Human Resources Optimised with Advanced Analytics

Human Resources Optimised with Advanced Analytics

Today we are going to present some insights related to employee’s working the satisfaction using Advanced Analytics tools and techniques. As a source for this study, we make use of the data made available on this link by the data scientist Ludovic Benistant who made important anonymizations. Some pictures have Brazilian Portuguese words, sorry about that! Let’s go!

Research Questions

Following the DCIM (Data Culture Introduction Methodology) methodology to guide this research, we came up the following questions:

  • What factors have the greatest influence on employee satisfaction?
  • What are the main satisfaction scenarios that exist?
  • What are the main patterns associated with key satisfaction scenarios?
  • What factors influence professionals to leave?

Data Characteristics

In total, 14,999 employees were evaluated, considering the following variables already sanitized by our scripts:

  • Employee satisfaction level (0 to 10) – Probably filled out by the employee;
  • Last evaluation (0 to 10) – Probably filled in by a manager;
  • Number of projects (2 to 7) – Number of projects in which the employee acted;
  • Average monthly hours (96 to 310);
  • Time spent at the company (2 to 10) – How long the person already worked in the company;
  • Whether they have had an accident at work – (Yes = 1 / No = 0);
  • Whether they have had a promotion in the last 5 years (Yes = 1 / No = 0);
  • Salary Range (Low = 1, Medium = 2, High = 3); Note: Actual values were not made available.
  • Left the company (Yes = 1 / No = 0).

Number of people per department


per-departament

Frequency Analysis / Distribution of Satisfaction

overal-satisfaction-level

The highest concentration of satisfaction is within the range of 7 to 9, and there are few people with satisfaction scores between 1.5 and 3.0.

Results

Ranking of Influence Factors in Work Satisfaction

By processing this dataset on VORTX Big Data algorithm

  1. Average monthly hours (50)
  2. Time spent at the company (21)
  3. Number of projects (20)
  4. Salary Range (13)
  5. Left the company (10)
  6. Whether they have had a promotion in the last 5 years (9)
  7. Whether they have had accident at work (9)

The factor “Last evaluation” had no relevant influence and it was automatically discarded by VORTX.

Satisfaction Scenarios

In the table below we have the result of the processing with the separation of employees into groups done automatically by the platform. In all, 120 groups have been found, and here we will focus on only the 20 most relevant and leave the others out as isolated cases and not the focus of the analysis.

english-table

Model Visual Validation

Typically managers, as far as we have experienced,  are not sure regarding machine’s ability automate the discovery of insights. Therefore, as proof of the model, we chose to show the raw data visually to demonstrate the insights aforementioned.

grupo-9-o-mais-insatisfeitos

The pattern of hours worked by the 588 people in scenario 9 (very dissatisfied). X Axis = Monthly working hours.

 

grupo-1

The pattern of hours worked in the largest scenario (1), which has 4085 employees, a good job satisfaction and a low level of job evasion. X Axis – Monthly working hours

In the view below, each circle represents a contributor in four dimensions:

  • The level of satisfaction on the Y axis.
  • Average hours per month on the X axis.
  • Orange colors for people who left the company and blue for those who remain.
  • Circle size represents the number of years in the company.

general-pattern

Alright, we just saw the overall pattern including the whole organization, so what would happen if we see it by the department?

accounting-and-it

managment-to-product

rd-and-support

technical

Conclusions and Recommendations

This study shed some light on the improvement of human resource management, which is at the heart of today’s businesses. Applying data analytics algorithms in this area allows automating and accelerating the process of pattern discovery in complex environments with, let’s say 50 variables or more. Here it was just a few. Meanwhile, the search for patterns in a traditional BI continues to be a purely artisanal work with a well know imitation of 4 dimensions per attempt (read more on this at Understanding the differences between BI, Big Data and Data Mining). The automation of discovery is an extremely important step in predictive analytics, in this case, the evasion of highly qualified professionals and possible dissatisfactions overlooked by management.

