28 HR Analytics Terms Every HR Professional Must Know Part-II

HR analytics

In our second post on a glossary of HR analytics, we continue with a few more terms.

#1. Demand and supply forecasting

As the name suggests, demand forecasting means estimating the number of people required with the right skill set to accomplish certain upcoming tasks. Supply forecasting on the other hand implies estimating the number of people who will be available to accomplish a certain future task.

#2. Cost to hire analytics

Needless to say that all external hires come at a cost. Sometimes this cost can be hidden. For example, the cost of sifting through hundreds of resumes to find the perfect candidate or it can be as simple as cost of an advertisement for job vacancy. This is referred to as cost to hire analysis. It makes use of notable hiring data to evaluate and identify the various cost heads; and then suggests means to reduce or contain certain large cost heads.

#3. Joining probability analysis

HR professionals often have to deal with situations such as a candidate accepts an offer but backs out from joining the organization. Joining probability analysis includes building a probability score for all candidates, as an indicator of how likely are they to join. It successfully identifies profiles of candidates who are more likely to join, based on an analysis of their character and traits. A tool like this helps avoid the loss incurred when a candidate backs out from joining after receiving an offer.

#4. Hiring channel mix modeling

HR professionals make use of several channels while hiring candidates. These include employee referrals, recruitment consultancies, social media and so on. Hiring Channel Mix Modeling analyses previous hiring and identifies all channels that have led to hiring for an organization and the interplay between them. It can also go a step further and pinpoint the channels that have been the most fruitful in hiring.

 #5. Attrition analytics

In order to understand attrition analytics, first of all we need to understand what is flight risk. Flight risk refers to the risk associated when an employee is looking to switch jobs. The HR must devise methods to retain talent as long as possible. Attrition analytics helps reduce flight risk by adopting a predictive approach and identify problem areas in advance.

#6. Clustering

Clustering refers to a type of machine learning that makes predictions by crowding data. The following example shows 1,000 data points divided in three clusters. Take a look at the image below. Machine learning makes it possible to make estimations of the different clusters. Additionally, when a new point of data is introduced, the algorithm is able to predict in which cluster it is more likely to belong.


#7. Training data vs test data

When you have a data set, you can definitely choose to develop an algorithm. But how would you know that the predictions you made were accurate? In order to find out about that, you would need another different set of data which is known as test data set.

Both training data and test data are created by splitting one full data set. The first part of this set is usually reserved for training purpose. This can be used for creating the next set of predictive algorithm. The second set of data is the test data. This data will be used, once the algorithm is created, in order to test, how accurate the predictions of the algorithm. Take a look at the image below for a better understanding:

training data

#8. Overfitting

Overfitting refers to a modeling error which occurs when a function is too closely for into a limited set of data. However, in HR analytics, it is related to machine learning. Machine learning is a complex technique and it can provide very detailed analyses. Since a lot of detailing is involved, it is at a risk of ‘overfitting’. This implies that anyone can create an algorithm that has the ability to predict the data almost perfectly.

#9. Bagging

Bagging is another form of meta-algorithm that stands for bootstrap aggregation. Bagging refers to a particular technique in which multiple training sets are independently sampled, based upon the original data set. Bagging helps in reducing the effects of outliers in the algorithm, and thus the algorithm’s variance as well. This technique is mostly used for the decision tree model.

#10. C4.5

C4.5 is a decision tree algorithm as well as a well-known data mining algorithm. With every new branch, C4.5 uses the criterion of information gain versus default gain ratio per attribute and then selects the best feature to riven its branch on.

#11. Linear regression

Linear regression analysis refers to a statistical method, that estimates the relationship between a dependent variable and one or multiple independent variables. Regression analysis implements the least squares methods in order to estimate the best fitting curve on the data. This curve can be used to predict various outcomes.

#12. Data cleaning

Data cleaning is a well-versed topic within HR analytics. It is the process of going through the data, fixing inconsistencies and gathering missing data and preparing it for analysis. Since HR data is oftentimes regarded as ‘dirty’, this system of data cleaning is implemented in order to ensure that the data is perfectly alright.

#13. Business intelligence

Business intelligence refers to, making an effective use of data and information to well-informed and sound business decisions. It comprises various elements such as data analysis, data mining and reporting. Business intelligence in HR can help make the right decisions, and letting go decision that are backed by data.

