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C2020-013 | IBM SPSS Modeler Data Mining for Business Partners v2

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C2020-013 - IBM SPSS Modeler Data Mining for Business Partners v2 - braindump

Vendor IBM
Exam Number C2020-013
Exam Name IBM SPSS Modeler Data Mining for Business Partners v2
Questions 25 Q & A
Recent Update October 12, 2018
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C2020-013 exam Dumps Source : IBM SPSS Modeler Data Mining for Business Partners v2

Test Code : C2020-013
Test Name : IBM SPSS Modeler Data Mining for Business Partners v2
Vendor Name : IBM
Q&A : 25 Real Questions

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IBM IBM SPSS Modeler Data

IBM (IBM) pronounces Collaboration with NVIDIA (NVDA) to extend Open source computing device researching equipment for statistics Scientists | killexams.com Real Questions and Pass4sure dumps

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IBM today introduced that it is bettering a few of its present application products with the Apache Spark open-supply statistics processing engine, including the SPSS predictive analytics utility.

IBM purchased SPSS for $1.2 billion in 2009. SPSS itself all started at Stanford university in 1968 and is commonplace in facts courses at universities.

IBM is especially incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman instructed VentureBeat in an email.

Spark is also being applied to IBM’s BigInsights, Streams, and DataWorks utility, in line with a press release.

The Sparkification of IBM comes after large Blue announced a huge commitment to the open-source application in June. at the time, IBM introduced Spark as a provider on its Bluemix cloud; that carrier is now often accessible.

IBM has made more than 60 contributions to Spark considering the June announcement, in line with the statement.


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C2020-013 exam Dumps Source : IBM SPSS Modeler Data Mining for Business Partners v2

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Q&A : 25 Real Questions

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IBM SPSS Modeler Data Mining for Business Partners v2

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What you need to know about predictive analytics | killexams.com real questions and Pass4sure dumps

Predictive analytics

In an era when data is seen as an increasingly valuable commodity, the tools and techniques used to analyze that data gain extra importance.

Historically analysis, even using computers, has been something of a labor intensive task because the raw data needed to be verified and complex models built to process it. But due to the growth of machine learning and data mining techniques we’re now seeing an increase in predictive analysis where machines can take historical and current information and apply it to a model to predict future trends.

Whilst in business terms predictive analytics is primarily about looking to the future -- spotting sales trends for example -- it has other uses too. It can be used to spot trends that might indicate fraudulent credit card use for example. As with any other type of data processing the quality of the results depend heavily on the integrity of the data and the quality of the underlying model.

Analytical Models

Predictive analysis relies on the use of models to "score" data. These models come in three main types. The most common is the predictive model which uses sample data with known attributes to train the model so that it can then look at new data and determine how it’s likely to behave. This information can be used to predict customer behavior or spot patterns of activity.

Descriptive models are used to classify data into groups. Where predictive models focus on the behavior of an individual customer, to determine credit ratings for example, descriptive models sort customers by characteristics like their age or their past buying behavior. This information can then be used in marketing campaigns aimed at specific groupings.

Decision models take a wider view, taking account of the known data as well as results from predictive models. This allows them to forecast the results of decisions that involve a large number of different variables.

Within these models various statistical techniques are at the heart of processing the data. The regression model is the most common, used to define the relationship between variables. In addition time series modeling is used to forecast the way variables will behave in future.

Technology

Predictive analysis is closely tied to other technologies, in particular machine learning. Data mining too is an important part of unlocking the value contained in business systems. As more powerful systems become readily available technologies like neural networks will be applied to build more complex analysis models.

Commercial analytics tools are available from many of the big IT suppliers. These include SAP HANA, Oracle Advanced Analytics and IBM SPSS Modeler. Open source tools are also on offer from developers including GNU and Apache.

Business Uses

Predictive analytics has been used in many areas of business in recent years. It’s frequently found in customer relationship management (CRM) systems where it can help in building sales campaigns, or providing customer services. Analytics can spot buying patterns and identify problems that could lead to lost business.

The internet has made it much easier for customers to compare services and products from different suppliers, so increasingly companies are looking at how they can retain existing business. Predictive systems can help here too, by looking at buying records and service use patterns it’s possible to spot when someone is perhaps thinking about switching suppliers. They can then be targeted with marketing initiatives to persuade them to stay.

