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IBM SPSS Statistics Product capabilities overview 3.3.2016

IBM SPSS Statistics SPSS Stats... · 2016-03-23 · IBM SPSS Statistics overview IBM SPSS Statistics software is used by a variety of customers to solve industry specific business

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IBM SPSS StatisticsProduct capabilities overview 3.3.2016

Analytics

© 2016 IBM Corporation2

Session agenda

IBM SPSS Statistics positioning4

IBM SPSS Statistics overview1

IBM SPSS Statistics usage2

Example use cases3

Analytics

© 2016 IBM Corporation3

Session agenda

IBM SPSS Statistics positioning4

IBM SPSS Statistics overview1

IBM SPSS Statistics usage2

Example use cases3

Analytics

© 2016 IBM Corporation4

IBM SPSS Statistics overview

Statistical approach involves

– forming a theory about a possible

relationship

– converting it to a hypothesis

– testing that hypothesis using statistical

methods

It is a manual, user-driven, top-down

approach to data analysis

Data mining involves

– the interrogation of the data

– determined by the method and

goal, rather than by the user

It is a data-driven, self-organizing,

bottom-up approach to data analysis that

works on very large data sets

Statistics Approach Modeling Approach

Both approaches drive predictive analytics

IBM SPSS Statistics IBM SPSS Modeler

“Statistical Modeling: The Two Cultures,” Leo Breiman, Statistical Science, 2001, Vol.16 (3), pp.199-231.

Analytics

© 2016 IBM Corporation5

IBM SPSS Statistics overview

• Hypothesis testing, advanced

statistical analysis

• Top-down approach

• Spreadsheet-like look & feel

• General environment for predictive

analytics and statistical analysis

• Well-suited for ad-hoc analysis

– Core descriptive statistical capabilities

– Advanced statistical functions

– Many types of regression

– Charting and mapping capabilities

– Tabular analysis & output

Analytics

© 2016 IBM Corporation6

IBM SPSS Statistics overview

Quickly understand large and complex

datasets using advanced statistical

procedures ensuring high accuracy to

drive quality decision-making

Reveal deeper insights and provide

better confidence intervals via

visualizations

Solve complex business and

reseach questions via means of

statistical analysis and assumption

validation

Programmability for advanced users that

leverages common statistical programming

languages in the market (Python, R)

Analytics

© 2016 IBM Corporation7

IBM SPSS Statistics overview

IBM SPSS Statistics software is used by a variety of customers to solve industry specific business issues to drive quality

decision-making. Methods like forecasting, analyzing trends and assumption validation can provide a robust, user friendly

platform to understand your data and solve complex business and research problems.

IBM SPSS Statistics helps organizations like Lloyds TSB, Kent State University and the IRS have expanded their markets,

improved research outcomes while ensuring regulatory compliance and managed risk (evaluate programs, prevent crimes

and assess loan risks).

Organizations have a core need to understand data and the statistical analyses applied to that data, and to quickly and

accurately analyze and interpret data to drive decision-making. In a variety of industries and applications, there is a need to

confirm (or deny) the existence of trends in data. IBM SPSS Statistics is the world’s leading statistical software used to

solve such business and research problems by means of ad-hoc analysis, hypothesis testing and predictive analytics.

Analytics

© 2016 IBM Corporation8

Session agenda

IBM SPSS Statistics positioning4

IBM SPSS Statistics overview1

IBM SPSS Statistics usage2

Example use cases3

Analytics

© 2016 IBM Corporation9

IBM SPSS Statistics usage SPSS Statistics is typically used by a statistician, who knows the technique to use (regression, decision tree, correlations,

etc.), and will:

1) Build descriptions from the data, e.g.

• How many customers are male, or from a certain age group

• Average amount per transaction

• How many students passed last year

• How many goals scored per match, on average

• What information is in my data?

2) Build inferences from the data

• Based on 1500 voters polled, who will win election?

• Based on past student retention rates, how many will finish the year?

• Based on revenue per quarter over X years, how much we can expect to bring in this quarter?

• Base on average points per game, what will the team (or player) score in the next game?

• Are our assumptions correct?

• Based on a sample of a population, what does the rest of the population look like?

• Based on a sample in time, what is the likely outcome in the future?

• What things actually have an affect on the results?

Analytics

© 2016 IBM Corporation10

IBM SPSS Statistics bundles

Statistics Base

Advanced Statistics

Custom Tables

Regression

StandardStatistics Base

Advanced Statistics

Custom Tables

Regression

Data Preparation

Missing Values

Categories

Decision Trees

Forecasting

Statistics Base

Advanced Statistics

Custom Tables

Regression

Data Preparation

Missing Values

Categories

Decision Trees

Forecasting

Bootstrapping

Conjoint

Exact Tests

Neural Networks

Direct Marketing

Complex Samples

Viz Designer

Amos

Sample Power

Professional Premium

Analytics

© 2016 IBM Corporation11

Session agenda

IBM SPSS Statistics positioning4

IBM SPSS Statistics overview1

IBM SPSS Statistics usage2

Example use cases3

Analytics

© 2016 IBM Corporation12

CRI now makes smarter clinical decisions that help patients and improve performance

The need

To advance clinical practice in the mental health sector,

Centerstone Research Institute (CRI) wanted to use emergent

analytics technologies to bridge the gap between researchers

and healthcare providers.

The solution

With the implementation of IBM predictive analytics software,

CRI built a framework that is able to predict which services are

likely to work best for which individual.

