The main part of an HR or workforce analytics projects is when all analyses have been done and you need to put 1 and 1 together to find the actual insights, the causes of your issues, the solution to your problem. Statistics help you well but can only take you so far. This is where the inter-relations plot can help out. You don't need to be a statistician to work with it and it will help you a lot to understand how events are impacting each other and to determine root causes.
Diving into the space of HR automation and understanding the role of Analytics and Bots in prioritizing and streamlining HR functions with efficiency to the uplift and upkeep the Business Profitability as a whole.
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
Talent Analytics: A Systems PerspectiveSharad Verma
Describes components of Talent Analytics from a systems perspective: People, process, technology, tools, leadership, context.
Highlights difference between goals and systems.
Describes how analytics can be used to build an innovation engine.
Provides real life examples from predictive retention analysis in a Financial Technology firm.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
Using Business Intelligence: The Strategic Use of Analytics in GovernmentIBM Government
IBM Center for the Business of Government addresses the value of analytics for measurably improving each of four government sector: health care, logistics, revenue management, and intelligence. Using the business strategy of leveraging analytics to promote promote change, these sectors can run as efficiently as any successful business.
The main part of an HR or workforce analytics projects is when all analyses have been done and you need to put 1 and 1 together to find the actual insights, the causes of your issues, the solution to your problem. Statistics help you well but can only take you so far. This is where the inter-relations plot can help out. You don't need to be a statistician to work with it and it will help you a lot to understand how events are impacting each other and to determine root causes.
Diving into the space of HR automation and understanding the role of Analytics and Bots in prioritizing and streamlining HR functions with efficiency to the uplift and upkeep the Business Profitability as a whole.
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
Talent Analytics: A Systems PerspectiveSharad Verma
Describes components of Talent Analytics from a systems perspective: People, process, technology, tools, leadership, context.
Highlights difference between goals and systems.
Describes how analytics can be used to build an innovation engine.
Provides real life examples from predictive retention analysis in a Financial Technology firm.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
Using Business Intelligence: The Strategic Use of Analytics in GovernmentIBM Government
IBM Center for the Business of Government addresses the value of analytics for measurably improving each of four government sector: health care, logistics, revenue management, and intelligence. Using the business strategy of leveraging analytics to promote promote change, these sectors can run as efficiently as any successful business.
Asking When, Not If in Predictive ModelingAndrea Kropp
Teaching Talent Analytics executives how to use survival analysis to predict WHEN an employee will attrit from the organization. Most predictive modeling for employee attrition focuses on IF a person will leave and completely ignores the time frame.
Human Capital Growth Webinar: A new approach to people analytics leading with...Human Capital Growth
This webinar presents an alternative analytics strategy that is business focused and helps organizations lead with intelligence.
http://www.humancapitalgrowth.com/new-approaches-to-people-analytics-helping-hr-lead-with-intelligence.html
Business analytics is not just about business development, its scope has been extended to human resource management. HR analytics is one of the great application of Business Analytics. In these slides you would find how it is important for various organizations.
Demand Forecasting, undeniably, is the single most
important component of any organizations Supply Chain. It
determines the estimated demand for the future and sets the level
of preparedness that is required on the supply side to match the
demand. It goes without saying that if an organization doesnt get
its forecasting accurate to a reasonable level, the whole supply
chain gets affected. Understandably, Over/Under forecasting has
deteriorating impact on any organizations Supply Chain and
thereby on P and L. Having ascertained the importance of De-
mand Forecasting, it is only fair to discuss about the forecasting
techniques which are used to predict the future values of demand.
The input that goes in and the modeling engine which it goes
through are equally important in generating the correct forecasts
and determining the Forecast Accuracy. Here, we present a very
unique model that not only pre-processes the input data, but
also ensembles the output of two parallel advanced forecasting
engines which uses state-of-the-art Machine Learning algorithms
and Time-Series algorithms to generate future forecasts. Our
technique uses data-driven statistical techniques to clean the data
of any potential errors or outliers and impute missing values if
any. Once the forecast is generated, it is post processed with
Seasonality and Trend corrections, if required.Since the final
forecast is the result of statistically pre-validated ensemble of
multiple models, the forecasts are stable and accuracy variation
is very minimal across periods and forecast horizons. Hence it
is better at estimating the future demand than the conventional
techniques.
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
People Analytics: Creating The Ultimate WorkforceCenterfor HCI
If you are a leader or manager in a large organization, you are probably familiar with these terms. But you may be unaware how your organization can benefit from people analytics and what it will take.