#14. Workforce analytics

Workforce analytics is a great combination of software and methodology that makes use of statistical models to worker-related data, allowing enterprise leaders to optimize human resource management. Workforce analytics can help leaders to develop and improve recruiting methods; make general and specific hiring decisions and of course keep the best workers within the company.

Click here to read  – Part I

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28 Analytics Terms Every HR Professional Must Know -Part-I

HR Analytics

In present times, when we talk about hiring, recruitment and human resources, we often refer to the term ‘analytics’; to be more specific, we often refer to or come across the term HR analytics. And related to HR analytics are several other terms such as data mining, predictive analytics and so on. Nonetheless, how many of us know the exact meaning of these terms and how many words exist in the domain of HR analytics.

In the following post, we will take a look at the glossary of HR analytics

HR analytics
Img. Src. The Alexa Blog

#1. HR analytics

HR analytics refers to the application of essential data mining and business analytics techniques to talent data. It usually refers to analytics that measures performance and efficiency that matter to HR only.

#2. Predictive analytics

It is a section of advanced analytics used to make extrapolations about anonymous impending events and referred as predictive analytics. In this case, it is about recruiters predicting the likely job candidates for a vacant position.

Predictive analytics implements many techniques including statistics modelling, data mining, artificial intelligence and machine learning to scrutinise existing data and make predictions about the coming event. In recruitment, it allows organisations to become proactive; anticipating behaviours and outcomes based on actual data.

#3. Data mining

Data mining is almost like digging for gold. Just as gold diggers sift through piles of dust and sand in hope to strike a piece of shiny gold, data mining is the method of learning patterns in piles of raw data and turning them into concrete information; which later can be used to make predictions about staffing.

#4. Machine learning

Machine learning is a representation of Artificial Intelligence (AI) that allows computers with the ability to learn without being explicitly programmed. It is mostly achieved through various pattern recognition processes. With the help of machine learning, can start recognising the pure data points’ of candidate’s information, their work history and their profile.

#5. Descriptive analytics

Descriptive analytics mines historical performance data to look for the reasons behind the past success or failure.

#6. Cost modelling

Cost modelling helps the one in the C-Suite to understand and interpret several HR related expenses. These include recruitment and on-boarding costs, estimated time required for an employee to attain maximum productivity, compensation, employee turnover, and overall productivity costs. Cost modelling can also offer an insightful picture of retention and recruitment plans, even for a stipulated period.

#7. Decision tree

A decision tree is a model that looks like a tree. It comprises decisions and their possible consequences. It is a significant tool to make predictions. A decision tree allows you to predict what might happen by learning from existing data.

#8. R

Many HR practitioners often use excel. However, most predictive HR analysts use R. It is the most attractive tool for data scientists. R is a free open-source system for statistical visualisation and computation. It also enables you to work with massive data sets that would be too huge to handle in Excel.

#9. Structured data vs. unstructured data

There are a two types of data in the HR analytics domain — structured and unstructured. When data is neatly organised into a spreadsheet or database, it is called structured data.

On the other hand, where the data is not properly structured, it is referred to as unstructured data. Its lack of structure makes it time-consuming and tiring to use.

#10. Multivariate analysis

Multivariate analysis is essentially the statistical procedure of simultaneously analysing multiple independent (or predictor) variables with multiple dependent (outcome or criterion) variables using matrix algebra (most multivariate analyses has a correlation).

In human resources when you want to predict how age and engagement levels influence someone’s compensation and performance ratings, there are two dependent variables. This is what is known as multivariate analysis. Take a look at the image below:

multivariate analysis

 #11. Quantitative scissors

Quantitative scissors is a phrase widely used by data scientists to describe a moment when an employee begins to be profitable. Consider the example of 2 lines intersecting. One is a cost line, and another is a benefit line. When the benefit line is higher than the cost line, then the employee becomes an asset to the organisation, not an expense.

This term was first introduced by talent analytics chief scientist Pasha Roberts.

#12. Boosting

When one creates an algorithm, one wants to be as accurate and as predictable as possible. Boosting is an interactive statistical technique, used in the process developing an algorithm that creates multiple extra training data-sets. A model is created for each these data-sets. Since these data sets are created deliberately, it implies that the weight of the misclassified data points is increased. Therefore the next algorithm will fit these miscalculations much better. This process repeats itself several times. Together these models decide on the most reasonable consequence. They make this choice based on a weighted vote in which more accurate models have more voting power than less specific models.

