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ITECH7407 Real Time Analytics - Data Analytics Assignment, Federation University, Australia

This is a business analytics project aimed at generating innovative analytics solutions for a Company. The objective is to analyze the given datasets from a relevant firm's perspective in terms of implications and strategies which the chosen company could adopt to improve its functions, resources and processes efficiently and effectively.

Task 1- Background information - Write a description of the selected dataset and project, and its importance for your chosen company. Information must be appropriately referenced.

Task 2- Create Data View/Cube - Data Modelling and Provisioning - Upon selecting the dataset, convert the dataset into HANA data cube/view by following the SAP HANA data modeling and provisioning technique using SAP HANA Web-based development workbench.

Task 3 - Perform Data Mining on data view - For your project, perform the relevant data analysis tasks on data view/cube (created in Task 2) using data mining techniques such as classification/association/time series/clustering and identify the BI reporting solution and/or dashboards you need to develop for the operational manager of the chosen company.

Task 4 - Research - Justify why you chose thee BI reporting solution/dashboards/data mining technique in Task 3 and why those data sets attributes are present and laid out in the fashion you proposed.

Task 5 - Recommendations for CEO - The CEO of the chosen company would like to improve their operations. Based on your BI analysis and the insights gained from your "Dataset" in the lights of analysis performed in previous tasks, make some logical recommendations to the CEO, and justify why/how your proposal could assist in achieving operational/strategic objectives with the help of appropriate references from peer-reviewed sources.

Task 6 - Cover letter - Write a cover letter to the CEO of the chosen firm with the important data insights and recommendations to achieve operational/strategic objectives.

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Answer - WORLD HEALTH CARE INVESTMENT GOVERNMENT SECTOR INVESTMENT

Introduction

In this assignment we are analysis the health expenditure ratio in current scenario . The expenditure ratio gap is increasing or decreasing from last 10 year of (%of GDP). This is big data ,collected from world bank data. This Big data we gathered from the website is in the format of csv. This assignment shall have discussing about the predictive tool and their usages. The current trends is showing prediction or forecasting about the trends in any sector. The Big data analysis is a helpful for knowing the future trends .Our aim of report is , we use SAP analytical tool for our collected data. . In this article we are predictive World Health Organization Global Health Expenditure and analysis the Current health expenditure In this report. Our analysis showing the trend in health sector

Our report is discussing the information about primary health sector .The primary health sector need more expenditure therefore this will help to improvement of health .Our analysis is mainly focus on government investment is past 10 year as we have 10 year data.

SAP Analytical Tool and Data Model

We are using the SAP predictive tool that have basic functionality . The tool is menu driven . The tool having in-built algorithm that will help to analysis the big data . For the SAP tool when we open the tool first window show For the SAP tool when we open the tool first window show the numerous option for the working on Big data we select the expert analysis option . When we select the expert analysis option then a window is come's up that is asking tor loading the Big data . This window show for the Big data numerous type . We select from this window is excel data . According to selection excel data is load and a process is start for cleaning and loading the data . After the loading and cleaning the data a screen come's up that is showing prediction option.

Now this window saying we are ready to prediction on Big data using SAP in -built function.

The window show three option

1. Prepare

2. Predict

3. Visualization

We start with Prepare option fun Big data. Afterward we use Predict option and now we could saw the information using visualization option.

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Data Analytics Assignment.jpg

Data Analytics Assignment1.jpg

Data Analytics Assignment2.jpg

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This figure is showing the information year wise XY graph .We use SAP BI tool and extract the data year for each country and that is given below:

LOCATION

TIME

Value

AUS

2014

4300.383

AUT

2014

5059.599

BEL

2014

4612.01

CAN

2014

4538.199

CZE

2014

2469.139

DNK

2014

4872.49

FIN

2014

3931.38

FRA

2014

4666.074

DEU

2014

5160.105

GRC

2014

2133.652

HUN

2014

1810.395

ISL

2014

3784.891

IRL

2014

4939.523

ITA

2014

3250.499

JPN

2014

4245.202

KOR

2014

2290.805

LUX

2014

6689.863

MEX

2014

1020.82

NLD

2014

5277.928

NZL

2014

3511.49

NOR

2014

6157.628

POL

2014

1599.739

PRT

2014

2593.617

SVK

2014

1998.395

ESP

2014

3042.008

SWE

2014

5183.385

CHE

2014

7115.238

TUR

2014

1042.59

GBR

2014

3958.337

USA

2014

9027.752

CHL

2014

1731.705

EST

2014

1771.668

ISR

2014

2457.971

SVN

2014

2624.563

BRA

2014

1374.997

CHN

2014

658.039

COL

2014

859.99

IND

2014

205.885

IDN

2014

366.647

LVA

2014

1301.142

LTU

2014

1745.527

RUS

2014

1355.052

ZAF

2014

1044.913

CRI

2014

1188.894

AUS

2014

2896.647

AUS

2014

1403.736

AUT

2014

3744.639

AUT

2014

1314.96

BEL

2014

3607.855

BEL

2014

1004.155

CAN

2014

3193.934

CAN

2014

1344.264

CZE

2014

2041.65

CZE

2014

427.489

DNK

2014

4102.126

DNK

2014

770.364

FIN

2014

2943.886

FIN

2014

987.494

FRA

2014

3568.978

FRA

2014

1097.096

DEU

2014

4354.249

DEU

2014

805.856

GRC

2014

1241.902

GRC

2014

864.325

HUN

2014

1214.74

HUN

2014

595.655

ISL

2014

3046.815

ISL

2014

738.076

IRL

2014

3476.796

IRL

2014

1462.727

ITA

2014

2458.859

ITA

2014

791.641

JPN

2014

3570.973

JPN

2014

674.229

KOR

2014

1345.949

KOR

2014

944.856

LUX

2014

5505.895

LUX

2014

1104.998

MEX

2014

528.706

MEX

2014

492.114

NLD

2014

4257.517

NLD

2014

1020.412

NZL

2014

2793.877

NZL

2014

717.614

NOR

2014

5251.776

NOR

2014

905.853

POL

2014

1130.359

POL

2014

469.379

PRT

2014

1713.921

PRT

2014

879.697

SVK

2014

1603.258

SVK

2014

395.138

ESP

2014

2141.562

ESP

2014

900.446

SWE

2014

4321.193

SWE

2014

862.192

CHE

2014

4498.989

CHE

2014

2616.25

TUR

2014

809.103

TUR

2014

233.486

GBR

2014

3152.497

GBR

2014

805.84

USA

2014

7345.899

USA

2014

1681.853

BRA

2014

610.597

BRA

2014

764.4

CHL

2014

1045.16

CHL

2014

686.546

CHN

2014

380.868

CHN

2014

258.05

COL

2014

623.106

COL

2014

236.884

CRI

2014

868.611

CRI

2014

320.283

EST

2014

1340.783

EST

2014

430.165

IND

2014

47.166

IND

2014

158.719

IDN

2014

143.58

IDN

2014

220.041

ISR

2014

1554.195

ISR

2014

872.516

LVA

2014

776.422

LVA

2014

524.72

LTU

2014

1179.29

LTU

2014

565.328

RUS

2014

837.24

RUS

2014

517.812

SVN

2014

1866.426

SVN

2014

758.137

ZAF

2014

449.03

ZAF

2014

595.882

AUS

2015

4413.979

AUT

2015

5159.345

BEL

2015

4589.464

CAN

2015

4632.837

CZE

2015

2392.635

DNK

2015

5000.768

FIN

2015

4098.952

FRA

2015

4656.575

DEU

2015

5296.994

GRC

2015

2187.656

HUN

2015

1861.888

ISL

2015

3964.208

IRL

2015

5106.253

ITA

2015

3292.267

JPN

2015

4428.349

KOR

2015

2481.587

LUX

2015

6496.939

MEX

2015