In some businesses predictive analysis is aimed more at preventing problems than at gaining new business. A 2014 survey by SAP revealed that just over 40 percent of financial services businesses used predictive analytics to minimize risk. We’ve already touched on the use of predictive systems to spot fraudulent activity, the US Internal Revenue Service is known to use predictive analysis on tax returns to spot suspicious activity, but they have other uses in this sector too. Predictive systems can identify those customers who are likely to make late payments for example and help focus collection efforts. For insurance providers the technology can be used in the identification of risk and thus help to calculate premiums more accurately.

The medical sector is also starting to use predictive analysis techniques. On a macro scale they can be used to predict what proportion of the population is likely to develop certain conditions such as heart disease or diabetes. But they can also support clinical decision making, using patient data to plan long-term treatment.

Another field where predictive analytics has the scope to make a big difference is project management. Here it can be used to predict the risks of project failures, overspends and so on, data which can be used by managers to keep things on track.

Security too is an area where this type of analysis is going to gain in importance. The latest behavioral systems are able to analyze the way a person uses a computer or phone and accurately predict when an account sign on is being used by someone else. This has implications not just for securing systems but for policing things like pay walled websites.

The Future

There is some skepticism surrounding the value of predictive analytics, much of this surrounds the importance of using data in context. Lots of factors influence individual behavior and many of these -- such as weather conditions or someone having had an argument with their partner -- are impossible to factor into even the most sophisticated model.

But despite these doubts business leader are keen to take advantage of the opportunities predictive technology can offer. "The impact of integrating real-time analytics with business operations is immediately apparent to business people because it changes the way they do their jobs, says Jim Sinur, research vice president at Gartner. "The most dramatic change is the increased visibility in how the company is running and what is happening in its external environment. Individual contributors and managers have more situational awareness, so they are able to make better decisions faster".

Our everyday world is more and more driven by data and although there may be limitations in what predictive systems can achieve, that data can still offer valuable insights. Businesses need to remain competitive and by helping them to identify opportunities, improve customer service and minimize risks predictive analytics is almost certain to play a key role.

Published under license from ITProPortal.com, a Future plc Publication. All rights reserved.

Photo Credit: one photo/Shutterstock


Spss Inc. Moves Beyond Statistics, | killexams.com real questions and Pass4sure dumps

Last year SPSS Inc. celebrated its 30th anniversary, making the Chicago-based company one of the nation's longest-surviving software firms. It's also one of the biggest. Software Magazine's list of the world's top 500 software vendors has SPSS at No. 97, and Crain's Chicago Business ranks SPSS as the city's 4th largest software company.

Most statisticians, math students and social science researchers are familiar with SPSS's flagship software, which offers digital tools for analyzing and modeling large sets of numbers. Over the years, the company has expanded its product offerings to extend into the corporate sector, and a series of recent company acquisitions has enabled SPSS to emerge as a leader in the so-called "business intelligence" market.

Jack Noonan, SPSS president and CEO, spoke to Silicon Prairie about the challenges of turning SPSS into a global, multidimensional firm, the potential of the business intelligence market and how to stay alive in the software business for 30 years.

The following is a transcript of the interview, edited for length and clarity.

Q: Exactly what is business intelligence?

A: Business intelligence focuses on turning data into useful decision-making information. It's a set of data-centric applications that do some pretty basic things. We report on data, we classify customers and products into groups and we forecast how well we'll do in the future.

Q: Is there a difference between business intelligence and data mining?

A: Data mining is a subset of the business-intelligence marketplace. It is specifically focused on the modeling part of these three pieces of information. Data mining has some reporting in it, but it's typically the kind of reporting that you would do to understand variance and the distribution of data. Typically, data mining is focused more on classification and forecasting, so it's more of a modeling kind of activity.

Q: How did SPSS get into the business of business intelligence?

