and increased profitability. business intelligence users reported the greatest levels of performance improvement in customer service, supply chain visibility, end-to-end supply chain collaboration, and overall productivity. These results seem to demonstrate that to build expansive data analysis work, companies exigency to exist using the privilege tools.

    Still hope?

    Although most of the survey respondents occupy only implemented the less sophisticated types of analytics, they are silent hopeful about the results that they are going to achieve. Companies reported that they hope moderately significant improvements in customer service, supply chain visibility, productivity, and profitability in the next 12 months. However, only 40 percent of survey respondents are planning to build a moderate to very big investment in cognitive analytics in the next 12 months, and only 52 percent procedure to build a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents procedure to invest in descriptive analytics.

    The 2018 survey results point to a disconnect between reality and anticipated results. Companies seem to exist focusing their investments in baseline solutions but are hoping for results that are correlated to more sophisticated solutions.

    In spite of these discouraging signs, there is hope. Survey respondents generally reported facing fewer roadblocks to implementing expansive data analytics than they did ultimate year. On a seven-point scale, every sole of the potential impediments that they asked about dropped from an middling score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See motif 6.)

    After analyzing the 2017 data versus the 2018 data, they determined that managerial support was the key incompatibility between this year and ultimate year. Companies that had sturdy managerial support did not notice getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to expansive data analytics implementation. They moreover did not believe that concerns about security risks posed an impediment to adoption. However, they did silent believe that talent acquisition, integrating siloed legacy systems, and gaining competency in fresh tools were significant barriers to further implementations.

    The fact that more companies are reporting having the support of top management is an encouraging sign, as this is essential for continuing to push successful expansive data analytics initiatives when they pan difficulties or missteps. It moreover ensures that the implementations will receive the funding they exigency to gain the required tools, staff, and training.

    The road ahead

    It would exist facile to gape at the results from this year's survey and feel discouraged. While the number of companies implementing expansive data analytics programs has increased, their satisfaction with the data they are working with and with the overall results of these initiatives has dropped. Companies seem frustrated with their exigency of progress and recur on investment.

    However, this sentiment of frustration and even disillusionment is not surprising. It is actually fairly typical for the implementation of any fresh technology, as described by the analyst group Gartner in its "Hype Cycle." Many technology experts occupy observed that when a fresh technology is introduced there is often a era of increasing hype and structure expectations. When the technology does not initially live up to those overinflated expectations, there is often a era that Gartner calls "the trough of disillusionment." During this period, companies' expectations for the technology drop rapidly as they fade through the pain of actual implementation. Then, as companies learn what the technology can actually do, expectations rise again. During that time, companies regain some of the expectations that they lost in the trough of disillusionment, but they never again reach the height seen during the start of the hype cycle. The pain that many firms seem to exist experiencing with their expansive data analytics implementations is a normal, and often essential, partake of the maturity process.

    It's possible that expansive data analytics has entered that "trough of disillusionment," as companies realize how much travail they exigency to accomplish to spotless up their data and expand their exercise of additional types of analytics. To accumulate dependable and trusted data, companies exigency to occupy not just a central data repository, but moreover an executive-sponsored, cross-organizational approach to data collection and analysis. This process needs to factor in accountability, repeatability, and subject-matter validation. This takes time and work. To serve lead companies out of this "trough of disillusionment," notice the sidebar on "Suggested best practices."

    Companies are moreover in the process of learning that the analytical tools they are currently using are not sophisticated enough to provide them with the timely, accurate, and specific insights needed to build smart decisions. Companies exigency solutions that can quickly retort the following questions: What happened? Why did it happen? If they build this change, what could happen? These tools moreover exigency to exist able to track the monetary and operational impacts of the changes that were made. These types of solutions are out there, but companies will exigency to build investments in fresh software and address the change management issues of fully utilizing the insights gained. They will moreover exigency to invest in their employees to build confident they occupy the skill sets required to travail with these systems.

    While the initial shine and optimism might occupy worn off expansive data analytics, companies occupy a more realistic assessment of the road ahead. They more clearly understand the gaps they pan in data trait and accessibility and the limitations of their existing tools. But expansive data analytics, combined with synthetic intelligence methods, silent holds considerable vow for the future. With the privilege even of executive support and investment, companies can reach the point where they occupy the telling data that they exigency for analytics tools to build better decisions more efficiently. But until then, companies must prepare themselves to travail their passage through a era of uncomfortable growing pains.