1037.424

NLD

2015

5148.399

NZL

2015

3523.953

NOR

2015

6239.435

POL

2015

1687.009

PRT

2015

2649.599

SVK

2015

2027.438

ESP

2015

3175.457

SWE

2015

5271.934

CHE

2015

7570.232

TUR

2015

1028.911

GBR

2015

4071.806

USA

2015

9491.4

CHL

2015

1803.128

EST

2015

1859.566

ISR

2015

2646.915

SVN

2015

2675.356

BRA

2015

1401.847

CHN

2015

762.113

COL

2015

856.457

IND

2015

238.098

IDN

2015

382.844

LVA

2015

1399.678

LTU

2015

1864.126

RUS

2015

1305.409

ZAF

2015

1090.164

CRI

2015

1234.481

AUS

2015

3010.696

AUS

2015

1403.283

AUT

2015

3824.239

AUT

2015

1335.106

BEL

2015

3598.054

BEL

2015

991.411

CAN

2015

3267.052

CAN

2015

1365.785

CZE

2015

1996.719

CZE

2015

395.915

DNK

2015

4209.806

DNK

2015

790.962

FIN

2015

3070.813

FIN

2015

1028.139

FRA

2015

3566.119

FRA

2015

1090.456

DEU

2015

4465.266

DEU

2015

831.728

GRC

2015

1274.979

GRC

2015

877.298

HUN

2015

1244.876

HUN

2015

617.012

ISL

2015

3203.972

ISL

2015

760.236

IRL

2015

3635.388

IRL

2015

1470.865

ITA

2015

2455.804

ITA

2015

836.464

JPN

2015

3723.488

JPN

2015

704.861

KOR

2015

1461.565

KOR

2015

1020.022

LUX

2015

5312.62

LUX

2015

1107.449

MEX

2015

541.211

MEX

2015

496.214

NLD

2015

4171.893

NLD

2015

976.506

NZL

2015

2783.549

NZL

2015

740.405

NOR

2015

5335.628

NOR

2015

903.807

POL

2015

1180.713

POL

2015

506.296

PRT

2015

1753.137

PRT

2015

896.463

SVK

2015

1616.195

SVK

2015

411.243

ESP

2015

2263.563

ESP

2015

911.894

SWE

2015

4393.487

SWE

2015

878.447

CHE

2015

4796.262

CHE

2015

2773.97

TUR

2015

803.928

TUR

2015

224.983

GBR

2015

3240.206

GBR

2015

831.599

USA

2015

7778.12

USA

2015

1713.28

BRA

2015

606.972

BRA

2015

794.874

CHL

2015

1095.979

CHL

2015

707.149

CHN

2015

433.046

CHN

2015

272.587

COL

2015

606.252

COL

2015

250.205

CRI

2015

925.402

CRI

2015

309.079

EST

2015

1406.151

EST

2015

453.331

IND

2015

59.013

IND

2015

179.086

IDN

2015

123.759

IDN

2015

259.085

ISR

2015

1659.709

ISR

2015

945.56

LVA

2015

798.324

LVA

2015

601.354

LTU

2015

1251.57

LTU

2015

611.387

RUS

2015

766.517

RUS

2015

538.892

SVN

2015

1919.384

SVN

2015

755.972

ZAF

2015

465.581

ZAF

2015

624.583

AUS

2016

4513.991

AUT

2016

5273.243

BEL

2016

4659.518

CAN

2016

4721.578

CZE

2016

2481.697

DNK

2016

5074.523

FIN

2016

4117.913

FRA

2016

4773.039

DEU

2016

5451.854

GRC

2016

2262.788

HUN

2016

1966.456

ISL

2016

4207.586

IRL

2016

5267.28

ITA

2016

3429.458

JPN

2016

4585.388

KOR

2016

2687.725

LUX

2016

6447.728

MEX

2016

1020.301

NLD

2016

5235.456

NZL

2016

3639.448

NOR

2016

6175.252

POL

2016

1784.262

PRT

2016

2782.688

SVK

2016

2170.342

ESP

2016

3256.72

SWE

2016

5347.612

CHE

2016

7823.965

TUR

2016

1092.466

GBR

2016

4164.248

USA

2016

9832.317

CHL

2016

1892.592

EST

2016

1987.951

ISR

2016

2725.22

SVN

2016

2771.083

LVA

2016

1597.334

LTU

2016

1992.29