A: When I joined the company (January 1992), about a third of our revenue was from the corporate environment, about a third was from the academic environment, about a third from government. Now almost 60 percent of our revenue comes from the commercial environment, so the uses of our product have evolved into the commercial market space to actually solve business problems. SPSS historically was focused on survey research and analyzing data that was gathered through a survey process. Well, the information you have in your sales force automation systems about your customers actually is survey data, but it's collected during the sales cycle. And it's exactly the same kind of data you would have collected about your customers in the survey process. So data mining is taking this customer data and turning it into useful decision-making information--the industry is just changing the names to protect the guilty. Three years ago our customers called the activity they were involved in database analysis. Now, they're doing nothing different than they were doing three years ago, but they're calling it data mining today.

Q: Who are going to be the major players in the business-intelligence market in the next five years?

A: There are three companies that have rated the data-mining players of the business intelligence marketplace: META Group, Forrester Research and International Data Corp. And we're rated either No. 1 or No. 2 by those three entities. The other two players are SAS Institute and IBM. They're also forecasting the business intelligence market growth to be anywhere from 40 to 50 percent per year.

Q: SPSS has bought several companies in recent years, with Integral Solutions being the latest. Is there an overall strategy to your acquisitions?

A: We're averaging two or three acquisitions a year, and since '95 we've done about nine or ten. The analytic software market is consolidating and we've positioned ourselves as one of the consolidators. We are looking for companies and technologies that are involved in extracting data from transaction systems, cleaning data, reporting on data, visualizing data, analyzing that data and then distributing it throughout the enterprise via techniques like the World Wide Web.

Q: What is your response to the CNBC report that SPSS possibly is an acquisition target by IBM or Oracle?

A: We're not in acquisition discussions with anyone. The only acquisition discussions we're involved in is us acquiring folks.

Q: This year, you've released SPSS data mining and analysis software for the AS/400. Why did you wait this long before entering this market?

A: The AS/400 is proprietary technology that is supported by a number of software vendors that supply turnkey applications, whether it's for specific financial applications or applications to run an entire small company. That's where AS/400 has been focused and it's been a closed community. But this market has opened itself up and it's another opportunity to do business intelligence or data mining. There are hundreds of thousands of AS/400s out there, so the market is very large. Minnesota-based ShowCase Corp. has ported some of the business intelligence software supported by Hyperion Solutions to the AS/400. They've just gone public and they are very successful in this marketplace, so they've opened my eyes.

Q: SPSS is 30 years old--ancient for a software company. How have you managed to survive for so long?

A: I think our success is partly because of our technology and partly because we have been able to successfully move a set of needed technology to the platforms the users are choosing. We started out 30 years ago supporting our constituency on mainframe computers. Over time, the mid-range computer came into being, and this became the platform of choice for researchers, so we moved our technology to the mid-range. Then the desktop came out and this was the platform of choice and we moved our technology to the desktop. Now you see the current wave is to the server or the Web environment, and you're seeing that we're moving our technology there. Also, statistics do not rust. All we need is to stay current on the technology, the platform of choice and the user interface of choice.

Q: Software companies have a very high failure rate. What are so many doing wrong?

A: I think the No. 1 reason for software companies failing over the last dozen years has been their inability to move to new platforms quickly--specifically, the Windows environment. A number of companies failed because they didn't get there fast enough, and we're seeing the same thing with the Web today. Desktop applications are being replaced by browser-based applications. We have a whole new platform, so I believe you're going to see a number of failures. The second reason software companies fail is the value of their offering no longer has value because of changes in the marketplace. Specifically, the hardware may change. Companies a few years ago did compression technology because the disk drives weren't big enough. Not a problem today--no one talks about compressing their data anymore. Their market literally dried up because there's no more value in their software.

Q: What's coming up for SPSS in the future?

A: There are two things we're focusing on. One is scalability, handling larger and larger sets of data; our new distributed architecture is supporting that. You will see us support the major Unix platforms and mainframe platforms with our server-based products. You will also see us focus on helping our customers deploy our technology throughout their organizations, typically using-Web based technology.



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Issu : https://issuu.com/trutrainers/docs/c2020-013
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Blogspot : http://killexamsbraindump.blogspot.com/2017/11/never-miss-these-c2020-013-questions.html
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publitas.com : https://view.publitas.com/trutrainers-inc/real-c2020-013-questions-that-appeared-in-test-today
zoho.com : https://docs.zoho.com/file/60eu6c7d94a0bef7c4b05bf2d3e6836692ec4






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