With VORTX’s ability to discover the different scenarios, we were able to analyze the data and conclude that:

  • People in group 1 and 2 (55% of the company) have a reasonable work satisfaction with a weekly load of 50 hours on average, without receiving promotion or suffering an accident at work.
  • The pattern persists in all departments.
  • The most satisfied groups of the 20 largest were the 7 and 10 who worked more than 247 hours a month, took on several projects but as they did not receive promotion they left the company. These people should be retained since there seams to be highly qualified.
  • Group 16 proves that it is possible to earn a good salary and be dissatisfied. These 77 people should be interviewed to identify the root cause of such unsatisfaction.
  • The cut-off line for non-company employees is: minimum 170 and maximum 238 hours worked per month.People with more than 3.5 years of work harder and are more satisfied.
  • Monthly hours above 261 resulted in very low levels of satisfaction.
  • Monthly hours below 261 with a number of projects greater than 3 turns out in high job satisfaction.
  • Scenario 15 shows the importance of promotion over the last 5 years of work.
  • The ones with more than 5 projects decrease their satisfaction, the ideal number is between 3 and 5. Of course, in this case, to better understand the indicator is necessary to better understand what the number of projects represents to different departments.

For managers, collecting as many indicators as possible is always good especially without interruption in all areas. More variables to enrich your model would be:

  • The distance between employee’s home and work.
  • The average time that is taken from home to work.
  • The number of children.
  • The number of phone calls or emails sent and received.
  • Gender and age and the reason for leaving the job.

We hope this information is useful for you guys in some way. If you find it relevant, share it with your colleagues. If in doubt, contact us! A big hug and success in developing your own HR strategy!

Big Data Scenario Discovery, why is it super useful for decision making?

Big Data Scenario Discovery, why is it super useful for decision making?

Hi everyone, in today’s demonstration, we are going to show you how Big Data Scenario Discovery can help decision making in a profound way in various sectors. We use AQUARELA VORTX Big Data, which is a tool that is a groundbreaking technology in the machine learning field. The Dataset used for the experiment was presented in the previous post about Big Data country auto-segmentation (clustering). The differences here is that this one also includes the Gini Index (found later on) and removes the electrification rate in rural areas. Also, it seeks systemic influences towards a GOAL, in this case, we selected Human Development Index, previously the segmentation just grouped similar countries according to their general characteristics.

The key questions for the experiment:

  1. How many Human Development Index scenarios exist in total? And which countries belong to them?
  2. Amongst 65 indexes, which of them have most influence to define a High or Low Human Development Index?
  3. What is the DNA (set of characteristics) of a High and Low Human Development scenario?

Alright, hang on for a minute! Before you see the results, take a look at all variables analysed in the previous post. Then try to figure out by yourself using the most of your intuition, what would be the answer to these 3 questions. This is a very fun and very useful cognitive task to scenario validation. OK?

Results after pushing the Discoverer button:

HDI - Total

This is the overall distribution of 188 countries, where most of the countries present HDI between 0.65 and 0.75. And very few above 0.90.  In total, there are 15 different HDI scenarios, which the first 3 correspond to more than 94% of the total and that is what we are to focus on.

Scenario 1

The most common scenario and the average HDI

Scenario 2

Countries with the lowest HDI

Scenario 3

Countries with the highest HDI

Where are they located?

Screen Shot 2016-09-15 at 20.21.36

What factors influence HDI the most and the least?

Ranking

The list marks the top and bottom 10 factors. The factor Intimate or Nonintimate partner Violence ever experienced 2001-2011 – Was automatically removed from the ranking as it does not correlate with HDI.

What is the DNA of each main scenario?

Screen Shot 2016-09-15 at 19.56.15

All factors presented at once. Note that the scales on X axis changes dynamically hovering the mouse on VORTX data scope screen.

Screen Shot 2016-09-15 at 19.56.06 Screen Shot 2016-09-15 at 19.55.57

Drilling down into the DNA

Under-Five Mortality rates vs HDI

Screen Shot 2016-09-15 at 19.51.05

Screen Shot 2016-09-15 at 19.51.19

Screen Shot 2016-09-15 at 19.51.30

Filtering visualisation by the most relevant factor and HDI (HDI is the focus of the analytics so it has the darker colour. Here we see that countries with the highest HDI have lowest levels of under-five mortality rate.