RUS

2016

1304.732

CRI

2016

1236.798

AUS

2016

3083.391

AUS

2016

1430.6

AUT

2016

3908.287

AUT

2016

1364.956

BEL

2016

3672.174

BEL

2016

987.344

CAN

2016

3319.113

CAN

2016

1402.465

CZE

2016

2034.436

CZE

2016

447.261

DNK

2016

4268.811

DNK

2016

805.712

FIN

2016

3066.836

FIN

2016

1051.076

FRA

2016

3956.65

FRA

2016

816.389

DEU

2016

4612.3

DEU

2016

839.554

GRC

2016

1388.143

GRC

2016

868.857

HUN

2016

1302.6

HUN

2016

663.856

ISL

2016

3429.607

ISL

2016

777.979

IRL

2016

3796.125

IRL

2016

1471.155

ITA

2016

2553.719

ITA

2016

875.739

JPN

2016

3863.281

JPN

2016

722.107

KOR

2016

1589.921

KOR

2016

1097.804

LUX

2016

5218.14

LUX

2016

1155.251

MEX

2016

533.923

MEX

2016

486.378

NLD

2016

4239.49

NLD

2016

995.966

NZL

2016

2862.569

NZL

2016

776.878

NOR

2016

5257.005

NOR

2016

918.247

POL

2016

1246.285

POL

2016

537.977

PRT

2016

1846.427

PRT

2016

936.261

SVK

2016

1752.635

SVK

2016

417.707

ESP

2016

2320.021

ESP

2016

936.699

SWE

2016

4465.547

SWE

2016

882.065

CHE

2016

4911.814

CHE

2016

2912.151

TUR

2016

856.955

TUR

2016

235.512

GBR

2016

3311.805

GBR

2016

852.443

USA

2016

8047.295

USA

2016

1785.022

CHL

2016

1152.404

CHL

2016

740.187

CRI

2016

930.209

CRI

2016

306.589

EST

2016

1504.012

EST

2016

483.902

ISR

2016

1703.278

ISR

2016

980.267

LVA

2016

872.665

LVA

2016

724.669

LTU

2016

1332.797

LTU

2016

659.31

RUS

2016

743.045

RUS

2016

561.686

SVN

2016

2014.024

SVN

2016

757.059

AUS

2017

4543.085

AUT

2017

5439.979

BEL

2017

4774.332

CAN

2017

4826.316

CZE

2017

2616.372

DNK

2017

5182.837

FIN

2017

4172.592

FRA

2017

4902.139

DEU

2017

5728.451

GRC

2017

2324.839

HUN

2017

2044.647

ISL

2017

4580.71

IRL

2017

5449.367

ITA

2017

3541.73

JPN

2017

4717.276

KOR

2017

2897.052

LUX

2017

6474.79

MEX

2017

1034.378

NLD

2017

5385.699

NZL

2017

3682.741

NOR

2017

6351.326

POL

2017

1955.069

PRT

2017

2888.155

SVK

2017

2268.915

ESP

2017

3370.855

SWE

2017

5510.715

CHE

2017

8009.167

TUR

2017

1193.856

GBR

2017

4245.542

USA

2017

10209.41

CHL

2017

1914.797

EST

2017

2125.328

ISR

2017

2833.597

SVN

2017

2775.482

LVA

2017

1722.368

LTU

2017

2005.184

AUS

2017

3109.495

AUS

2017

1433.589

AUT

2017

4043.528

AUT

2017

1396.451

BEL

2017

3760.69

BEL

2017

1013.642

CAN

2017

3381.998

CAN

2017

1444.318

CZE

2017

2149.85

CZE

2017

466.522

DNK

2017

4363.352

DNK

2017

819.484

FIN

2017

3077.837

FIN

2017

1094.755

FRA

2017

4068.423

FRA

2017

833.716

DEU

2017

4869.427

DEU

2017

859.024

GRC

2017

1423.381

GRC

2017

901.458

HUN

2017

1365.076

HUN

2017

679.571

ISL

2017

3758.019

ISL

2017

822.691

IRL

2017

3954.522

IRL

2017

1494.846

ITA

2017

2621.956

ITA

2017

919.774

JPN

2017

3970.575

JPN

2017

746.701

KOR

2017

1686.738

KOR

2017

1210.313

LUX

2017

5286.434

LUX

2017

1188.356

MEX

2017

533.345

MEX

2017

501.033

NLD

