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SPS-202 - IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate - BrainDump Information

Vendor Name : IBM
Exam Code : SPS-202
Exam Name : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate
Questions and Answers : 25 Q & A
Updated On : March 22, 2019
PDF Download Mirror : SPS-202 Braindumps
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SPS-202 exam Dumps Source : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate

Test Code : SPS-202
Test Name : IBMSPSSMBPDM- IBM SPSS Modeler Business(R) Partner Data Mining Associate
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBMSPSSMBPDM- IBM SPSS Modeler

IBM Watson Studio: Product Overview and insight | killexams.com Real Questions and Pass4sure dumps

down load the authoritative e-book: Cloud Computing 2019: the usage of the Cloud for aggressive advantage

See the whole checklist of computing device gaining knowledge of SolutionsSee consumer experiences of IBM Watson Studio

bottom line

Watson is an umbrella for all IBM deep discovering and artificial intelligence, in addition to laptop learning. The company became a pioneer in introducing AI technologies to the business world. What this potential for patrons: Watson Studio is a exact contender for any company looking to deploy laptop studying and deep researching applied sciences.

The platform gives extensive equipment and technologies for facts scientists, builders and field depend consultants that need to discover information, build models, and educate and set up computer gaining knowledge of fashions at scale. The answer contains equipment to share visualizations and results with others. Watson Studio helps cloud, computer and local deployment frameworks.

The latter resides behind a firm’s firewall or as a SaaS solution working in an IBM deepest cloud. IBM Watson Studio is ranked as a “chief” within the Forrester Wave. It turned into a customers’ option 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a group of IBM tools and applied sciences to construct effective computing device getting to know purposes and services. This comprises IBM Cloud pretrained computing device studying fashions corresponding to visible recognition, Watson herbal Language Classifier, and others. The environment makes use of Jupyter Notebooks together with other open supply tools and scripting languages to complement constructed-in collaborative undertaking points.

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The outcome is an ambiance that enables quick and powerful laptop gaining knowledge of building and quality tuning of models. facts scientists and others can make a choice from a number of capacities of Anaconda, Spark and GPU environments.

Watson Studio supports better visual modeling through a drag-and-drop interface supplied by using IBM’s SPSS Modeler. furthermore, it accommodates automatic deep researching the use of a drag-and-drop, no-code interface in Neural community Modeler.

Overview and lines consumer Base

statistics scientists, builders and area remember consultants.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/formats Supported

supports Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.

codecs Supported

Most major information and file codecs are supported through open supply Jupyter Notebooks.

Integration

IBM Watson Studio connects a couple of IBM items, together with SPSS Modeler and records Science event (DSX) together with open supply tools, with a view to bring a strong Predictive Analytics and laptop getting to know (PAML) solution.

The ambiance accommodates open data sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud edition features interactivity with computer servers and R Studio, together with Python, R., and Scala coder for facts scientists.

Reporting and Visualization

Visualization via SPSS Modeler. strong logging and reporting capabilities are developed into the product.

Pricing

IBM has adopted a pay-as-you-go model. Watson Studio Cloud – average expenses $ninety nine per month with 50 means unit hours per month covered. Watson Studio Cloud - business runs $6,000 per month with 5,000 capability unit hours. Watson Studio laptop charges $199 per thirty days with limitless modeling. Watson Studio local – for enterprise records science teams N/A.

IBM Watson Studio Overview and contours at a look:

seller and contours

IBM Watson Studio

ML focus

wide information science center of attention with cloud and desktop ML structures.

Key features and capabilities

powerful visible awareness and herbal classification equipment. flexible strategy that accommodates open source tools. Connects to IBM SPSS Modeler.

consumer feedback

particularly rated for points and capabilities. Some complaints revolving round using notebooks.

Pricing and licensing

Tiered mannequin from $99 per month per user to $6,000 per 30 days per consumer or more at enterprise level.


IBM sends Cognos, SPSS to the cloud | killexams.com Real Questions and Pass4sure dumps

Two of IBM’s most established evaluation products, the Cognos company Intelligence and the SPSS predictive analytics equipment, are headed for the cloud, the newest in an ongoing push by using IBM to port its substantial utility portfolio to the cloud.

having access to this sort of application from a hosted atmosphere, in place of procuring the equipment outright, offers a number of advantages to purchasers.

“We manage the infrastructure, and this permits you to scale more effortlessly and get all started with less upfront investment,” pointed out Eric Sall, IBM vice president of global analytics marketing.

IBM introduced these additions to its cloud functions, as well as a few new choices, at its perception user conference for records analytics, held this week in Las Vegas.

by 2016, 25 p.c of recent enterprise analysis deployments can be accomplished within the cloud, in keeping with Gartner.

Analytics might support groups in lots of techniques, in line with IBM. It may supply further insight in the procuring habits of consumers, as well as insight into how neatly its personal operations are performing. It could help safeguard systems from assaults and attempts at fraud, as well as guarantee that business departments are assembly compliance requirements.

the new online edition of Cognos, IBM Cognos enterprise Intelligence on Cloud, can at the moment be validated in a preview mode. IBM plans to present Cognos as a full business provider early next year. clients can run Cognos towards statistics they retain in the IBM cloud, or towards records they save on premises.