#13. Random forest

Contrary the boosting technique; the random forest technique randomises the algorithm instead of the data. Usually, a decision tree algorithm selects the best attribute to divide its branches. However, in a random forest technique, this procedure of selecting the best attribute is randomised. It leads to the production of different trees. Hence, a forest and these random trees produce a much better result together.

#14. Pruning

The technique of pruning is associated with the concept of a decision tree. Pruning is used to reduce the complexity of a decision tree. A decision tree is built by taking the most critical attribute to split its branches, and this process continues till the tree is completed.

(to be continued in part II)

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HR’s Struggle for Implementation of Big Data & HR Metrics

big data implementation challenges

While some companies are in dilemma of implementing big data, others are leaping into big data with a vengeance. HR executives already have enough on their plate, now the arrival of Big Data and disruptive HR tools scares them off. To overcome the big data implementation challenges require customised approach as there is no one-size-fits-all solution.

Nonetheless, the derailment of effectuating a plan of Big Data implementation, curtails from the mere fact that those organisations do not have the knowledge or resource to develop and implement the big data strategy on their own.

How big data does fit in Human Resource?

In HR, big data refers to the use of several data sources available to an organisation, including modern tools – such as cloud-based services, advanced analytics platforms and visualisation tools. Big data in HR metrics used to assess and improve practices including, talent acquisition, retention, and overall organisation performance.

It empowers the HR managers to analyse the tonnes of unstructured and structured data to answer significant questions concerning predictors of workforce attrition, workforce productivity, succession planning and impact of training programs on organisation performance.

Big data implementation challenges

big data implementation challenges
Img. Src. Business News Daily

Challenge #1 – Hadoop is tough

In one of the survey, “73% of respondents agreed that understanding the big data platform was the most noteworthy challenge of a big data project”.

Since the technology is comparatively new, several data professionals are not acquainted with how to manage Hadoop (concerning HR metrics).

Solution – Increase the internal resources to maintain and train IT employees on advanced Hadoop courses.

Challenge #2 – Data Quality

Data warehouse is essential because data is coming from several different sources from all facets of the organisation. Keeping the every piece of data in its original form is affecting the data quality. Every year dirty data costs organisations $600 billion.

Some common reasons affecting the data quality includes incorrect data linking, duplicate data and input errors.

Solution – Being meticulous at cleaning and maintaining data and big data algorithms can be used to improve the data quality.

Challenge 3 # – Scalability

Most organisation fail to predict how quickly big data project can grow and evolve. However, it is crucial to be able to scale up and down on-demand with big data. Moreover, big data workloads tend to be a rupture, making it challenging to calculate where resources should be allocated.

Solution – As compared to on-premise solution implementation of HR analytics & big data plan on the cloud will scale much faster and easier.

Challenge #4 – Security

Another prominent big data implementation challenge is keeping that vast data sets secure. Particular hardships include:

  • Restricting access based on user’s requirement.
  • User authentication for every team member and team accessing the data.
  • Proper use of encryption on data at rest and in transit.
  • Meeting compliance regulations.

Solution – Do not overlook the primary security measures, warrant that encryption integrates with access control and ensure proper enforcement and training.

Recommended big data implementation approach

Step 1 – Secure Executive level sponsorship

Big Data projects take the time to scope. Therefore, it needs to be proposed and fleshed out. Without the dedicated project team and executive support, there are fair chances it will fail.

Step 2 – Amplify rather than re-build

At this phase, try to receive approval to assess a few options until organisation settle on most appropriate technology for their HR metrics requirements. Therefore, one must start with existing data warehouses because here the challenge is – “ascertain and prioritise additional data sources and then identify the precise hub-and-spoke technology”.

Step 3 – Make value to the patron priority

Once an enterprise has acknowledged and prioritised the data sources. Now start connecting them to the requirements of the customer base.

Step 4 – First run a swift structure and increase over time

Start working on the incremental releases and integrate new data centres one at a time after establishing project team and priorities. Such approach will let organisation understand how to use data effectively to influence actions throughout the enterprise.

Step 5 – Connect customer data

Push data-driven decision throughout the enterprise – from the development of the product to pricing, packaging and promotion. Remember, each new set data represents prospect to change the way company deliver services and products.

Step 6 – Develop repeatable process and action trails

Avoid “data paralysis” while taking a thoughtful methodology for integrating into data sets. Evaluate the responses from the learnings by asking team members what can be gained by adding data set. Do not just create another factoid devoid of a link to the product or the customer. Just clear the path for implementation with the organisation.