2017

4377.677

NLD

2017

1008.021

NZL

2017

2894.164

NZL

2017

788.577

NOR

2017

5398.962

NOR

2017

952.365

POL

2017

1352.232

POL

2017

602.837

PRT

2017

1924.844

PRT

2017

963.311

SVK

2017

1826.88

SVK

2017

442.035

ESP

2017

2385.688

ESP

2017

985.168

SWE

2017

4606.429

SWE

2017

904.286

CHE

2017

5030.365

CHE

2017

2978.802

TUR

2017

934.229

TUR

2017

259.627

GBR

2017

3341.448

GBR

2017

904.094

CHL

2017

1164.379

CHL

2017

750.418

EST

2017

1616.234

EST

2017

509.076

ISR

2017

1780.425

ISR

2017

1053.173

LVA

2017

940.903

LVA

2017

781.465

LTU

2017

1341.422

LTU

2017

663.577

SVN

2017

2023.569

SVN

2017

751.913

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SAP analytical tool is a predictive tool. We are using this tool as this is simple and easy to learn.

The first step is need to load the data in SAP software. There are numerous option in SAP Analytical tool. We are opting the expert analytical option and load the excel data.

Data Analytics Assignment3.jpg

We are analysis the world bank data using SAP tool and analysis the world health organization global health expenditure and analysis the current health expenditure. Our gather data require more analysis and statistical work thereafter we will get the result about the current expenditure in health sector . According to data analysis tool observation , 800 million people are forced to spend at least 10% of their income on healthcare, meaning some families must choose between health costs and other essentials such as food and education. We select Prepare option and data will be prepare for the analysis or predict about the data

SAP Analytical Tool Analysis

Our assignment report is based on SAP analytical tool analysis option. According to analysis the global health spending is low and middle income countries is increase up to 6%. The global health spending in high income countries is increasing up to 4%.

This data is showing that now low and middle countries are doing more investment as compare to high level countries. The global data showing now people are very much concern about health issues and spending the money into health sector. Our analysis shown that people are investing money in health from their pocket as compare to government. The private investment is increasing in health sector. This is also showing government is not so much aware about health sector . There is a need to invest more money compare to this current scenario. This scenario is showing that people are expending money on health not and they are not dependent on government investment. The low and middle income countries undergoing the transformation period is about the spending the money in the health.

According to our data analysis U.S. country spends more money health as compare to other wealthy countries

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Data Analytics Assignment4.jpg

In this report using SAP analytical tool we collected graph using visualisation option about the information of health sector expenditure.