A full business edition of the online IBM SPSS Modeler will be purchasable within 30 days. This equipment will encompass all the SPSS add-ons for records based predictive modeling, akin to a modeler server, analytics decision administration software and a information server.

past this year, IBM pledged to offer a whole lot of its software portfolio as cloud services, many via its Bluemix set of platform functions.

besides Cognos and SPSS, IBM also unveiled a number of new and up-to-date offerings at the conference.

One new carrier, DataWorks, gives a couple of options for refining and cleaning facts so it's equipped for evaluation. The company has launched a cloud-based data warehousing carrier, called dashDB. a brand new Watson-primarily based provider, called Watson Explorer, offers a way for users to ask natural language questions on diverse units of interior information.

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IBM Watson Studio is Coming to Your desktop | killexams.com Real Questions and Pass4sure dumps

example of a Modeler stream running on Watson Studio computing device

IBM Watson Studio is an exceedingly effective information science offering. if you’re now not typical, Watson Studio is an built-in environment designed to make it easy to increase, educate, and manipulate models. It comprises a large choice of equipment used for every little thing from records prep and mix to picture consciousness. And now Watson Studio is on its solution to your windows or Mac desktop computer.

Overview of Watson Studio

Watson Studio contains lots of tools all inside one built-in atmosphere. the most regularly occurring equipment of Watson Studio are:

  • SPSS Modeler for facts prep, mix, and modeling devoid of coding
  • facts Refinery, a spreadsheet fashion tool, that permits quickly, self-service records practise
  • Python, R, and Scala Jupyter Notebooks to create and share files that contain reside code, equations, visualizations, and explanatory text
  • Neural network Modeler for designing TensorFlow, Keras, PyTorch and Caffe constructions without coding
  • Interactive Dashboards to deliver sharable are living reports so the conclusion users can discover their personal insights
  • obtainable Deployment options

    Watson Studio will quickly be purchasable as three deployment options:

  • Watson Studio on the IBM Cloud is the easiest way to get begun. It presents 3 tiers from a free starter tier with pay-as-you-go compute all the technique to an enterprise edition.
  • Watson Studio local is our behind-the-firewall answer proposing a relaxed way so as to add Watson Studio capabilities to your commercial enterprise. contains purchase options for a handful of users or the total organization.
  • And coming quickly: Watson Studio desktop
  • Introducing Watson Studio computing device

    Watson Studio desktop is our new third deployment option inside the Watson Studio family. It takes essentially the most frequent parts of what's available inside Watson Studio on the cloud and ports it to your windows or Mac computing device for offline use, for evaluation of information data you don’t are looking to upload, and for snappy performance. All with a fast and simple desktop installing.

    Watson Studio on the computer will scale from someone user all of the way as much as a big team, and will be a fine addition to the Watson Studio family together with our cloud and native flavors.

    The initial leading features of Watson Studio computing device can be a SPSS Modeler canvas in conjunction with the statistics refinery spreadsheet editor. this will encompass backwards compatibility for SPSS Modeler streams.

    merits of Watson Studio laptop
  • It’s quickly, in particular for local files. increase particular person productivity the usage of a in the neighborhood put in customer.
  • It provides offline use. want to construct a mannequin on a aircraft devoid of WiFi? Watson Studio laptop will work with out an internet connection for weeks at a time.
  • Adhere to safety and governance mandates along with your facts. Watson Studio computing device allows for all your statistics to stay in your computing device.
  • unlimited modeling devoid of overages. using Watson Studio computing device, that you could boost models without any incremental cost. This allows you to build models iteratively on your laptop, then move it to Watson Studio on the Cloud to set up.
  • It’s one Watson Studio, so no re-learning required. With the same designs because the cloud and local types, switching between deployment options is seamless without a unique practicing.
  • And similar to the rest of Watson Studio, any one — coders or non-coders — may be in a position to put together, mix, explore, and model their statistics in precisely minutes.
  • Watson Studio laptop’s potent graphing knowledgeJoin the Beta

    The Beta for Watson Studio is beginning now with two equipment already ported to the computing device: a refreshed edition of SPSS Modeler & facts Refinery. it'll also consist of tasks to prepare your work, statistics assets organization, and a effortless check for updates function.

    be part of the beta to supply comments on the way you like these tools, and advocate which other tools we carry to the computer. The Beta is part of the IBM SPSS Modeler Subscription Trial, so when you register for that you just’ll have access to both downloads.

    After you register, search for the “IBM SPSS Modeler Subscription Trial” card, click on down load, then you definately’ll see the Watson Studio laptop Beta downloads on the appropriate hand facet of the page.

    join the discussion on the beta neighborhood

    After registering for the beta, be a part of within the discussion on the Watson Studio computing device beta neighborhood. here that you would be able to ask questions concerning the beta, supply strategies for future aspects, get forum assist, and take part in valuable surveys to have an impact on product direction. be a part of the discussion board right here by means of logging in with your IBMid.

    wait for future announcements right here about this pleasing addition for the Watson Studio household.

    Watson Studio computer task organizer

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    Your guide to the top big data certifications today | killexams.com real questions and Pass4sure dumps

    IDG

    IDG

    Data and big data analytics are fast becoming the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake big data initiatives can be even harder.