Gender Inequality Rate vs HDI

Screen Shot 2016-09-15 at 19.55.12

Screen Shot 2016-09-15 at 19.55.31

Screen Shot 2016-09-15 at 19.55.41

Gross National Income GNI per capta vs HDI

Screen Shot 2016-09-15 at 19.53.38 Screen Shot 2016-09-15 at 19.53.25 Screen Shot 2016-09-15 at 19.53.15

Insights and Conclusions of the study

The possibilities generating new knowledge from this Big Data strategy are endless, but we focused on just a few questions and few print screens to demonstrate its value. During this research, we found interesting to see the machine autonomously confirming some previous intuitions, while breaking some preconceptions. It is important to mention that we are not measuring causation as if one factor leads to another and vice-versa, the results show systemic correlations only. Here there are some of them that called our attention:

  • Gender inequality playing a strong role and inverse correlation in Human Development Index while we are living a transition of the industrial age to information where knowledge if surpassing the physical differences between genders.
  • Research and development having a direct correlation to HDI.
  • The United States having its own scenario due to its unique systemic characteristics.
  • Gross National Income GNI per capita leading the ranking and the values around 40 thousand dollars.
  • Public expenditure ahead of Education related indexes.

Business applications

Applying the same questions we had at the beginning of the article, now let’s see how they would look like for different business scenarios:

Sales

  • How many scenarios exist for your sales? Which customer segment belong to each scenario?
  • Amongst several business factors, which of them have the most influence to define a High or Low revenue?
  • What is the DNA (characteristics) of a High and Low revenue scenario?

Industry

  • How many production/maintenance scenarios exist for your production line? Which processes belong to each scenario?
  • Amongst several production factors, which of them have the most influence to define a High or Low outcome or High or Low maintenance/costs?
  • What is the DNA (characteristics) of a High and Low production/maintenance scenario?

Healthcare

  • How many patient scenarios exist for a specific disease or medical condition? Which patients belong to each scenario?
  • Amongst several patient characteristics, which of them have the most influence to result in High or Low levels of a specific disease or medical condition?
  • What is the DNA (characteristics) of a High and Low medical condition scenarios?

All in all, we expect that this article can help easy landing on the newest territories of machine learning and in case you need more information on how this solution applies to your business scenario, please let us know. If you found this analytics interesting and worth spreading, do so. Super thanks on behalf of Aquarelas team!

VORTX Big Data

Aquarela developed VORTX Big Data to make predictive analytics a lot easier, more precise and more robust than current solutions on the market with significant impact on business problems such as: Churn reduction, business scenarios discovery, predictive maintenance, market segmentation and healthcare resource optimisation.

 

How VORTX Big Data organises the world?

How VORTX Big Data organises the world?

Hello everyone,

The objective of this post is to show you what happens when we give several numbers to a machine (VORTX Big Data) and it finds out by itself how the countries should be organized into different boxes. This technique is called clustering! The questions we will answer in this post are:

  • How are countries segmented based on the world’s indexes?
  • What are the characteristics of each group?
  • Which factors are the most influential for the separation?

Here we go!

Data First – What comes in?

I have gathered 65 indexes of 188 countries of the world, the sources are mainly from:

  • UNDESA 2015,
  • UNESCO Institute for Statistics 2015,
  • United Nations Statistics Division 2015,
  • World Bank 2015,
  • IMF 2015.