Step 7 – Trial, Quantify and Learn

Test the assumptions with each data set. If a company is using the big data appropriately, one can determine the most optimised solution to overcome big data implementation challenges right from recruitment to performance management to succession planning.

Step 8 – Map the data sets to the customer life cycle

At each stage of customer life cycle, begin mapping big data by asking following questions:

– How do customers discover new products or services?

– Can organisation connect that action to their advertising activities?

These 8 steps are primarily involved in the implementation of big data plan.

The mentioned above big data implementation challenges occur at all levels. Therefore, prior initiating the deployment of big data in HR metrics should be thoroughly thought out.

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The Art of Machine Learning in the Process of Recruitment

Recruitment Strategies

Hiring the best candidate for a vacant position is truly a challenging task for employers. Most companies invest a lot of time in an attempt to find the ideal candidate suitable for a vacant position. It can range from spending more money on advertising to contacting headhunters to find more candidates. And then the story continues. Now, with technology foraying in, the recruitment strategies has become more scientific with a comparatively less dependence on personal prediction.

While human touch continues to be a necessity in the overall process of recruitment, the advent of technology has changed the game considerably. Right from Big Data to Predictive Analytics to Workforce Analytics—all have ventured into the domain and has been highly instrumental in revamping the overall process of recruitment.

One of the latest weapons in the arsenal of recruitment strategies happens to be machine learning. With the volume of available information in the hiring process, machine learning can uncover much more efficient methods for identifying several strong candidates.

What is machine learning?

Machine learning is a representation of Artificial Intelligence (AI) that enables computers with the capability to learn without being explicitly programmed. It is primarily accomplished through various pattern recognition processes. For example, if you ask a computer to search for a candidate by identifying patterns in data that produce the best results. In this way, a computer will be able to find correlations and patterns that a human would overlook – eventually leading to a better quality of candidates. Possibilities are infinite beyond this. The capability to foretell upticks and downticks, in a given market is, of course, is a revolutionary new process.

Machine learning would give recruiters a serious advantage over their competitors.

How is machine learning influencing the recruitment strategies?

Recruitment Strategies
Img. Src. Datumbox

Needless to say, machine learning has been instrumental in changing the process of recruitment to a great extent. One of the biggest issues for recruiters right now is they have extensive networks, but they haven’t been able to leverage the power of the same considerably. It is where machine learning comes to the rescue.

With the help of machine learning, recruiters can start recognising the pure data points of candidates’ contact information, their work history and their profile. And be able to match those with several opportunities.  Machine learning does not select the best candidate automatically; instead, it narrows down the field of search and allows recruiters to focus on analysing the intangibles. From this, a stronger hire can be made, therefore leading to a greater ROI (Return on Investment) from each candidate. Going deeper, machine learning will be able to take a broader view of trends in specific industries and even specific job titles. For example, machine learning can determine that a particular developer has been in the organisation for nearly two years and now there are roughly 90% chances that she will be looking for a new job in the next few months.

Benefits of implementing machine learning

There are several hidden costs in the process of recruiting an employee: advertising, selection, recruitment, onboarding and so on. And all of these come with their set of costs. However, implementing machine learning can help in the process tremendously. While at the beginning it might be a little cumbersome, over time, the implementation of machine learning will end up reaping long-term rewards and benefits for the company.

As you begin to invest more in your recruitment process, develop, and retain individuals in your enterprise, it is of utmost concern that you integrate analytics (machine learning) into your decision making. In the long term, the company will benefit to a great extent.

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4 Digital HR Trends Generating Lots of Noise

Digital Hr Trends

Cloud Computing, Data Analytics, Social Media and Mobility are four inevitable digital HR trends, which have influenced the global market at a critical level. These revolutionary technologies are restructuring the organisations everywhere.

In today’s, hyper-competitive work world and global economy, an enterprise is only as successful and innovative as the talent it draws and energise. It accentuates the role of Human Resource personnel, who are progressively being summoned upon to work as strategic partners to their organisation.

– Data analytics assist workforce to operate efficiently, connect dots and speed decision making.

– Cloud computing and Mobility providing companies access to the right skill sets and empower employees to work virtually regardless of geography.

– Social Media fuels the collaboration with the suppliers and colleagues as well as customers.

These digital HR trends have a profound influence on how people work, how they want to grow and progress in their careers and what they expect from their employers.

Defining the digital HR

The digital HR is an online or automated administration and delivery of the range of workforce management data and services. Human Capital information is swiftly accessible to HR executives, as it is to employees themselves through self-service competences.