Real business results

Provided a potential 42% improvement in patient outcomes

Enabled cost savings of 58% in the cost per unit of outcome

change

Bridged the gap between researchers and physicians,

transforming the way mental health services are provided

Solution components:

IBM SPSS Statistics

IBM SPSS Modeler

"With our expertise in Big Data management, and IBM’s leading-edge analytics technologies, we’re well positioned to help

shift the paradigm for mental health services."

—Tom Doub, CEO, Centerstone Research

Institute

http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=J053479Y94979F29

Centerstone Research Institute

(CRI) is a not-for-profit research

organization dedicated to improving

the quality and effectiveness of care

for individuals with mental health and

addiction disorders.

Headquarters:

• Bloomington, Indiana

Patients served per year:

• 70, 000

Research Partners:

Harvard University Medical Center

Vanderbilt University

Indiana University

Northwestern University

University of Illinois of Chicago

Meharry Medical College

Janssen Pharmaceuticals

Telesage, Inc.

Analytics

© 2016 IBM Corporation13

Fondazione IRCCS Istituto Nazionale dei Tumori (INT) personalized cancer care

The need

Fondazione INT, a leading cancer and research center in Milan, wanted to improve patient care by

tailoring treatment approaches to specific individuals. The institute needed the ability to analyze

past treatments and cases, and combine that information with the patient’s personal statistics and

disease profile, to create a fact-based treatment plan for each patient. In addition, being able to analyze

overall outcome data would help the institute provide more cost-effective, efficient care for its patients.

The solution

The IBM solution proactively shows the physician statistics on similar clinical cases, possible

alternative treatments and predicted outcomes for each. Knowing this, physicians can ensure that

patients receive only the procedures they need.

Real business results

Avoids unnecessary treatment (estimated to be up to 60% of all treatment)

Increases the chances of successful outcomes by creating personalized treatments

Improves hospital performance, both clinical and operational, by streamlining processes and

lowering costs

"By providing our physicians with vital input on what worked best for patients with similar clinical characteristics, we can help improve treatment effectiveness and the final patient outcome.”

—Dr. Marco A. Pierotti, Fondazione INT

Solution components:

IBM SPSS Statistics

IBM SPSS Modeler

IBM Cognos BI

Analytics

© 2016 IBM Corporation14

Steno Diabetes Center ensured confidence in the validity of research

The need

Steno Diabetes Center is a medical research institution in Denmark, specializing in diabetes.

To break new ground in its research into diabetes and its complications, Steno needed to be

able to analyze complex data sets produced by research projects and clinical trials.

The solution

IBM SPSS Statistics provides data-handling and analysis capabilities that help Steno identify

significant factors in the development of disease and evaluate the effectiveness of

treatments.

Real business results

Increased ease of use for medical staff to combine data sets to perform complex

analyses, without help from statisticians or IT specialists

Trusted technology builds confidence in Steno’s published research

"The ability to handle and combine data sets from different cohorts of patients in IBM SPSS is very valuable... It’s a solid, mature and reliable platform, so we know we can trust it to deliver the correct results – and it gives the wider academic community more confidence in the validity of our studies.”

—Professor Peter Rossing, Chief Physician and Head of Research, Steno Diabetes

Center

Solution components:

IBM SPSS Statistics

Analytics

© 2016 IBM Corporation15

HUS analyzes sensordata to improve neonatal intensive care

The needA range of medical devices monitor the vital organs of neonatal infants within Neonatal Intensive CareUnits. Monitors attached to these devices display constantly changing data, usually at second-by-second intervals that caregivers must translate into actionable information. In addition to the Big Dataproblem, the early diagnosis of life threatening conditions is difficult because the clinical signs areusually vague and subtle until the condition is well established.

The solution

Applying machine learning algorithms to high frequency physiological data from patient monitors in

real-time to detect life threatening infections

Real business results

Life threatening conditions are detected sooner

Early warning gives caregivers the ability to proactively deal with complications

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Analytics

© 2016 IBM Corporation16

Session agenda

IBM SPSS Statistics positioning4

IBM SPSS Statistics overview1

IBM SPSS Statistics usage2

Example use cases3

Analytics

© 2016 IBM Corporation17

IBM SPSS Statistics positioning

Statistics

The study of the collection, testing, and interpretation of data. Analysis is generally

performed on a subset of data (sample) and the analyst/researcher performs a

particular technique/s to validate a hypothesis.

Data mining

The analysis and organization and manipulation of observational data to find

relationships and patterns. Analysis is generally performed on all data available with

the intention to predict future outcomes. Multiple methods are attempted/used to

gain the most accurate/profitable model, that is tightly linked to the ultimate use case

at hand.

IBM SPSS Statistics

IBM SPSS Modeler

Analytics

© 2016 IBM Corporation18

Use case specific tooling for analytics

Questions IBM SPSS Statistics IBM SPSS Modeler

What are you trying to achieve? Hypothesis testing Goal Focused, support ongoing

business process

Who will be responsible for doing

it?

Analytics background (stats,

maths etc)

Data scientist (domain and

machine learning expertise)

What type of data will you be

using for the analysis?

Structured data Structured and unstructured data,

from several sources

Where does it reside? Excel, text files, small database Data warehouse, databases,

some flat files

How big is the data? Small data (usually 100’s or

1000’s, <1Mill)

Larger data (usually > 1000’s, to

Many millions)

How often do you want/need to

perform the analysis?

Ad-hoc Repeatable (weekly, monthly,

daily, real time)

What happens to the

results/output/score?

Reporting on results/findings (xls,

ppt, pdf)

Deploy results into some other

systems

Analytics

© 2016 IBM Corporation19