Selected variables for the analysis were:

  1. Human Development Index HDI-2014
  2. Gini coefficient 2005-2013
  3. Adolescent birth rate 15-19 per 100k 20102015
  4. Birth registration under age 5 2005-2013
  5. Carbon dioxide emissions Average annual growth
  6. Carbon dioxide emissions per capita 2011 Tones
  7. Change forest percentile 1900 to 2012
  8. Change mobile usage 2009 2014
  9. Consumer price index 2013
  10. Domestic credit provided by financial sector 2013
  11. Domestic food price level 2009 2014 index
  12. Domestic food price level 2009-2014 volatility index
  13. Electrification rate or population
  14. Expected years of schooling – Years
  15. Exports and imports percentage GPD 2013
  16. Female Suicide Rate 100k people
  17. Foreign direct investment net inflows percentage GDP 2013
  18. Forest area percentage of total land area 2012
  19. Fossil fuels percentage of total 2012
  20. Freshwater withdrawals 2005
  21. Gender Inequality Index 2014
  22. General government final consumption expenditure – Annual growth 2005 2013
  23. General government final consumption expenditure – Perce of GDP 2005-2013
  24. Gross domestic product GDP 2013
  25. Gross domestic product GDP per capita
  26. Gross fixed capital formation of GDP 2005-2013
  27. Gross national income GNI per capita – 2011  Dollars
  28. Homeless people due to natural disaster 2005 2014 per million people
  29. Homicide rate per 100k people 2008-2012
  30. Infant Mortality 2013 per thousands
  31. International inbound tourists thousands 2013
  32. International student mobility of total tertiary enrolment 2013
  33. Internet users percentage of population 2014
  34. Intimate or no intimate partner violence ever experienced 2001-2011
  35. Life expectancy at birth- years
  36. Male Suicide Rate 100k people
  37. Maternal mortality ratio deaths per 100 live births 2013
  38. Mean years of schooling – Years
  39. Mobile phone subscriptions per 100 people 2014
  40. Natural resource depletion
  41. Net migration rate per 1k people 2010-2015
  42. Physicians per 10k people
  43. Population affected by natural disasters average annual per million people 2005-2014
  44. Population living on degraded land Percentage 2010
  45. Population with at least some secondary education percent 2005-2013
  46. Pre-primary 2008-2014
  47. Primary-2008-2014
  48. Primary school dropout rate 2008-2014
  49. Prison population per 100k people
  50. Private capital flows percentage GDP 2013
  51. Public expenditure on education Percentage GDP
  52. Public health expenditure percentage of GDP 2013
  53. Pupil-teacher ratio primary school pupils per teacher 2008-2014
  54. Refugees by country of origin
  55. Remittances inflows GDP 2013
  56. Renewable sources percentage of total 2012
  57. Research and development expenditure 2005-2012
  58. Secondary 2008-2014
  59. Share of seats in parliament percentage held by woman 2014
  60. Stock of immigrants percentage of population 2013
  61. Taxes on income profit and capital gain 205 2013
  62. Tertiary -2008-2014
  63. Total tax revenue of GDP 2005-2013
  64. Tuberculosis rate per thousands 2012
  65. Under-five Mortality 2013 per thousands

What comes out?

Let’s start looking at the map, where these groups are, then we go to the VORTX’s visualization for better understanding the DNA (composition of factors of each group).

Mundi

Click on the picture to play around with the map inside Google maps.

Ok, I see the clusters but know I want to know what is the combination of characteristics that unite or separate them. In the picture below is the VORTX visualization considering all groups and all factors.

Main groups

On the left side, there are the groups and their proportion. Segmentation sharpness is the measurement of the differences of groups based on all factors. On the right side is the total composition of variables or we can call the world’s DNA.

In the next figures, you will see how different it becomes when we select each group some groups.

Cluster 1

The most typical situation of a country representing 51,60.  We call them as average countries.

Cluster 2

The second most common type representing 26.46% of the globe.

Cluster 3

This is the cluster that has the so called first world countries with results are above average representing 14.89% of the globe. The United States does not belong to these group, but Canada, Australia, New Zeeland and Israel.

Cluster 4 - USA

The US is numerically so different from the rest of the world that VORTX decided to separate it alone in one group that had the highest distinctiveness = 38.93%.

United Arab Emirates

Other countries didn’t have similar countries to share the same group, this is the case of United Arab Emirates.

Before we finish, below I add the top 5 most and the 5 least influential factors that VORTX identified as the key to create the groups.