Digital HR is accomplished through the implementation of exceedingly automated Human Resource System and supporting transformational tools.

Leveraging the Digital HR trends

Digital Hr Trends
Img. Src. ngahr.com

The impact of technology is quantifiable and growing. According to the industry speculators, the implementation of HR tools is the necessity, not the luxury. The technological changes are occurring fast that put the businesses today on a hot seat to stay ahead of the curve.

Above all, it is influencing every aspect of Human Resource from training to compensation and benefits for talent acquisition to succession planning.

Companies across all regions and of all sizes and industries are focusing on the leadindg technologies including, cloud computing, mobile devices and applications, social media and business analytics. These digital HR trends are in extensive use within HR departments and the bigger corporations they serve.

Substantial investments in the following digital HR trends is expected over the next 3-4 years, which will bring some greater changes in the Human Resource working methodologies.

1. Analytics

Today, the HR practitioners explicitly understand the importance of data and metrics.  A good number of HR executives accept that they use analytics to anticipate Human capital requirements. However, when it comes to adding the strategic value to business, there is a lot to be done.

There is widespread recognition to employees’ skill analysis and employee satisfaction but the concrete skill gap assessments implementation is still missing in most of the organisations.

It is required for companies to get smarter about analytics. If your vision is to create the workplace of the future, then you need to stop undermine the power of people analytics.

2. Mobility

Mobile technologies have already made their way in Human Resource. Now, it is the time to exploit the transformational features of mobile devices in the correlation of supporting tools. The more cited benefit of “mobility” in HR is more efficient virtual work and increased performance.

The advent of mobile technologies is one of the most appreciated digital HR trends as it not only reduced the costs but offered better access to the data and improved workforce optimisation.

The adoption of mobile technologies among companies varied regarding their focus area. For instance, business services are more into implementing HR analytics while small enterprises or SMEs invested into recruitment via mobile devices.

3. Social Media

Long gone are times when social media just for the “stay in touch” purpose. Now, it has become one of the powerful weapons for Human Resource not just to attract the talent, but it also helps companies to build a robust brand.

Social is an interesting yet efficient tool for gathering information, for communicating employees and prospective candidates. It plays an essential role in strategic workforce planning.

Improved sourcing and recruitment of best talent are cited as the primary benefit of social media. Moreover, the shift to social media from the traditional recruitment approaches involves a generational shift, which helps – to attract, engage and retain talent at minimal cost.

The most cited inherent benefits of social media include improved communication with employees and the development of company-wide culture.

4. Cloud Computing

The cloud computing is not just another “IT” term, but now it is an integral part of Human Capital Management in every organisation. The most commonly used cloud data sets include –

  • Payroll processing
  • Recruitment
  • Benefits administration, and
  • Employee administration

The startling fact about this digital HR trend is developing economies far outperform those in the advanced economies regarding HR cloud adoption. The biggest benefit of cloud computing is the greater collaboration across geographic regions.

It is time to embrace the opportunities and overcome the challenges

Although the adoption of these digital HR trends is significant for the survival of business, the transition would not be easy. For most organisations, the existing infrastructure makes the transformation difficult and in some cases impractical.

Moreover, the technology concerns are not the only challenges to HR transformation. The hardest part of transition involves business process and culture as well. Other concerns include diversity, globalisation and the different needs of each generation in the workforce (baby boomers and millennials).

What is your next move?

The arrival of these revolutionary digital HR trends made the strategic thinking an essential part of Human Resource leadership. Human Capital Management across the organisation is necessary. To compete in the new digital era successfully, HR should

– Embrace the transition to strategic mind-set and driving business results

– Use technology efficiently to execute on business imperatives and extend collaboration

–  Contemplate the competitive peril of not leveraging the technology to contribute to business strategy.

Ultimately, digital HR makes it easier for prospects to approach the organisation and vice versa. The digital HR equipped with state of the art tools will be capable of reaching the global talent pool, and provides the greater exposure to attract and retain the best candidates faster and better than the competition.

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Where and How Big Data Fits in Human Resource?

hr big data analytics

With the advent of big data, HR is primed to become a right strategic and more evidence-based business partner to senior leadership. Today, big data analytics and HR work as “one” providing an opportunity for businesses to make the most rigorously evidence-based workforce management decisions ever.