    Not surprisingly, that challenge is reflected in the rising demand for big data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified big data skills have gained value in recent years: with 74 advanced data analytics related skills and certifications rising in average value by 6 percent in 2015, followed by 116 advanced data analytics related skills and certifications increasing 4.8 percent overall in market value in 2016. Additionally, Foote Partners research found 123 related certified and noncertified big data skills seeing a 0.3 percent gain in value in the first quarter of 2017.

    Organizations are on the hunt for data scientists and analysts with expertise in the techniques required to analyze big data. They also need big data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their way around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.

    [ Find out the hottest data and analytics trends today. | Get ahead with the top certs for big data, project management, agile, and the cloud. | Get weekly career tips by signing up for our CIO Leader newsletter. ]

    These skills are in high demand and are relatively rare. Individuals with the right mix of experience and skills can demand high salaries. The right certifications can help.

    "Advanced data analytics capabilities are just too critical for staying competitive," David Foote, co-founder, chief analyst and chief research officer of Foote Partners, said in a statement released with the research. "They've expanded in popularity from a few industries to nearly every industry and market. And there is the Internet of Things, the next critical focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to have a $4 trillion to $11 trillion global economic impact by 2025 as businesses look to IoT technologies to provide more insight."

    While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of base salary than the value of certified big data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily rise over the next 12 to 24 months.

    If you're looking for a way to get an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding rapidly.

    Below is our guide to the most sought after big data certifications to help you decide which cert is right for you.

    If you would like to submit a big data certification to this directory, please email us.

    Analytics: Optimizing Big Data Certificate

    The Analytics: Optimizing Big Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, financial industry professionals, and small business owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to analyze large datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, building analytics models, finding the best model to explore correlation among variables and more.

    Organization: University of Delaware

    Price: $2,895 course fee

    How to prepare: A basic background in statistics and some prior college coursework is recommended.

    Certificate in Engineering Excellence Big Data Analytics and Optimization (CPEE)

    Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence Big Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of all aspects of analytics, including working with big data using Hadoop. It focuses on R and Hadoop skills, as well as statistical modeling, data analytics, machine learning, text mining and optimization. Students are evaluated on a real-world capstone project and a series of quizzes.

    Organization: International School of Engineering (INSOFE)

    Price: ₹3000 (INR) application fee and a program fee of ₹3,25,000 + 15 percent service tax.

    How to prepare: INSOFE admits students based on performance on its entrance exam and prior academic background and work experience.

    Certification of Professional Achievement in Data Sciences

    The Certification of Professional Achievement in Data Sciences is a non-degree program intended to develop facility with foundational data science skills. The program consists of four courses: Algorithms for Data Science (CS/IEOR), Probability & Statistics (STATS), Machine Learning for Data Science (CS), and Exploratory Data Analysis and Visualization (STATS).

    Organization: Columbia University

    Price: $1,858 per credit (a minimum of 12 credits, including the four courses, are required to complete the program). In addition, there is an $85 non-refundable application fee for the on-campus program and $150 for the online program. The online program also includes an additional non-refundable technology fee of $395 per course.

    How to prepare: An undergraduate degree and prior quantitative and introductory to computer programming coursework are required.

    Certified Analytics Professional

    The Certified Analytics Professional (CAP) credential is a general analytics certification that certifies end-to-end understanding of the analytics process, from framing business and analytic problems to acquiring data, methodology, model building, deployment and model lifecycle management. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

    Organization: INFORMS

    Price: $495 if you are an INFORMS member, or $695 if you're not. Team pricing is available for organizations. 

    How to prepare: A list of study courses and a series of webinars are available through registration.

    Cloudera Certified Associate (CCA) Data Analyst

    A SQL developer who earns the CCA Data Analyst certification demonstrates core analyst skills to load, transform and model Hadoop data to define relationships and extract meaningful results from the raw output. It requires passing the CCA Data Analyst Exam (CCA159), a remote-proctored set of eight to 12 performance-based, hands-on tasks on a CDH 5 cluster. Candidates have 120 minutes to implement a technical solution for each task. They must analyze the problem and arrive at an optimal approach in the time allowed.

    Organization: Cloudera

    Price: $295

    How to prepare: Cloudera recommends candidates take the Cloudera Data Analyst Training course, which has the same objectives as the exam.

    Cloudera Certified Associate (CCA) Spark and Hadoop Developer

    The CCA Spark and Hadoop Developer credential certifies a professional has proven their core skills to ingest, transform and process data using Apache Spark and core Cloudera enterprise tools. It requires passing the remote-proctored CCA Spark and Hadoop Developer Exam (CCA175), which consists of eight to 12 performance-based, hands-on tasks on a Cloudera Enterprise cluster. Each question requires the candidate to solve a particular scenario. Some cases may require a tool such as Impala or Hive, others may require coding. Candidates have 120 minutes to complete the exam.

    Organization: Cloudera

    Price: $295

    How to prepare: There are no prerequisites required, but Cloudera says the exam follows the same objectives as the Cloudera Developer Training for Spark and Hadoop course, making it excellent preparation for the exam.

    Cloudera Certified Professional (CCP): Data Engineer

    The CCP: Data Engineer credential certifies the ability to perform core competencies required to ingest, transform, store and analyze data in Cloudera's CDH environment. It requires passing the remote-proctored CCP: Data Engineer Exam (DE575), a hands-on, practical exam in which each user is given five to eight customer problems each with a unique, large data set, a CDH cluster and four hours. For each problem, the candidate must implement a technical solution with a high degree of precision that meets all the requirements.