Top 5

  1. Maternal mortality ratio deaths per 100 live births 2013 – 91% influence
  2. Under-five Mortality 2013 thousand – 90%
  3. Human Development Index HDI-2014  – 90%
  4. Infant Mortality 2013 per thousands – 90%
  5. Life expectancy at birth- years – 90%

Bottom 5

  1. Renewable sources percentage of total 2012 – 70% influence
  2. Total tax revenue of GDP 2005-2013 – 72%
  3. Public health expenditure percentage of GDP 2013 73%
  4. General government final consumption expenditure – Percentual of GDP 2005-2013 73%
  5. General government final consumption expenditure – Annual growth 2005 2013 75%

Conclusions

According to VORTX if you plan to live in another country or sell your product abroad, it would be wise to see to which group this country belong to. If it belongs to the same group you live in, then you know what to expect.

Could other factors be added to removed from the analysis? Yes, absolutely. However, sometimes it is not that easy to get the information you need at the time you need it, Big Data analyses usually have several constraints and typically really on the type of questions are posed to the Data and to the algorithm that, in turn, relies on the creativity of the Data Scientist.

The clustering approach is becoming more and more common in the industry due to its strategic role in organizing and simplifying the decision-making chaos. So how could a manager look at 12.220 cells to define a regional strategy?

Any question or doubts? Or anything that calls your attention? Please leave a comment!

For those who wish to see the platform operating in practice, here is a video using data from Switzerland. Enjoy it!.

VORTX Big Data

Aquarela developed VORTX Big Data to make predictive analytics a lot easier, more precise and more robust than current solutions on the market with significant impact on business problems such as: Churn reduction, business scenarios discovery, predictive maintenance, market segmentation and healthcare resource optimization.

How Titanic passengers are segmented by VORTX Big Data?

How Titanic passengers are segmented by VORTX Big Data?

To demonstrate how VORTX works, I selected a well-known dataset with information about the passengers who embarked on Titanic. Despite the tragic event, this dataset is fairly rich in details and has been widely used in Machine Learning communities since it allows the application of several Big Data techniques.

In this case, I am going to apply VORTX, which it is Big Data tool focused giving automatic segmentation plus other important decision-making indicators. This technique is called clustering. More information about this on this post (How can big data clustering strategy help business)In the conclusion section, I give some ideas on how it help businesses by means of this innovative approach.

Titanic Dataset summary

According to Encyclopedia Titanica “On 10 April 1912, the new liner sailed from Southampton, England with 2,208 passengers and crew, but four days later she collided with an iceberg and sank: 1496 people died and 712 survived”.  For this analysis the data we had access we had the following figures: 

  • 1309 people on board of which 500 survived (38%) and 809 (62%) died.
  • The average age of 29.88 years (estimated).
  • 466 women of which 127 died and 339 survived.
  • 843 man, of which 682 died and 161 survived.
  • Ticket cost on average £53.65 per woman while £76.60 for man.

For more details on the complete dataset – Google for Titanic Dataset.

Factors under analysis

Unfortunately, 267 passengers (20.39%) had to be excluded from the analysis due to missing age values. Furthermore, out of 15 factors presented in the original file, I select the numerical ones with stronger weights calculated by VORTX. Usually, we classify factors, variables or data attributes in the following 3 categories:

  • Protagonist – Factors with strong positive influence to generate a valuable pattern with clarity.
  • Antagonist – Factors with noise or unclear patterns and negative influence that play against the protagonist.
  • Supporting – Factors that do not play a significant role in changing the path of the analysis, but can enrich the results.

According to the influence power, the protagonists chosen for this analysis were:

  • Age of the passenger = 87.85%
  • How much each passenger paid to embark = 72.69%
  • Number of parents on the ship = 71.69%
  • Number of siblings or spouses on the ship = 72.42%

During the calculation the gender that indicates if the passenger was male or female tended to play an antagonist role, meaning the absence of a pattern to form the groups dropping the dataset sharpness to 7%.  Therefore, it was removed.

