Statistics: Data Analysis for decision makers

Recent years have seen a growth in the use of business analytics to use data-driven insights to help business decision-making. However, due to the lack of innovation and study in the area of the application of business analytics to enhance business decision making, this aspect has been postponed and derailed. Based on information processing theories and contingency theory, data analytics analysis is necessary, especially for data that relates to our company, a coffee shop, and how we may use it to support decision-making. This report is therefore crucial and necessary for examining data analytics and how to apply it in decision making.

There is need to examine how data analytics has been in the past and its significance in reviewing solutions that traditional business intelligence brings about. A statement of problems is also provided in this report so as to demonstrate features and capabilities that Coffee Shop analytics has, the business components in it and the benefits that are offered by business analytics alongside the cost involved. Coffee Shop results indicates that there can be intelligence connections, analysis, and securing of data thereby helping in improving decision making and customer service. This brings out the clear need and necessity of carrying out this project and how worthy it is in investing on it.

Introduction

Data analytics refers to a program used to organize vast amounts of information andchange it into practical understanding (Gemignani, Zach & Patrick 2014). This program works to sensitizing the user on points that are necessary when it comes to conducting a rational analysis of data sets that requires interpretation to the real life situation especially the current world. Data may be in chunk form and the owner may see them as if they are useful or burdensome but one will have to analyze and screen them well under some statistical formulas and knowledge. This will work to helping business to know where to prioritize and where to adjust in your businesses. These business analysis will also help in coming up with the formula on the company expenditure and investments for the sake of improving the future of the business. Analysis on other data such as the past company data and those of other companies remains vital as one gets to gauge the company as per those data. This will go a long way in avoidance of one into falling into business failures as other companies.

Statistics and analyses of the company will help the programs in employing techniques that are developed within disciplines notably artificial intelligence, statistics, information technology and mathematics. This capability of programs being used to interpret data especially in business organizations will help the company in understanding their data and work to transform these data into practical and useful information in decision making. The competitiveness in the market will demand that the business will be required to use raw data in formulation and analyzes before they are being converted to making decisions so as to bring effects on business success. Background

A coffee shop has recorded its recent business activities, sample data for the food preparation time is shown in table 1. The shop has been running its business of foods and beverages and in the recent days, the company has been going through the low moments and therefore we saw the need to address this issue so that the company can come back to its normal operations and scale higher. There is need to use the business statistics and analytics tools and knowledge to address this issue and hence come up with a better way of conducting this business.

Table 1: Food Preparation time (Mins

Staff ID

Time to prepare Latte (Mins)

Time to prepare Hot Chocolate (Mins)

Time to prepare Pannini (Mins)