Big data not just fuelled hiring but solidify the HR’s reputation as a strategic business partner that makes evidence-based, analytics-driven decisions – especially when it comes to talent. It means speeding up the hiring process, improving sourcing and selection and reducing the cost. All of which equate to substantial competitive advantages.

HR Big Data Analytics – Two parts of one equation

The approach of data as a prediction business tool is not novel or new, but the sophistication and intensity of which it is now being used are quite new. The role of big data analytics in HR and L&D is of utmost importance because it empowers the connections and conversations. It offers a new set of insights, around the complex and profound organisational issues including, change, culture and learning. HR big data analytics help businesses to optimise the way in which they deliver and scrutinise the transactional elements of Human Capital Management.

What is big data?

Big data is exceptionally large sets of data that may be evaluated computationally to reveal trends, patterns and associations, especially relating to human interactions and behaviour.

However, HR Big data analytics primarily defines the analytic techniques operating on big data. Rather relying on intuition, decisions are made by massive amount of current and historical data, emerging technologies and statistical analysis.

Why is big data needed in HR?

By using the HR big data analytics solutions, businesses can analyse the vast amount of data in minutes and seconds. It helps them to reveal previously hidden sentiments, patterns and workforce intelligence.  The accuracy and speed of insight delivered to any device including tablets and smartphones. It implies that organisations are now in a position to make better, faster decisions.

Here are some statistics to give some idea of the scale of change that organisations can expect with the arrival of big data:

– By 2018 it is estimated that 64,00 organizations with a workforce of 100 or more will implement the HR big data analytics.

– AS compared to last year; there is a significant increase of 38 percent of companies correlating business impact to HR technology.

The HR Big Data Analytics – Challenges Ahead

hr big data analytics

When enterprises seek to develop HR big data analytics perspective, they experience some challenges. The first and foremost is laying their hand on the data they need – which is refined, systematic and reliable.

According to industry speculators, such data falls into following 3 categories:

  1. People data. Including skills, demographics, engagement and rewards, etc.
  2. Performance data. Data captured from the use of instruments including, goal attainment, 360 assessment, and succession and talent programs.
  3. Program data. The data collected in the form of participation in programs, attendance and adoption ranging from talent management to leadership programs, training and development to key projects and assignments.

The Problem.

Here the challenge is this much data is often long-winded and difficult to access. The problem arises due to two reasons – systems and structures.

1. Structural barriers

It primarily indicates the issue among relevant people, HR functions and performance operations. The problem is augmented when separate HR teams are operating across the business units. For instance, in conglomerate companies, data often become the glue which links together the vast purpose and mission.

The ideal solution is to share data and turn it into valid insight as a business improvement tool.

2. System barriers

The existence of poorly integrated and incompatible systems is another major issue among organizations which is holding them back. Most companies are operating under such HR systems which cannot talk to each other.

Also, there is a concern of security. Sometimes, the smooth flow of information access is hampered by authorisation problems which distort everything from critical information to social media access.

Above all, database and IT skills can be perceived as a challenge for most organizations. Today, the advent of transformational HR tools and systems demands the ability – to program often prerequisite and to use database query languages to run some fairly basic data enquiries. Now, it can provide skill problems every so often necessitating the resourcing of “extra capability”.

To tackle the issue, it is imperative for organisations to have integrated IT and HR systems which enable the data to be stored consistently. Also, empower people to access the data with appropriate protections for integrity.

Also, organizations must also select right tools for allocating access and analysis. These tools must be integrated with wider systems.

Where the big data analytics fits in the human resource?

When HR is concerned, big data is a big deal. It empowers human resources and employers to make more informed decisions. Now, let’s take a look at where big data analytics fits in HR or other words how HR can leverage the “big data” to simplify their workflow.

1. Talent

Quickly and precisely forecast “Who”. With sufficient data, you can easily predict who going to be high performers and high achievers among new hires. It implies that you will quick in deciding if they should be shifted to fast-track programs.

2. Turnover

Attrition is the nightmare for most of the organizations, today especially when workforce size is becoming millennials dominant. With HR big data analytics you can forecast the risk of the most turnover based – which positions, which units, and which functions. Moreover, you can reduce the loss by modelling the scenario in advance.

3. Risk

Leverage the HR big data analytics to run the simulations on which candidates are likely to experience the drop in their performance. Also, create realistic profiles of which candidates are at risk of leaving prematurely and when.

4. Retention

Similar to attrition, retention is something that needs everyday innovation. Here again, big data analytics can help you to determine what resources should be targeted and allocated regarding retention activities.