    Notes:

    1. Regression analysis is a set of statistical processes for estimating the relationship among variables. For example, regression analysis could exist used to establish whether there is a relationship between respondents who exercise descriptive analytics and those who reported improvements in customer service.

     

    Suggested best practices

    The faculty to gain the most value from expansive data analytics is a journey. You can accelerate your company's progress by following proven best practices that address the people involved, the processes and data employed, and the technology utilized.

     People

  • Involve cross-functional teams, with key stakeholders from sales, finance, and marketing, early in any implementation effort. The solutions or root causes that expansive data analytics may disclose will repercussion multiple areas and not always exist confined to the supply chain. Likewise, exist confident to partake any successes with the other functions involved.
  • Link your expansive data analytics efforts to your company's key initiatives. Using the tools to help something that is necessary to your business will serve you build momentum and gain support across the organization.
  • Make confident that you occupy senior-level support and ideally a project champion for your expansive data analytics efforts. Garnering this support will exist easier if you can tie it back to monetary performance.
  • Process:

  • Avoid "one-off," sole design efforts. Recognize that expansive data analytics is not meant to exist a sole project. Instead, you are structure an ongoing organizational capability. Integrating siloed projects is costly and time-consuming. The cost of coordinating implementation efforts early on will pay off down the road.
  • Be intentional about the focus of a specific analytics effort. Clearly communicate what question the expansive data analytics project will quest to retort and who the key stakeholders are. Otherwise you risk scope creep, with the project broadening to the point where it becomes unmanageable and difficult to measure.
  • Adopt a "crawl, walk, run" mentality. Start with a diminutive pilot project first. Getting small, achievable wins early will serve build momentum for bigger projects down the road.
  • Measure direct monetary impact. A key metric for judging the success of a expansive data analytics project is how much it contributed to the bottom line.
  • Data:

  • Gain cross-functional organizational consensus on what data sources should exist used.
  • Make confident that you exercise the best sources of data and that the data itself is specific and accurate. This may involve the establishment of yardstick operating procedures for data collection a few months prior to the start of an implementation.
  • Have sound data governance and repeatable data validation to ensure that the entire organization trusts the data.
  •  Technology:

  • Design solutions to exist used cross-functionally. Ultimately, expansive data analytics should exist applied across the organization, beyond the supply chain. build confident the technology you exercise has this discontinuance goal in mind.
  • Apply an agile development approach. As you implement expansive data analytics in more areas and to address more problems, your approach will change. build confident that your technology can easily reconcile to any changes.
  • Make confident that the technology is scalable.
  • Lisa Harrington is President of lharrington group LLC. Dr. Zac Rogers is an second professor in the managment department at Colorado situation University's College of Business.  Dale Rogers is a professor of logistics and supply chain management at Arizona situation University's W. P. Carey School of Business. Richard Sharpe is CEO of Competitive Insights LLC, a provider of cost-to-serve and profit-analytical solutions. Susan Lacefield is Executive Editor of CSCMP’s Supply Chain Quarterly.

    lisa at lharringtongroup.com

    zac.rogers at colostate.edu

    Dale.Rogers at asu.edu

    rsharpe at ci-advantage.com

    After you comment, click Post. If you're not already logged in, you will exist asked to log in or register.

    Please enable JavaScript to view the comments powered by Disqus.

    We Want to Hear From You! They invite you to partake your thoughts and opinions about this article by sending an e-mail to ?Subject=Letter to the Editor: Quarter 2019: expansive data's growing pains"> . They will publish selected readers' comments in future issues of CSCMP's Supply Chain Quarterly. Correspondence may exist edited for clarity or for length.

    Want more articles dote this? Subscribe to CSCMP's Supply Chain Quarterly.


    Microsoft (70-475): Designing and Implementing expansive Data Analytics Solutions | killexams.com existent questions and Pass4sure dumps

    MeasureUp’s 70-475: Designing and Implementing expansive Data Analytics Solutions exercise test is designed to serve candidates prepare for and pass the Microsoft 70-475 exam.

    The Microsoft 70-475 exam is intended for data professionals who occupy tang designing expansive data analytics solutions on Microsoft Azure. Candidates should exist able to design expansive data batch processing and interactive solutions as well as expansive data real-time processing solutions. Candidates should moreover exist able to design machine-learning solutions and create and manage end-to-end cloud analytics solutions.

    Certification: This exam counts as credit toward the following certifications:Specialist



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