AB

2,33

2,13

3,64

EF

1,89

1,58

3,98

AB

1,64

1,67

2,61

IJ

2,32

1,12

2,31

AB

1,69

1,93

2,18

IJ

2,14

1,68

2,14

EF

1,37

1,97

3,29

GH

1,67

1,74

1,73

IJ

2,07

1,17

2,94

GH

1,44

1,4

2,92

GH

2,18

1,06

3,62

CD

1,62

1,66

2,92

CD

1,12

1,85

1,73

AB

2

1,79

2,36

AB

1,57

2,02

3,16

CD

1,73

1,55

0,47

CD

2,28

1,74

1,12

AB

1,43

1,91

2,65

CD

2,18

1,85

1,13

AB

1,38

2,03

1,98

IJ

2,02

1,9

2,57

GH

2,31

1,83

2,72

IJ

1,83

1,73

2,22

AB

1,61

1,55

2,48

EF

1,63

1,71

2,63

CD

2,06

1,56

3,59

AB

2,15

1,55

3,96

EF

2,35

1,78

2,04

GH

1,82

2,01

1,18

GH

2,29

1,54

2,96

GH

1,64

1,64

1,26

GH

2,32

1,75

2,31

IJ

2,22

1,81

2,01

GH

1,7

1,59

2,35

IJ

1,69

1,35

3,88

GH

2,26

1,38

2,07

GH

1,37

1,05

0,8

GH

2,47

1,17

1,52

GH

1,49

1,5

0,39

EF

1,47

1,51

1,35

CD

2,01

1,88

2,4

AB

1,68

1,35

1,25

IJ

1,41

1,49

2,66

AB

2,15

1,22

2,71

IJ

2,22

1,58

2,11

AB

2,18

1,38

5,29

GH

1,75

1,9

3,53

IJ

1,38

1,2

0,26

EF

2,49

1,35

1,28

GH

1,89

1,38

2,12

CD

1,44

1,74

1,69

CD

2,13

0,75

1,66

IJ

1,49

1,48

3,85

AB

1,84

1,91

3,34

GH

1,51

1,39

1,22

IJ

2,05

1,67

3,77

CD

1,6

1,74

3,36

AB

1,4

1,66

3,62

GH

1,48

1,54

3,34

CD

2,17

1,55

2,34

EF

1,77

1,51

3,72

GH

1,9

2,01

3,72

EF

1,41

1,69

2,19

EF

1,46

2,04

2,19

IJ

1,74

1,83

3,45

IJ

2,11

1,29

2,87

GH

2,37

1,44

0,05

CD

1,75

1,79

1,49

CD

1,75

2

1,81

CD

2,36

1,64

2,72

IJ

2,02

1,33

4,89

GH

2,07

2,18

0,55

CD

2,29

1,16

-0,01

IJ

1,75

1,68

3,76

GH

1,88

1,57

2,8

IJ

1,41

1,22

1,55

AB

1,83

1,52

3,75

GH

1,93

1,62

1,9

GH

1,24

1,26

2,25

GH

2,16

1,86

3,63

IJ

2,26

1,33

1,5

AB

2,31

1,66

1,67

GH

1,75

1,64

0,04

IJ

2,24

1,41

1,59

CD

1,3

1,18

2

GH

2,5

1,58

1,84

EF

1,36

1,48

1,82

EF

2,18

1,93

2,92

IJ

2,18

1,72

1,94

IJ

2,17

1,88

3,3

IJ

1,39

1,37

1,4

GH

1,89

1,56

1,89

Table 2: Customer behaviour

 

 

 

Customer loyalty Card

Time customer spends in coffee shop (mins)

Beverage Purchased

Number sachets of sugar used purchased

Confectionary Purchased

Soup or Sandwich Purchased

abc123

25,05

none

0

cookie

none

gfd654

21,28

water

0

scone

soup

ghj645

18,43

none

0

muffin

sandwich

uyt876

24,57

tea

1

scone

none

tyu567

20,92

juice

0

scone

sandwich

ewr345

19,68

hot chocolate

0

cookie

sandwich

fgh567

20,37

none

0

muffin

soup

sdf765

10,59

hot chocolate

0

scone

none

lkj876

16,44

juice

0

none

sandwich

nht678

9,81

hot chocolate

0

muffin

sandwich

kuj123

23,95

tea

1

cookie

none

kil786

15,49

juice

0

cookie

soup

drf435

17,29

coffee

3

none

sandwich

tre456

24,21

none

0

muffin

none

rty567

12,87

water

0

none

none

uyt876

28,23

hot chocolate

1

cookie

soup

lkj876

27,15

none

0

scone

sandwich

nht678

19,95

juice

0

cookie

soup

kuj123

18,51

coffee

1

none

none

kil786

24,21

hot chocolate

2

scone

sandwich

Data analysis:

A summary of your data using descriptive statistics and data presentation techniques:

Time to prepare Latte (Mins)

Time to prepare Hot Chocolate (Mins)

Time to prepare Pannini (Mins)

1,877959184

1,60122449

2,356122449

0,035357261

0,02844015

0,109854219

1,89

1,605

2,28

2,18

1,74

2,92

0,350019025

0,281543116

1,087501281

0,122513318

0,079266526

1,182659036

-1,143232411

-0,216816226

-0,230897586

-0,105529

-0,296129986

0,039034754

1,38

1,43

5,3

1,12

0,75

-0,01

2,5

2,18

5,29

184,04

156,92

230,9

98

98

98

A discrete probability distribution is made up of discrete variables whereby, if a random variable is discrete, then it will have a discrete probability distribution (Gemignani, Zach & Patrick 2014).

Examples of discrete probability distributions that are commonly applied in statistics are: binomial distribution, geometric distribution, hypergeometric distribution, multinomial distribution, negative binomial distribution and Poisson distribution. In all this distribution, we will focus on binomial distributions and how it can be applied in real life. The case example of this binomial distribution is that if you change the way the businesses are conducted in Coffee Shop, the business will improve. Doing the same thing over and over in the business will not change the outcome (Gemignani, Zach & Patrick 2014) hence there is need to apply the binomial distribution concept so that Coffee Shop will improve as far as business is concerned.