Asking When, Not If in Predictive ModelingAndrea Kropp
Teaching Talent Analytics executives how to use survival analysis to predict WHEN an employee will attrit from the organization. Most predictive modeling for employee attrition focuses on IF a person will leave and completely ignores the time frame.
Human Capital Growth Webinar: A new approach to people analytics leading with...Human Capital Growth
This webinar presents an alternative analytics strategy that is business focused and helps organizations lead with intelligence.
http://www.humancapitalgrowth.com/new-approaches-to-people-analytics-helping-hr-lead-with-intelligence.html
Business analytics is not just about business development, its scope has been extended to human resource management. HR analytics is one of the great application of Business Analytics. In these slides you would find how it is important for various organizations.
Demand Forecasting, undeniably, is the single most
important component of any organizations Supply Chain. It
determines the estimated demand for the future and sets the level
of preparedness that is required on the supply side to match the
demand. It goes without saying that if an organization doesnt get
its forecasting accurate to a reasonable level, the whole supply
chain gets affected. Understandably, Over/Under forecasting has
deteriorating impact on any organizations Supply Chain and
thereby on P and L. Having ascertained the importance of De-
mand Forecasting, it is only fair to discuss about the forecasting
techniques which are used to predict the future values of demand.
The input that goes in and the modeling engine which it goes
through are equally important in generating the correct forecasts
and determining the Forecast Accuracy. Here, we present a very
unique model that not only pre-processes the input data, but
also ensembles the output of two parallel advanced forecasting
engines which uses state-of-the-art Machine Learning algorithms
and Time-Series algorithms to generate future forecasts. Our
technique uses data-driven statistical techniques to clean the data
of any potential errors or outliers and impute missing values if
any. Once the forecast is generated, it is post processed with
Seasonality and Trend corrections, if required.Since the final
forecast is the result of statistically pre-validated ensemble of
multiple models, the forecasts are stable and accuracy variation
is very minimal across periods and forecast horizons. Hence it
is better at estimating the future demand than the conventional
techniques.
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
People Analytics: Creating The Ultimate WorkforceCenterfor HCI
If you are a leader or manager in a large organization, you are probably familiar with these terms. But you may be unaware how your organization can benefit from people analytics and what it will take.
Predictive Analytics in HR 4 Use Cases, Benefits & Tips.pdfRosalie Lauren
An array of methods are employed in predictive analytics in HR to examine data and forecast future outcomes. It reduces the impact of biases and subjective evaluations on critical HR processes like hiring and performance reviews. You've come to the correct spot if you're seeking for strategies to keep up in the highly competitive world of human resources.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
Global competition for talent, outsourcing labor, compliance legislation, remote workers, aging populations – these are just a few of the daunting challenges faced by HR organizations today.
Yet the most commonly monitored workforce metrics do very little to deliver true insight into these topics. Leaders need to graduate from metrics to analytics, surfacing the important connections and patterns in their data to make better workforce decisions. By graduating from metrics to analytics, HR professionals and leaders can better understand the contributing factors that are impacting their organization, and take the right actions to implement programs that will provide a true competitive advantage.
Human resource planning in changing context733swati
The HR Planning Process
1.Situation analysis or Enviromental Scanning
2.Forecasting Demand for human Resources
3.Analysis of the supply of human resources.
4.Development of plans for action.
Analytics is the discovery, interpretation & communication of meaningful patterns in data.
HR analytics is a methodology for creating insights on how investments in human capital assets contribute to the success of four principal outcomes.
HR analytics focuses primarily on the HR function and is not – as is largely believed.
The terms HR Analytics, People Analytics, Workforce Analytics are often used interchangeably.
#HR #HRAnalytics #TypesofHR #Analytics #Sourcing #Workforce #PeopleAnalytics #SocialNetworkAnalytics #TimetoHire
1. How well do you know
your workforce?
Introduction Analyticmaturityisajourney
Talent Management can be defined as “A The use of analytic techniques can vary greatly
conscious, deliberate approach undertaken to across organizations and it usually evolves over
attract, develop and retain people with the time, as the trends become more established and
aptitude and abilities to meet current and future more widely embedded into best practice. The
organizational needs.” But effective talent journey into analytics can start with simple ad-hoc
management goes beyond merely attracting, reports to investigate specific issues that can
developing and retaining talent; it extends to influence business performance, for example the
optimizing the performance of existing and relative performance of Sales Managers in a given
potential workforce, whilst maintaining good quarter, or the staff turnover figures by
standardsofemployeesatisfaction. department.