5. Future-ready

HR big data analytics also enable you to model the various changes that an enterprise may experience from political to global level. And, what the impact of talent sourcing, hiring, engagement, performance management and retention could be.

When is the right time to embrace the HR big data analytics in your organization?

We already know that when it comes to HR technology, “no-one-size-fits-all”. Similarly, not all organizations are ready to adopt the big data analytics yet.

Here are handful questions that must be answered which help you decide better whether you should consider the HR and big data mix within your organization or not.

– There is a business problem to be solved.

– The CEO (business leader) wants it.

– Regulators demand it.

– Investors are interested in it.

– We can use it to cut more cost from HR.

– The cutting edge technology is here, so let’s use it.

As a business leader, you need to evaluate your reasons as mentioned above wisely and present a convincing statement why you need to implement the HR big data analytics within your organization.

Adoption of HR Big Data Analytics Demands Transformational and Tactical Approaches

If data driven and people analytics strategies are to take off, they should be pushed as transformational projects with day-to-day management and total enthusiasm.

Tactical Approaches

– Make stories from the collected and derived statistics.

– Tap and map the skills necessary. Leverage the talent to develop aligned analyst.

– Attract more capability from areas such as economics, psychology and anthropology.

Transformational approaches

– Move it up to the HR capability plan.

– Make HR big data analytics a continuous transformational development

– Emphasis it on the main business priorities

Today, big data analytics is the key part of the business conversation. Hence, HR professionals need to fully embrace the challenge of workforce analytics and meet the looming challenge of big data. To successfully achieve a business goal it is mandatory for organizations first to understand how and where big data analytics fits in their HR.


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Predictive Analytics – The Next Big Thing In Talent Acquisition


As the world progresses and technology advances, our lives change rapidly with each passing day. The domain of recruitment is no exception. Today, the overall process of hiring and recruitment has undergone substantial changes especially with technology foraying in.

Gone are the days when hiring and recruiting were largely dependent on the gut feelings of employers and they fell back on the years of experience that a candidate has. Today, analytics plays a significant role in the overall process of recruitment and has substantially redefined the practice of hiring. Let’s explore predictive analytics in this purview.

What is Predictive Analytics?

The branch of advanced analytics used to make extrapolations about the unknown future events is referred to as ‘predictive analytics’. In this case, it is about recruiters predicting for probable candidates for a vacant position. Predictive analytics implements many techniques including data mining, statistics modelling, machine learning and artificial intelligence to scrutinize existing data and make predictions about the forthcoming.

Predictive analytics in recruitment allows organizations to become proactive, advancing, anticipating outcomes and behaviors based on actual data rather than falling back on a notion of assumptions.

Predictive Analytics in Recruitment

Essentially founded on human capital-based statistics, predictive analytics is becoming more and more appealing to the HR departments across the globe. It is permitting HR branches to be more tactical in predicting about following resources and also possessing the right skills for various teams a few years down the line. According to a Daily, The Globe and Mail, “Companies investing in predictive data analytics use statistical models to identify trends and develop short-and long-term strategies for hiring, retaining and developing talent”.

Predictive analytics in recruitment is growing rapidly in the realm of HR these days, and this increased attention is due to the following reasons:

  • The volume of data that can be consolidated online from social media posts to purchases.
  • An increasing reach of workforce analytics software and technologies that are becoming more accessible and available.
  • The capability of companies to generate algorithms from GDP, unemployment rate, and growth, turnover rate, and other workforce trends to predict their future needs for human resources.
  • Companies are becoming more proactive in their process of hiring.
  • To understand how to engage and retain employees for a long time

Benefits of Predictive Analytics in Recruitment

Img. Src. roundworldsolutions.com

The effectiveness of predictive analytics is pretty much evident, right from the pre-hiring process. It enables HR and hiring managers to make a much better recruitment that would ensure business benefits in the long run. Three main ways in which predictive analytics assist in the pre-hiring process is explained below:

1. Improves quality of hiring process

The first benefit provided by predictive analytics is it improves the quality of recruitment. By combining the recruitment process to production performance, engagement survey information, attrition data, and other data from the employee lifecycle, prototypes can be generated that will predict the potential future performance of a candidate. While substantial gains may be possible, constant adjustments to the hiring model will lead to marginal improvements in the overall hiring process. Hiring managers should also ensure that their hiring models have reliable processes and data. Some examples include engagement survey data, recruiting sources, turnover data, pre-employment assessment results, interview results and performance results.