    Organization: Cloudera

    Price: $400

    How to prepare: Cloudera suggests professionals seeking this certification have hands-on experience in the field and take the Cloudera Developer Training for Spark and Hadoop course.

    EMC Proven Professional Data Scientist Associate (EMCDSA)

    The EMCDSA certification demonstrates an individual's ability to participate and contribute as a data science team member on big data projects. It includes deploying the data analytics lifecycle, reframing a business challenge as an analytics challenge, applying analytic techniques and tools to analyze big data and create statistical models, selecting the appropriate data visualizations and more.

    Organization: Dell EMC Education Services

    Price: $600 for video-ILT streaming; $5,000 for instructor-led

    How to prepare: EMC offers a training course, available as either a video or an instructor-led course.

    IBM Certified Data Architect – Big Data

    Designed for data architects, the IBM Certified Data Architect – Big Data certification requires passing a test that consists of five sections containing a total of 55 multiple-choice questions. It demonstrates a data architect can work closely with customers and solutions architects to translate customers' business requirements into a big data solution.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a series of seven multi-day courses on SPSS Modeler to InfoSphere BigInsights to prepare for the test.

    IBM Certified Data Engineer – Big Data

    The IBM Certified Data Engineer – Big Data certification is intended for big data engineers, who work directly with data architects and hands-on developers to convert an architect's big data vision into reality. Data engineers understand how to apply technologies to solve big data problems and have the ability to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate big data solutions within organizations, providing architects with input on needed hardware and software. This certification requires passing a test that consists of five sections containing a total of 53 multiple-choice questions.

    Organization: IBM Professional Certification Program

    Price: $200

    How to prepare: IBM recommends a series of nine multi-day courses to prepare for the test.

    Mining Massive Data Sets Graduate Certificate

    Designed for software engineers, statisticians, predictive modelers, market researchers, analytics professionals, and data miners, the Mining Massive Data Sets Graduate Certificate requires four courses and demonstrates mastery of efficient, powerful techniques and algorithms for extracting information from large datasets like the Web, social network graphs and large document repositories. The certificate usually takes one to two years to complete.

    Organization: Stanford Center for Professional Development

    Price: $18,000 tuition

    How to prepare: A Bachelor's degree with an undergraduate GPA of 3.0 or better is required. Applicants should have knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.

    MongoDB Certified DBA Associate

    The MongoDB Certified DBA Associate credential is intended to demonstrate that operations professionals understand the concepts and mechanics required to administrate MongoDB. It requires a 90 minute, multiple choice exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M102: MongoDB for DBAs; M202: MongoDB Advanced Deployment Operations). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.

    MongoDB Certified Developer Associate

    The MongoDB Certified Developer Associate credential is intended for software engineers who want to demonstrate a solid understanding of the fundamentals of designing and building applications using MongoDB. It requires a 90 minute, multiple choice exam.

    Organization: MongoDB University

    Price: $150

    How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M101J: MongoDB for Java Developers; M101JS: MongoDB for Node.js Developers; M101N: MongoDB for .NET Developers; M101P: MongoDB for Developers). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.

    SAS Certified Big Data Professional

    The SAS Certified Big Data Professional certification program is for individuals seeking to build on their basic programming knowledge by learning how to gather and analyze big data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data quality for reporting and analytics; fundamentals of statistics and analytics; working with Hadoop, Hive, Pig and SAS; and exploring and visualizing data. The program includes two certification exams, both of which the participants must pass.

    Organization: SAS Academy for Data Science

    Price: $9,000 for classroom (Cary, NC), $4,725 for blended learning (combination of 24/7 online access and instructor-led training)

    How to prepare: At least six months of programming experience in SAS or another programming language is required to enroll.

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    Perspectives on Text Analytics in 2009 | killexams.com real questions and Pass4sure dumps

    You’re reading this article because you’re actively interested in text analytics technology and market trends. Whether you’re a solutions provider or consumer, you want to be sure you’ve invested your time and financial resources wisely.

    The picture is complex, however, as the text analytics domain is not dominated by any one algorithm or approach, vendor or business sector. So far, the diversity of options has benefitted potential users; awareness (and realization) of the technology’s capabilities is building rapidly in areas such as media monitoring, publishing, customer-experience management and semantic search. Meanwhile, growth remains strong in domains that have long applied text analytics. These areas include life sciences, intelligence and law enforcement, and financial services. While the text analytics domain isn’t unaffected by current business conditions, economic pressures could actually spur uptake motivated by a quest for efficiency through automation.

    Sources and Perspectives

    To stay on top of trends, I try to talk to current and prospective users as often as I can, and I also try to keep up with researchers and fellow analysts. (You’ll find links for some of the folks I follow at my BeyeNETWORK channel.) I also catch up with vendors periodically.

    Because it’s helpful to understand current industry perspectives, I recently invited CEOs, CTOs and thought leaders to respond to the following query:

    What do you see as the 3 (or fewer) most important text analytics technology, solution or market challenges in 2009?