In order to effectively manage something, it has to As organizations develop more mature
be properly understood. This is because approaches to analytics, it is common to see the
understanding is an important prerequisite of emergence of regular reports, typically used by
management. Analytics is one technique in which the Management team to make business
something complex can be comprehensively decisions. Periodic reports inevitably require
understood with clarity. It is a technique from some up-front decisions as to which data to
which important insights can be obtained for include. Therefore it is common to see reports on
continuousimprovement. headcount, staff turnover, remuneration,
performance, and other basic parameters. Whilst
these reports can provide useful insights for
business decisions, their pre-defined nature
means that there may be more risks and
opportunities that the organization will not easily
capture,atleastnotuntilthesefactorsstarttohave
a measurable impact on the business
performance.
Director of human capital consulting (South East Asia) for Deloitte Consulting
How well do you know your workforce? - By Mario Ferraro
Analytics
n. The systematic computational analysis of data or
statistics.
(Oxford Dictionary)
By Mario Ferraro
2. The more advanced organizations use analytics
techniques to test hypotheses. For example, if
clear correlations were found between employee
training and productivity, it could be possible to
test whether an increased expenditure in training
couldresultintobetterprofitmargins.
With increased levels of sophistication and
adoption, analytic techniques can be used for Thisiterativeapproachissimilartotheoneusedby
predictive modeling. Having understood how doctors, who start from the patient's statement of
various aspects of the business can affect each the symptoms and then gather all information
other, analytics can be used to understand the and data to reach a diagnosis. Doctors often
likely impact of making certain decisions. Using recommend a comprehensive range of tests in
these techniques, organizations could in theory order to understand the root causes of the
make better investment decisions regarding, for problem. Based on the insights gathered through
example, training, headcount or salary the tests, the doctors will determine a diagnosis
increments, by simulating how such decisions and prescribe an appropriate course of action.
could influence the performance of the business The condition of the patient needs continuous
andoftheemployees. monitoring for a while, to determine whether the
prescribedtherapywaseffective.
Whilst it may be tempting to think of analytics as a
one-time exercise to address specific issues, the An organization can be thought of as a system
best organizations recognize that analytics is an with several parts. The overall performance of the
on-going process that should ideally be organization will depend on the collective
embedded in the organizational culture. The individual performance of its parts or
starting point is a clear articulation of the critical departments. Analytics will show how each part
workforce issues that the organization in trying to affectstheperformanceoftheentireorganization.
tackle. For example, in knowledge-based
industries, talent attraction and retention are It is important to understand how analytics
common concerns that can significantly affect techniques are different from HR metrics. HR
shareholder value. Once the issues have been metrics are numbers or ratios which express the
articulated, all available data can be leveraged to value of a specific parameter at a given time. In a
gain deep insights into all possible factors that sense, HR metrics are like the numbers on the
could directly or indirectly affect the issue. Today, dashboard of a car, which provide an indication of
fast processing times and inexpensive data whethereverythingisoperatingnormally.
storage make it possible to store and process large
volumes of data in a very short time. The analytics Analytics,ontheotherhand,providesinsightsinto
insights will reveal useful information regarding how a variety of dynamic variables interact with
possible risks and opportunities for performance each other to produce the measurable outcome.
improvement, based on which an action plan can In the HR context, analytics can describe how
be developed. However it is important to various factors work together to affect employee
continue the cycle, to monitor the effectiveness of behavior. Hence, analytics provide a detailed view
the action plan and to make the necessary ofwhatHRmetricsonlysummarize.
adjustments,ifrequired.
Forexample, theHRmetricsfor“staffturnover”will
Theanalyticscycle
Metricsvs.Analytics
How well do you know your workforce? - By Mario Ferraro
“Analytics is an on-going process
that should ideally be
embedded in the organizational
culture.”
3. Inductive and Deductive
Applications of Analytics inTechniques
TalentManagement
There are several benefits of this kind of periodic
inductive research. For example, analytics on one
companyshowedthatitwascommutingdistance
that represented the most influencing factor on
employee satisfaction. Knowing the most
influencing factors will play a big role in decision
making. The more knowledge the organization
hasthebettertheirdecisionsare.