2. Makes sourcing more effective and efficient

Well, sourcing the right candidate for an open position is something that recruiters grapple for days together. It is where predictive analytics come to the rescue. Proper implementation of predictive analytics in recruitment, enables managers to optimise their recruitment marketing strategies and eliminate poor sources. In addition to this, the same method can be applied to evaluate in-house recruiters, job boards, third-party recruiting firms, and other sources.

3. Enhances the speed of hiring

The final benefit of predictive analytics is, it can substantially improve the rate of hiring. As the hiring model is developed, the understanding as to which candidates are the best for the company also improves essentially. The organization then will be able to deploy tools that will serve as leading indicators of potential job performance. Once a potential candidate hits on the hiring radar and fits your job models, recruiters will be able to move quickly, connect with them and eventually take things forward. Because of the confidence on the hiring model, recruiters will be able to focus on candidates who are apt for the business.

Of late, several organizations have implemented predictive analytics and reportedly performing better in the realm of recruiting and even retaining candidates. Within the domain of talent management, Google has excelled by implementing predictive analytics in recruitment, in leadership and even in retention. Other organizations that have progressed with the implementation of predictive analytics is Cisco and Sprint.

Going forward, it is estimated that predictive analytics will be adopted by most organizations globally for both, hiring external and internal candidates. Given the number of tools available in the market, it is somewhat evident that predictive analytics has become much more than just a buzzword. Shortly, every organization must leverage the power of predictive analytics in recruitment to be able to make the best hires and ensure that businesses work out to their fullest potential.

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Augmentation of HR Analytics – Driving Insights To Action

Driving Insights To Action

Today, businesses can drive financial return on human capital investment and increase the value the workforce remit to organizational performance through the use of HR analytics. The recruitment leaders understand precisely how increased the power of analytic insights and metrics can align core workforce processes with organizational strategies and enable organizations to make the necessary business decisions today, for their future.

By using analytics, organizations can more efficiently improve and manage performance. The skilful use of HR analytics can assist business to improve its productivity through:

– Balancing the lowest effective headcount,

– Efficient workforce control while confirming satisfactory service delivery,

– Thereby appealing to the Executives, Chief Human Resource Officer (CHRO) and the Line Managers can perceive top and bottom performing employees – to better develop and retain key talent.

Also, address looming gaps in required competencies and retention trouble spots.

Analytic Workflow Re-defined with Broadbean Analytics Suite

The embedded decision support and analytical workflow are strategic elements of logical user interface. The Broadbean Analytics Suite address the objective of warranting that Human Capital is leveraged appropriately, guiding the team of hiring managers and recruiters from the summary level to details stockpiled in the Human Resource transactional system.

BDAS is a next generation recruitment analytics software that provides fact-based, timely insights into activities across the entire organization. For the reason that data is drawn from multiple systems, cross-functional analysis befits and soiled views eradicated.

Focus on Information Needs through Analytics Workflows – Tricks of the Trade

Driving Insights To Action
Img. Src. edureka.co

The primary function of HR is eventually to augment the workforce through apt processes to acquire, develop, optimize and pay the workforce while complying with statutory obligations. Each of these practices has a set of objectives; every so often there are concerns in meeting these aims.

Organizations can make efficient use of analytics to dig into the issues surrounding each process, using an analytical workflow of Broadbean Analytics Suite that guides users to answer demands of recruitment area, to gain acumens from accessible information, and then to take action. For instance,

ACQUIRE. To meet the business objective of cost-effective, efficient, and scalable recruiting process, HR leaders can look into the recruiting pipeline to develop a robust recruitment plan.

The role of BDAS. The analytical workflow of Broadbean Analytics Suite (BDAS) can guide your recruitment team to drill down to see the best talent recruiting sources & candidate pipeline for particular jobs, starting from a high-level metric of new hires. Along with, identify the best candidate source and average time & cost to hire.

OPTIMIZE. Developing a strategic workforce plan to meet the business objective, workforce composition can be compared with external data to stimulation how population aging will influence the personnel.

The role of BDAS. Backed by accurate data & easy-to-understand reports BDAS would guide the business leaders through evaluating the current attrition trend, headcount level, and anticipated turnover consequently that capacity can be forecasted. It also helps Talent Acquisition Heads to develop forward-looking scenario based workforce plans.

The BDAS empowers recruitment leaders to take action based on analytical results a workflow, facilitating navigation to more information, and expediting where to take action.

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