    Before relaying answers I received, I’ll mention two meta-replies. First, Marti Hearst, associate professor in the UC Berkeley School of Information, declined to respond, in part because “…for me, 2009 is too close a time horizon.” This thought applies not only to academic researchers but also to anyone who’s thinking strategically, beyond this year’s crop of product releases and business conditions. Next, Lexalytics CEO Jeff Catlin candidly characterized his response as “somewhat self-serving thoughts.” No doubt many of the vendors quoted see the greatest challenges as residing in areas their tools address, so take their views with a grain of salt.

    I’ll relay all views with minimal editing, complete with vendor self-promotion because, after all, commercial products are “where the rubber hits the road” for most enterprise users.

    Text Analytics in 2009

    Let’s start with Maria Milosavljevic, CTO at Capital Markets Cooperative Research Centre, an Australian group that is conducting leading-edge R&D in real-time security-market surveillance. The top 3 for Maria are:

  • Expectations: Unfortunately I think that the most difficult challenges of the past are still with us, and this is the biggest. People either believe that not much at all is possible or they believe that more is possible than reality. It is never the case where end users have realistic expectations.
  • Portability: Text analytics systems trained on a particular type of input data do not typically transfer well to other types of input. Cross-domain issues (e.g., from news to medical) are similar to transfer between intra-domain input documents (e.g., from medical news to medical journals).
  • Data quality: Data is never clean. Garbage in = garbage out!
  • Breck Baldwin of Alias-i, author of LingPipe natural language processing software, is very to-the-point in his reply, with a sort of twist on Maria’s expectations response.

    #1 thing the field needs is a profound and real success story of text analytics.

    The reply from Sid Banerjee, CEO of Clarabridge, looks at factors related to scaling text analytics to the enterprise, “the expansion of text analytics across a few dimensions.” His reply:

  • From a functional to an enterprise imperative. In 2006-2008, text analytics was deployed to marketing, call center, survey groups. Over the past few years, solutions have morphed from those that serve one group to an offering that supports multiple groups, and now customers are looking, on day one, to pick solutions that can demonstrate cross-functional value. The selling cycles are more complex, the solutions need to show more a priori business value and relevance, and at the same time, because the solutions are not just deployed in one area, IT wants to know more how the solution is going to fit with "enterprise" standards. In total: selling is now more complex, more constituencies are involved in the decision, but at the same time articulating and demonstrating business value is even more important.
  • From an isolated to an integrated solution. Toward the end of 2008, we started seeing more interest from companies and partners looking to seamlessly integrate the results of text analytics back into operational systems, i.e., process customer verbatims and merge the categorized, scored results back into call center applications. This new use cycle – not just for analytics, but for operational integration – provides an interesting opportunity for text analytics vendors to consider whether to be a stand-alone application or to be more tightly integrated with partner products and offerings.
  • From a static to a scalable architecture. The big story in 2008 was scale. Data volumes are going up. Usage requirements are going up. There's no reason to expect scalability requirements won’t continue to grow in 2009 and beyond. The winners will be those who can see beyond today's data and user volumes and design for an order or two more magnitude in their offerings, predicting this certain future.
  • Aaron B. Brown of IBM similarly looks at architectural issues and also at business concerns. Aaron is Program Director, ECM Discovery, for IBM Information Management Software and has great market and technical insights. I interviewed him last year on “Text Analytics for Legal Compliance.” That exchange is still a good read. Here are his thoughts on challenges for 2009:

  • Defining the business case for text analytics. In the current economic situation, organizations are clamping down on new projects and more than ever looking for hard ROI savings to justify investment. To pass the funding bar, text analytics solutions, which typically fall in the category of new projects undertaken for business optimization, need to come with solid business cases that demonstrate hard-dollar operational savings based on proven examples. Given the emerging nature of many text analytics solution areas, this will be a challenge to growth in 2009.
  • Creating fully interactive exploratory analytics. Most current text analytics approaches rely on extensive design-time configuration and customization. This has limited its applicability to use cases where categories of extracted entities, etc. are understood up front. An emerging class of investigation-centric applications, such as eDiscovery, requires text analytics that can be rapidly and seamlessly reconfigured by the business analyst as they explore data sets and interactively refine their understanding of what needs to be extracted. Further, these applications add pressure to democratize text analytics, as the business user can no longer wait for a linguistic or data processing expert to reconfigure the analytics each time a new insight is reached. The new challenge for text analytics is to enable extraction and analysis that are near instantaneous and at the same time are reconfigurable at interactive speeds by a business user without specialized text or linguistic expertise.
  • Expanding mainstream use cases for text analytics. To date, text analytics has had strong traction in certain niche solution markets. However, it has yet to be widely adopted as a horizontal capability powering broad business optimization use cases in content-centric (e.g., ECM), process-centric (e.g., BPM) or data-centric (e.g., BI) applications. As the market moves toward mainstream usage, text analytics technologies (and their relatives in text classification and search) will need to integrate more tightly into information and process management platforms – potentially being subsumed into them as native capability – and solutions will shift to leveraging analytics in conjunction with broader information management and technologies. 2009 will be a critical year for text analytics to start making this transition.
  • Other replies also looked at business conditions and at experiences in the adoption and use of text technologies. Lexalytics CEO Jeff Catlin foresees:

  • The poor economy will shift the sale of text analytics to larger companies. We're already seeing this in our sales, which used to be about two-thirds small companies and one-third large companies. Now it's mostly larger companies. The upside of this is that big companies are spending on search and text analytics as they seek to save money and increase efficiency due to lower staffing levels.
  • Technical push: Our push this year is going to be on empowering the customers with easy-to-use linguistic tools that will allow users to build and deploy some of their own text analytics bits and pieces using our core frameworks. The first of these for us is a user-driven entity recognizer that will allow users to mark up domain-specific text (let’s say medical text) with entities (diseases); and then after marking up a hundred or so stories, the system will build a "model" or recognizer for that type of entity so that it can deduce other diseases from how they’re described in the text. We'll be releasing our first version of this by late February/early March and hope to further enhance the tool over the course of the year to further empower the users. Our initial market research indicates that publishers/media would find such tools very valuable because of the amount of time/money they spend on maintaining lists.
  • I expect the number of vendors to contract as those with weaker or narrower offerings find it increasingly difficult to sell in this very challenging environment. To make a go of it in 2009, it seems that at a minimum, vendors need to provide: entity extraction, concept extraction and possibly sentiment analysis.
  • Craig Norris, CEO at Attensity, similarly noted three varieties of challenge, in his case relating to technology, implementation and seeing opportunity in adverse business conditions:

  • Getting sentiment right: Many of the vendors in the text-analytics space have pretty good text classification technology – where the application classifies terms based on dictionary entries or predefined lists. The output looks good – but because only terms are looked at, there tend to be a lot of false positives – negations are missed, relationships (such as why someone is unhappy) are never found. Many customers have purchased technology that only goes this far. They will be challenged this year by consumers of the data who find the data is, in fact, wrong. Natural language processing (NLP) technology like Attensity's not only finds sentiment; it can find sentiment in context, determining accurately whether it's negative or positive, the degree [or intensity] of it, and the “why” behind it.
  • Getting to Action: Another challenge this year will be around getting past high-level views of the data (general ratings of sentiment, general views of issues) to the root cause so that users can take action and companies can get real value. For example, knowing not only that a customer is unhappy with a product, but also that they intend to return it if they don't get a call back or a fix for their problem is critical to being able to remedy a situation and save a customer. Being able to know this is where the real ROI is for text analytics products. This is only possible with an approach that not only finds sentiment but can also understand the relationships between the issues and the reasons for the issues.
  • The economy: Certainly the economy will be a challenge for any technology solution vendor this year. Our offering has proven to enable two things to help companies in a tight economy: revenue preservation – find out if customers are going leave, understand why and save them; and revenue growth – understand if customers are having issues ("cries for help") and delight them by acting on those issues. This generates amazing word of mouth and ultimately growth.
  • Solutions Focus

    Vendor prognosticators with a solutions focus include Keith Collins, CTO at SAS, and Manya Mayes, SAS Chief Text Mining Strategist, see as challenges for 2009:

  • A broader set of vertical/horizontal offerings including more automated unstructured (text, voice, image) capabilities must be delivered for customer/product/competitive intelligence. SAS is doing this, for example, with SAS Text Miner integration into SAS Warranty Analysis. Automated capabilities include graphics, sentiment analysis, net promoter scores, and key performance indicators (KPIs) from text analysis results.
  • Solutions providers must take text analytics and search across the breadth of their offerings: For SAS, text+DI [data integration], text+BI[business intelligence], text+DataFlux, text+JMP, text+SOO [Service Operations Optimization], etc. SAS customers are requesting these capabilities, and we are building software and planning road maps accordingly.
  • Customers are consolidating software and needing to settle on one vendor that can handle all approaches to text – text analytics/mining/business analytics/search/categorization.
  • Collaboration and mobile BI are critical needs, and vendors need to be agile and move technology swiftly to meet the needs of consumers.
  • Yves Schabes, President of Teragram, a SAS company, focuses his response on one particular challenge – increasing workers’ efficiency:

    Given the current economic situation, large organizations are forced to keep up a fast pace of business with fewer resources, and it is therefore critical that these organizations have all of their content organized correctly so that employees can spend more time fulfilling their jobs and less time searching for information on their enterprise's system.

    Neil Hartley, CEO of Leximancer, sees “an industry that has been around for a long time and yet has seen little in the way of being operationalized within business processes.” Neil continues:

    Yes, there are proofs of concept, run by experienced vendor staff but these [prototypes], in my experience, rarely get adopted by the business a) because of the setup and maintenance required, and b) because the source data vocabulary changes making the initial setup redundant. Autonomy is the exception, but their deployments are largely search/retrieval-based and less qualitative.

    What the business needs is a high degree of automation (without the need for excessive setup and maintenance) together with clarity in the analysis and the ability to apply control to the process where needed. This is exactly what Leximancer provides.

    The other major trend I see is the need to make social media actionable, and this is something we’ve focused on heavily on our blog. I think customer attitudes on social media or microblogging sites are a leading indicator for the business. The business that waits for these trends to be reflected in their formal feedback programs may find it is too late to take effective action.