There are a number of approaches to performing
analytics: the most commonly used are the
There are several applications of analytics in the
deductive and inductive approaches. The
field of HR. One application is safety, looking at
deductive approach starts with making some
issues such as workplace accidents and factors
assumptions, or hypotheses, regarding
influencing them like, for example, too many
relationship between two variables. An example
night shifts.There is also research done in the field
of a hypothesis would be that increased training
of diversity and how diversity affects team
leads to better employee retention. The next step
performance. It is also important to analyze
involves gathering data related to this hypothesis
network and influence groups, how the people
and finally proving or disproving the hypothesis
around an employee affect their behavior and
basedontheanalysisofthedata.
performance. Understanding retention and
absenteeism are some of the other applicationsThe inductive approach on the other hand does
analyticsisusefulfor.not start with any specific assumptions, but rather
by just collecting data and analyzing it for
Predictive analytics can also be used to predict
correlations.This kind of approach is routinely use
and manage retention. For example, by
in specialized consulting firms like the Deloitte
performing analysis on internal and external data
Analytics Institute, which is based in Singapore
it is possible to predict which employees are at the
and delivers professional services to organizations
highest risk of leaving the organization and why.
around the world. Deloitte has performed
Based on these insights, the organization can
comprehensive analysis on talent management
make decisions to mitigate the risks, for example
by utilizing various sources of data including
sending letters of recognition, phone calls from
financial databases, telephone logs, employee
superiors,orsalaryincrements.
information as well as even lists of websites
employees visit. These internal data-sets can also In addition, predictive analysis can be used in the
be correlated with external factors, such as area of Workforce Planning to project the supply
demographic data, unemployment rates, GDP, and demand of talent for an organization.
inflationsandmanyothers. Scenarios and models are helpful here too to
anticipate challenges and make all the
appropriatedecisions.
Analytics can also help to understand the day to
day activities of the HR function, such as how they
operate, their decision making habits and service
levels. The HR function can be properly managed
andimprovedbyusinganalyticstounderstandit.
How well do you know your workforce? - By Mario Ferraro
“Knowing the most influencing
factors will play a big role in
decision making. The more
knowledge the organization has
the better their decisions are.”
Inductive
Approach
Start with a
hypothesis and
prove/disprove
Start with lots of data
and derive a theory.
Deductive
Approach
4. Conclusion
Contrary to popular belief, organizations don't HRM Enterprise is a comprehensive HRIS which
need to wait until they have complex systems and contains all modules required to systematically
a full set of data in order to start doing some manage HR functions. It’s known for its ability to
analytics; they can start small by performing centrally manage payrolls in multiple currencies
simple analysis and then later on expand their acrossmultiplelocationsandbusinessunits.Italso
analysis based on the data they continue to includes all other necessary talent management
gather. tools and frameworks both operational and
strategic which help streamline the day-to-day
As the organization recognizes the power and the function of the HR department and assist with HR
valueofanalytics,itwillonlybenaturaltocontinue planning. The modules have a mix of both tried
on the journey by gathering more data and andtestedaswellascontemporarytools.
extracting as much value as possible from it.
Analytics plays an important role in not only HRM Enterprise is module-based and designed
effective talent management but in furthering the with flexibility in mind, so you could pick and
performanceoftheentirecompanythroughit. choose modules that are relevant to your business
needs and industry. You do not have to pay for
features that you do not need. It is also fully web-
based and so can be accessed from any location
via the internet. The highly configurable nature of
the HRM Enterprise allows for almost any changes
to the system’s processes without the need for
complex and costly software code changes.
(Configurabilityratherthancustomisability)
This award winning system has evolved for 2
decades of development and now has over 650
customers in 30 countries and 18 diverse
industries. HRM enterprise is designed to be used
globally, but has also been tailored successfully for
the African region and it’s soon becoming the
preferredHRsysteminAfrica.
Logon to hSenidBiz.com or contact one of the
hotlinesbelowforafreeonlinedemo.
AboutHRMenterpriseXV
How well do you know your workforce? - By Mario Ferraro
Mario Ferraro is the Director of human capital
consulting (South East Asia) for Deloitte Consulting,
an international HR consultancy agency. Mario has
over 15 years of experience in the field of Human
Resources and has worked in international
consulting companie like PricewaterhouseCoopers.
Healsoholdsanawardforhisdistinguishedservices
for the Worldwide Employee Relocation Council
(WERC) of which he was a member of the Board of
Directors.
The whitepaper is an extract from a Keynote speech
About
the author
Contact Us
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