The graph below shows a Discrete Probability Distribution. The knowledge acquired in handling this task can be applied in solving business problems in that one can get to see how components are distributed for a given discourse and how they can be made to manipulate the future of that business so as to escape things such as business failures.

A confidence interval is a range of values that is likely to contain an unknown population parameter (Oestreich & Thomas 2016). If a random sample is drawn many times, mean population will be contained in a certain particular percentage intervals of confidence and this percentage is termed as the confidence level. This confidence intervals is mostly used in bounding mean or standard deviation though it can also be obtained for regression coefficients, proportions, rates of occurrence, and for the differences between populations (Oestreich & Thomas 2016). The confidence level is used when representing theoretical abilities of any given analysis so as to produce accurate intervals especially in cases where one has capability of assessing a wide range of intervals and the population parameter value is known.

Confidence intervals has been seen as the perfect work sample when one is targeting to achieve good population parameter estimates because procedure used tends to normally produce intervals which contains the parameter. Confidence intervals contains ‘the most likely value’ and an error margin that works around that given point estimate. This margin of error will help in indicating amount of uncertainty surrounding the population sample estimate parameter.

Regression analysis is therefore used to describe the relationships that exists between a given set of dependent variable and independent variables. This analysis will produce regression equation with coefficients representing relationship between a particular independent variable and the dependent variable (Robert, Dallas & Carla 2014). This aspect makes the regression equation a suitable tool for using in making predictions. The knowledge obtained in this regression analysis task can be applied in our coffee shop in determining when and what products sells best at a given particular period of time and how much they are likely to last. With such knowledge in place, we can easily tell what to expect and when to expect them. This will go a long way in saving on the unnecessary expenditures while maximizing on the profits and sales. Therefore with regression analysis knowledge in place, there will be a strong development in the Coffee Shop both today and in the future.

Summary and Conclusion.

Business Analytics can be seen at the moment as vital aspects as far as companies’ growth and development is concerned. Large investments are being made in big data analytics to make better business decisions from past data (Robert, Dallas & Carla 2014). This past data is normally generated from varied sources notably- business people, marketing, engineering, social media and on-line transactions to mention a few. At the moment, as the economy becomes more and more challenging while the business landscapes keeps changing faster than before, the organizations will now have to focus on critical business issues that are facing their firms. Furthermore, understanding the nature of those particular challenges is quite helpful and meaningful. As the competition increases and there is plenty of options available within the shared market, the consumers who are the customers will always be working on look-out with the aim of getting the next-best-thing.

Business Analytics is the field that comes in to play a very crucial role here since it applies statistics and tools that can be used to address and grasp consumer insights so they get lured into your products (Robert, Dallas & Carla 2014). The task is solved by using combination of data and business intelligence that will get into key insights and help in predicting future behavior and consequently help in running businesses better. Business Analytics’ technology in their latest developments are upgrading and taking the games to the next level by their quest in automating the business analysis process. This will work to enable experts of data analysis and the business users in interpretation of data easily and quicker.

Reference.

Albright, S. Christian., and Wayne L. Winston. Business Analytics: Data Analysis andDecision Making. Stamford, CT, USA: Cengage Learning, 2015.

Ghasemaghaei, Maryam, Khaled Hassanein, and Ofir Turel, 2015: Impacts of Big DataAnalytics on Organizations: A Resource Fit Perspective.Demirkan, Haluk, and Dursun Delen, 2009: Leveraging the Capabilities ofService-oriented Decision Support Systems: Putting Analytics and Big Data inCloud. Decision Support Systems

Wixom, Barbara H., and Hugh J. Watson, 2007: The Current State of Business Intelligence.

Schoenherr, Tobias, and Cheri Speier-Pero, 2015: Data Science, Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential.

Robert Eugene, Dallas Snider and Carla Thompson. IBMWatson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.Chen, Ying, Jd Elenee Argentinis, and Griff Weber. IBM Watson: How CognitiveComputing Can Be Applied to Big Data Challenges in Life Sciences Research. ClinicalTherapeutics38, no. 4 (2016): 688-701. doi:10.1016/j.clinthera.2015.12.001.

Oestreich, Thomas W, 2016: Magic Quadrant for Business Intelligence and AnalyticsPlatforms.Gemignani, Zach, Chris Gemignani, Richard Tolentino, and Patrick Schuermann, 2014: Data Fluency: Empowering Your Organization with Effective Data Communication.