    Technology and Applications

    I had expected that a higher proportion of 2009 “challenges” responses would center on the technology and its applications. Responses I’ve already quoted touch on those areas with perhaps greater attention to business and market concerns. Eric Martin, Product Marketing Manager at SPSS, focused solely on solutions, which to me reflects both Eric’s confidence in his company’s market position and his own background: Eric earned a Ph.D. in immunology and cellular biology and, like many others, started using text mining on biomedical articles before joining text analytics pioneer LexiQuest, which was later acquired by SPSS. Challenge areas Eric sees for 2009 are:

  • Blog analysis: Already hot and will probably get bigger in 2009. There's a lot of confusion though between blogs and other kind of user-generated content or Web 2.0 data. That's also very solution-oriented and not only requires text analytics but also data mining, Web scraping capabilities, etc.
  • Sentiment analysis: Not an option anymore in most projects. Part of Voice of the Customer, blog analysis applications, etc. High quality out-of-the-box results are demanded more and more.
  • Automated translation: Also getting more and more market traction prior to text analysis.
  • Matthew Hurst of Microsoft Live Labs is likewise a techie at heart, and he’s also very interested in user-generated and other online content. Matthew says, “As reported elsewhere in research and industry literature, the majority of textual data being published online today is from the many genres of social media. To fully leverage this data, the key challenges are:

  • Comprehensive and complete data acquisition: Due to the social nature of this content, missing documents or authors is like missing the replies to answers or key voices in the choir.
  • Tuning or recreating standard tools to deal with less formal content: Tokenization, sentence segmentation, part-of-speech (POS) tagging, parsing, etc. all have different qualities and requirements in the social space.
  • Development and upkeep of broad (product) ontologies: This is a key requirement for grounding any analytics.
  • Lastly, Ren Mohan, Co-Chairman and CTO of IxReveal, replied not only with the three challenges his company hears most often from our clients, but also with a couple of “megatrends” he is beginning to see. Challenges are:

  • Speed to implementation: Clients remain concerned about implementation taking months before realizing any benefit. Immediate payback, more like a couple of weeks, seems to be the mantra!
  • Systems should dynamically and quickly react to changing new information: Clearly, clients do not want to go through an extensive process of rebuilding their analysis when requirements for analysis change. All our customers keep changing their analysis as new information unfolds.
  • Finding actionable intelligence in data that is not grammatical: Less time in call handling, texting, little patience, quick notes by data sources – this implies an increasing amount of text data has embedded meanings but is less grammatical. So, our clients are asking us to help them analyze such data as well.
  • Ren’s “megatrends” are:

  • The line between structured and unstructured is blurring and a new trend is emerging: Users want a new approach altogether. As opposed to converting unstructured to structured, users would like to see it the other way, and they want it quickly and easily. In the ISO [insurance industry] fraud/claims management conference, the keynote presenter asked how many wished their systems could respond with Google-like access and ease; 100% of the audience raised their hands. When he asked how many of their systems behave like that today, no hands went up! They are all using SQL. So, we see a new trend in analysis going from structured (SQL like complexity) to unstructured (search like simplicity)!
  • Thus far, text analytics has been available only for mega corporations! Now, the trend is catching on in the consumer/desktop user world as well. Products like uReka! from IxReveal and SearchWiki from Google are examples. They give users a way to store and reuse search queries and links, and personalize them. This is the beginning of analysis as users have to now think (analyze) about content databases, links, social networks, etc.
  • Ren’s conclusion: “These are truly exciting times!” Indeed.

    A Live Challenge?

    Here are thoughts on one more potential challenge for 2009, a live challenge, of a different variety.

    As an aside while forwarding his take on 2009 text-analytics challenges, Lexalytics’ Jeff Catlin suggested a bake-off to be held at the 2009 Text Analytics Summit, which is slated for June 1-2 (preceded by tutorials) in Boston. Jeff says in his blog, “Our market is still dominated by too much flashy marketing and not enough down in the dirt numbers for ‘apples to apples’ comparisons.” Jeff is right, although I’ll add that I believe that most of the flashy marketing actually fronts very capable products. Perhaps we can get something going for this year – a commercially oriented version of the TREC (Text REtrieval Conference) challenge. Stay tuned, and do consider attending this year’s summit.

    And if you’re a current or prospective text analytics user and would like to tell me about challenges you face or expect to face, please do get in touch by email at grimes@altaplana.com or by phone at (301) 270-0795).

    SOURCE: Perspectives on Text Analytics in 2009

  • Seth GrimesSeth Grimes

    Seth is a business intelligence and decision systems expert. He is founding chair of the Text Analytics Summit and principal consultant at Washington, D.C., based Alta Plana Corporation. Seth consults, writes, and speaks on information-systems strategy, data management and analysis systems, IT industry trends, and emerging analytical technologies. Seth chairs the Sentiment Analysis Symposium and the Text Analytics Summit.

    Editor’s Note: More articles and resources are available in Seth's BeyeNETWORK Expert Channel. Be sure to visit today!

  • Recent articles by Seth Grimes


    Are You Using the Right Tools for Your Big Data Projects? | killexams.com real questions and Pass4sure dumps

    Data scientists rely on tools/products/solutions to help them get insights from data. Gregory Piatetsky of KDNuggets conducts an annual survey of data data professionals to better understand the different types of tools they use. Here are the results of the 2015 survey. He followed his initial posting with additional analyses to better understand which tools go together. In the latter posting, he looked at pairs of tools and found that some tools tend to go together (usage of tools are correlated with each other). He offered the anonymized raw data set for free to encourage other people to analyze the data, which I did.