VORTX Results and group characteristics

After processing, VORTX resulted in the following indicators, which most of them are not offered by other algorithms, therefore, I give a brief explanation for each of them:

  • Dataset Sharpness = 33.64%. It shows how clear or confident the machine is about the discovered grouping patterns. According to our dataset quality scale, sharpness above 20% is already useful for decision making.
  • Automatic discovery of segments (groups) = 8. This is a function that makes the whole process a lot easier for the data analyst. Unlike k-means and other algorithms, VORTX finds the right (ideal) number of groups by itself reducing dramatically the segmentation errors that topically happened.
  • Clustering Distinctness = How much different the elements of each group are in relation to the overall group that makes them a group. The most distinctive one is number 5 with 51.48% (darker color) and the least one group 1 with 8.58%. This means that elements from group 5 tend to more homogeneous than the other groups.
VORTX VIEW

VORTX screenshot

By analyzing the groups and checking against the ones who survived or not the trip I came to the survival rate of each group plus the average Ticket Fare, so if you have the characteristics of the group 5 or 7 you would have better chances of surviving.  (more…)

How can Big Data clustering strategy help business?

How can Big Data clustering strategy help business?

Hello folks,

To clarify the concept of clustering, which is a reoccurring theme in machine learning area (machine learning), we made a video tutorial that demonstrates a clustering problem that we can solve visually, then with finalize with a real case and some conclusions.  It is important to mention that other areas may benefit from this technique by targeting markets where you can meet different audiences according to their characteristics. We will use a video example

Below is the description of the video for those who like reading.

To facilitate the absorption of the concept, we will use a visual-based example. So, imagine that you have a textile factory and you want to produce as many flags as possible in the shortest time as with fewer materials as possible. Considering that there are around 200 national flags and each has different colors and shapes, we are interested to know which color patterns and shapes exist to optimize and organize the production line. That’s the idea, reduce costs and time while maintaining quality and volume.

All flags

Figure 1 – Representation of raw data without patterns detected

A good clustering algorithm should be able to identify patterns out of the raw data like we humans can visually identify looking at the Italian, Irish and Mexican flags like in the example below.  One factor that differentiates clustering algorithms from the classifying algorithms is that they have no hints about the patterns to study the model they must figure out automatically and this is a big challenge for practitioners.

bandeiras1

Figure 2: Cluster zero (0) composed of the Italian, Irish and the Mexican flags.

In this context, as important as to identify groups with similarities between each other and finding individuals who do not resemble any other element. The so-called outliers, which are the exceptions.

bandeiras2

Figure 3: Cluster six (6) composed of the flag of Nepal. An exception.

Finally, as the result of a good clustering process, we have the groups formed by the flags that have similar features and isolated individuals being the outliers.

bandeiras3

Figure 3: Clusters formed at the end of visual human-based processing.

One of the most important factors of clustering is the number of groups where the elements will be allocated. In many cases, we have observed very different results while applying the same data, and same parameterization in different algorithms. This is very important. See below what could be the result of an inaccurate clustering.

bandeiras4

Figure 4: Clusters result of a wrong clusterization

So, a practical question is:

Would you invest your money in this?

Probably not, and solving this problem is our challenge. A real application that we carried out was to identify the main characteristics of patients who don’t show up to their medical appointments, the well-known no-show problem that has deep implications in offices, clinics, and hospitals. The result was an amazing group with 50% of the analyzed data, which really deserves a specific policy. Doesn’t this give reason to the chief financial officers of these organizations?

Other possible applications of the clustering strategy were presented in this post “14 sectors for application of Big Data and data necessary for analysis.”

Some conclusions

  • Our vision is very powerful clustering images as in the case of flags.
  • It is humanly impossible to do analysis and logical correlations of numbers from a large database, so the clustering algorithms were created.
  • The accuracy of the results of clustering is crucial for making investment decisions.
  • Several sectors can benefit from this management approach.

Thank you!

VORTX Big Data

Aquarela developed VORTX Big Data to make predictive analytics a lot easier, more precise and more robust than current solutions on the market with significant impact on business problems such as: Churn reduction, business scenarios discovery, predictive maintenance, market segmentation and healthcare resource optimization.