Our analysis shown from last two decades global expenditure is stable in the term of public and private in health care sector. This is slow steady increase growth in health investment sector GDP. This graph is prepare by uses data from the World Health Organisation . Our analysis is showing total healthcare spending around 1.5 percent over last two decades. The World health organization data more than 800 million people are spending the minimum 10% of their income on healthcare. This is showing that people are spending money forcefully on health sector most choose between health cost and other

Data Analytics Assignment5.jpg

Our analysis about is health sector investment for last three decades is that is U.S country is that is spending the more money as compare to other countries. The health sector investment GDP ratio is increasing last 10 year, this is reflect by the data in % as health sector increasing from last year is 3.9 to 8.8%..

According to our analysis from last three decades ,U.S spent the 8.5% of GDP that is more from other countries. This data is showing U.S government is aware about the why the investment is require in health sector.

Now these report analysis shown low and middle income countries need to aware about the significance of public spending on health. The health budget must be increase. The government investment in health sector is require for achieving the goal of Sustainable Development.

The low and middle income countries require to invest more money or increase fund for health sector to achieve goal. The Government need to provide to poor people for health subsidy therefore the countries people will be healthy and they will help to countries development.

The government need to emphasis on health sector. The health budget shall include as basic requirement. Hence it will help to poor people .This will help to achieve the health target set by the countries.

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This chart is showing 2014 is a year where expenditure is low.

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The graph is describing about the information according to year wise summary.

The US is spending the 7.2 trillion US$ . This spending is 10% of global GDP health spending. Our analysis is based on tables. These tables are shown the each country's total expenditure on health per capita. We analysis using analytical tool our observation shown U.S health GDP spending ($10,224( is double as per person in comparable countries($5,280.

This observation is showing other wealthy countries not spending like U.S. We are showing the information according to country wise.

a. United States - $10,209

b. Switzerland $8,009

c. Germany $5,728

d. Sweden $5,511

e. Austria $5,440

f. Netherlands $5,386

g. France $4,902

h. Canada $4,826

i. Belgium $4,774

j. Japan $4,717

k. Australia $4,543

l. United Kingdom $4,246

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These figures are related to summary chart about total expenditure

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We select the data model and set the alogorithm for prediction using predictive tool

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In this analsysis we use the normalization algorithm in this predictive tool

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We are giving the Data Type defination

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Our analysis using SAP tool shows that U.S expenditure in health sector .We analysis the health budget of all country U.S. spends more on its health care sector

Our analysis shows The U.S. and Switzerland are in the top two spots, spending 17.15% and 12.25% of GDP respectively. Third place goes to France, with 11.45%, followed closely by Germany, with 11.27%.

As per our observation is from last five years largest health spending per capita is U.S.

In the last two decades total aggregate global expenditure on healthcare has been relatively stable, albeit with a slow steady increase. The following visualization uses data from the World Health Organisation (published in the World Development Indicators) to show this. Total healthcare spending as a percent of GDP has seen an overall increase of roughly 1.5 perceptual points over the last two decades, with a relatively constant share of resources coming from the public sector.

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Conclusion

Our report is regarding the health care investment in primary health care sector . Our analysis is shown that low and middle income countries investment is low as compare to high income countries .Since for any country growth require healthy country people so that they will contribute the country development. The lower and middle income country people investment the money in health care sector therefore the invest on food and education is low . According to analysis there is a need to government increase percentage in health care investment

Recommendation Letter

Hon. type Minister's name here

Minister of type department here

type address here

2019

Dear Minister insert Minister's name,

This letter is to request government to take immediate steps towards addressing health sector across Australia. Australia is a rich country, yet there are over 1,100,000 Australian families (10% of Australian families) in health investment stress, paying more than 30 percent of their income on recurrent health costs. Health sector rates are increasing while health of families are effected therefore they investment in health issue According to our analysis in health data sector government investment is low therefore people are investing the health sector from their pocket . The investment on food ,education is low . As analysis is saying Australia investment i

We would also like to bring attention to healthcare services and the need for appropriate funding.

Thank you for considering this request. I would also ask that you respond and indicate the steps that your government will take to address health care in Australia.

Sincerely,

Xxxxx name

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