    Dimension Reduction through Principal Components Analysis

    His approach looked at pairs of tools to understand their relationship with the other. I took a slightly different approach. I applied principal components analysis. The current approach groups the tools by looking at the relationship among all tools simultaneously. In general, principal components analysis examines the statistical relationships (e.g., covariances) among a large set of variables and tries to explain these correlations using a smaller number of variables (components).

    The results of the principal components analysis are presented in tabular format called the principal component matrix. The factor matrix is an NxM table (N = number of original variables and M = number of underlying components). The elements of a principal component matrix represent the relationship between each of the variables and the components. These elements represent the strength of relationship between the variables and each of the underlying components. The results of the principal components analysis tell us two things:

  • number of underlying components that describe the initial set of variables
  • which variables are best represented by each component
  • Results

    This use of principal components analysis is exploratory in nature. That is, I didn’t impose a pre-defined structure on the data itself. The pattern of relationships among the 95 tools drove the pattern of results. While human judgment comes into play in the determination of the number of components that best describe the data, the selection of the number of components is based on the results. The goal of the current analysis was to explain the relationship among the 95 tools with as few components as was necessary. Toward that end, there are a couple of rules of thumb that I used to determine the number of components using the eigenvalues (output of principal components analysis). The first rule of thumb is to set the number of components based on the number of eigenvalues greater than unity (1.0). Another way is to plot (called a scree plot) the 95 eigenvalues to identify a clear breaking point along the eigenvalues.

    The plot of the eigenvalues appeared to break around the 13th and 14th eigenvalue. Therefore, I chose a 13-factor solution to explain the relationships among the 95 data science tools.

    Table 1. Principal Component Matrix of 95 Data Science Tools - data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.Table 1. Principal Component Matrix of 95 Data Science Tools – data from KDNuggets 2015 annual survey of data professionals. Click image to enlarge.

    Based on a 13-factor solution, the principal component matrix (see Table 1) was somewhat easy to interpret. Some of the cell values in the matrix in Table 1 are bold to represent values greater than .33. The components’ headings are based on the tools that loaded highest on that component. For example, four IBM products loaded highly on component 6, showing that usage of these tools by a given respondent tend to go together (if you use one of IBM’s tools, you tend to use the other(s)); as a result, I labeled that component as IBM. Similarly, based on the tools that were highly related to the other 12 factors, the other 12 factors were labeled accordingly.

    Tool Groupings

    The results suggest that 13 tool grouping describe the data. I’ve listed the groupings below and include the tools that describe each grouping (if number of votes are greater than 20). Tools that fall within a specific group tend to be used together.

  • Hadoop, HBase, Hive, Mahout, MLlib, Other Hadoop/HDFS-based tools, Pig, Scala, Spark, SQL on Hadoop tools
  • Microsoft Azure ML, Microsoft Power BI, Microsoft SQL Server, Revolution Analytics
  • Dataiku, H2O (0xdata), Python, scikit-learn, Theano, Vowpal Wabbit
  • JMP, SAS Base, SAS Enterprise Miner
  • Gnu Octave, MATLAB, Orange, R, RapidMiner, Rattle, Weka
  • IBM Cognos, IBM SPSS Modeler, IBM SPSS Statistics, IBM Watson Analytics
  • Actian, C/C++, Perl, SQLang, Unix shell/awk/gawk
  • Caffe, Pylearn2
  • Pentaho and QlikView
  • Datameer and Zementis
  • XLSTAT for Excel
  • Other Deep Learning tools, Other free analytics/data mining tools, Other Hadoop/HDF-based tools, Other paid analytics/data mining/data science software, Other programming languages
  • C4.5/C5.0/See5, Miner3D, Oracle Data Miner
  • The use of specific Big Data, data mining and data science tools tend to occur together. Based on the current analysis of tool usage, the 90+ tools can be grouped into a smaller subsets of tools. The results of the current analysis are somewhat consistent with the prior results. For example, Piatetsky found that Pig usage was closely associate with Hadoop, Spark, scikit-learn, Unix shell/awk/gawk and Python. In the current analysis, we found that Pig was also associated with Hadoop and Spark. However, we found that the use of Pig was associated with HBase, Hive, Mahout, MLlib, Scala and SQL on Hadoop. It’s important to note that the former analysis used the top 20 tools while the latter analysis used all 90+ tools; this difference could explain the differences between the two analyses.

    To improve your chances of success in your Big Data projects, it’s important that you select the right data tools. No single tool will do it all, and data professionals tend to use more than one data-related tool (in this study, we found that, on average, data professionals use 5 data tools). One way to help in your selection process is to identify the tools sets that other data professionals are using. The results suggest that you consider looking at tools within components as potential candidates for tools you might consider.

    Some of the tool groupings are simply based on the parent company of the tools, including IBM, Microsoft and SAS. Perhaps cross-selling data science tools is a lot easier within a major brand than it is across different brands. This makes sense to the extent that a company’s products likely work together better than with other vendors’ products. Still, more research is definitely needed to understand why certain tools tend to be used together.

    I’m interested in seeing what others find with these data. Here is the link to the anonymized data set (CSV format).



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