How is HR analytics changing people management? 12-25

Shyam's take on HR analytics

I agree with Vinay on the need for HR analytics in the diverse cross culture environment in India.

However the accuracy and usefulness of the data depends on the quality of its integration.
This is possible only when the Data Scientists have also knowledge about HR domain. Otherwise the inference we draw will have a huge margin of error and so will the action on the benefit of it.

I suggest that, after drawing inference from the data. The HR specialist should interact with the concerned employees to authenticate the results. If personal interaction is not possible, they can send out a questionnaire and prepare reports based on the employee inputs. And  when taking a decision or inferring an opinion, it is suggested that data output along with the Report of the actual interaction with the employee should be taken into account before coming to a conclusion.

Specially when working on the Predictive analytics, it essential that, the stats are as closer to reality as possible.



How is HR analytics changing people management?

It is developing an engaged, agile and flexible workforce by making use of data to obtain valuable insights on the employees.

          

In today’s VUCA (volatility, uncertainty, complexity, and ambiguity) landscape, small and big companies are continually facing uncertainty and volatility in their operations. The working environment is increasingly becoming very complex and ambiguous. This has put human resources into a challenging situation in terms of making definitive decisions in the workplace. HR has to look constantly at new tools for making risk-free decisions, and this is where the help of HR analytics comes in.
HR analytics is the use of statistical methods like factor analysis, correlation and regression and making use of different sources and variables to arrive at meaningful insights. It helps HR understand the dynamics in the workforce.

One cannot rely on a person’s experience or tacit knowledge to base one’s decisions on, but on real data and numbers to back one’s workplace decisions. This has proved to add value and improve workforce resource utilisation and deployment. HR analytics is not about merely collecting vast amounts of data, but analysing and processing it to provide meaningful insights. These insights are then used to provide the answers sought by the company, to the following questions, among others:

• Will the predictive attrition model help identify who is going to resign in the next, three, six or 12 months? What is the efficiency factor of this predictive model?

• Is the new tax-friendly employee compensation and benefits programme driving higher retention?

• Are the highly engaged employees at the workplace highly productive? Are they spreading positive employer brand in the workplace and social media?

• Will work from home/flexible working hours lead to better engagement, and to what extent?

• Which generation of employees (Gen X, Gen Y, Gen Z , Millennial) is contributing more towards profitability?

• What are the major reasons for attrition? Is the preventive HR intervention effective in controlling attrition?

• Is there any correlation in employees who are leaving within six months of joining?

• Is there any relation between employees who stay within 10 km radius and retention?

• Is the newly designed Employee Fraud Risk Management effective?

Is there any correlation between frauds committed by habitual noncompliant employees/ disengaged employees?

Finance function decisions are based on data, such as graphs, charts, etc. However, in HR, it is based mostly on the relationship component and there is always the risk of being judgmental when it’s a person making decisions. Through HR analytics, this risk can be minimised.

HR can study the behavioural competencies of high-potential employees and share the data with the recruiting team to help them identify potential employees. This data can also be used in training. HR often comes up with various mechanisms intended to achieve employee satisfaction, leading to employee engagement. But today, the mechanism depends on what the employees value more and whether it is going to achieve the outcome.

Based on the HR analytics data, human resource policies are customised, for example, the recent decision by the Government of Delhi regarding the odd and even number cars taking turns to run on the roads. This has led some companies to offer Delhi metro cards to employees and encourage them to take local public transport instead of using their cars. Some companies are exploring the option of working from home.

People management is being accomplished with change thanks to evolving HR analytics. It gives a clear view of performances and drives the organisation towards its goals. This has led managers to understand their employees better and contribute towards planning for their training and development. In turn, the employees will be motivated to be more in tune with the culture of the organisation.

People analytics empowers HR with the ability to identify the gaps between employees and training, which is necessary to be bridged. This is especially important with new hires to help them understand expectations and workflow processes quicker. By digging into insights, HR managers can not only identify which hires will be low but also the high performers.

Predictive analytics contributes to analysis of the workforce beyond the mere basis of academics. While recruiting, many companies look for candidates with high academic records. But this is not full proof of a candidate’s ability to perform. Some bugging questions come to mind:

• Why does one programmer perform better than the other?

• Why do some average employees succeed while some perceived as good recruits fail?

• How much time does it take the new employee to be productive?

One way to employ the use of HR analytics is acceptance and deployment of HR management systems and cloud storage availability. These make it possible for organisations or companies to store all their data in a manner that can be integrated with other workplace operations.

Such cloud software today is readily available and affordable. It is time for employers to take advantage of this progress. By deploying HR analytics, companies collect data based on attrition, retention and other HR aspects. These metrics are then used further to look for trends based on ratios and accounts. The data is then vigorously analysed to give Google insight into employee trends.

These insights could be effectively used to shape the organization’s policies and decisions.

Businesses have noted that the ability to take initiative is a better indicator of actual performance.
Companies are deploying predictive analytics to drive their HR strategies. Employee data from all locations is analysed and processed to develop programmes based on age, demography and tenure. Information from predictive analytics/HR analytics is used to attract, retain and manage the right talent. To change the course of the manner in which they deal with employees, data-based information is used like predictive attrition models and exit analyses. This predictive modelling tactic is more handy than trying to figure out later why an employee left.

The new approach of HR/predictive analytics provides the ability to measure, quantify and qualify employee data. The new method digs into the ‘why’ rather than the ‘what’ to get the better perspective of things.

HR analytics helps to develop a workforce that is engaged, agile and flexible by using data to gain valuable insights on employees. In the diverse Indian context, the need for HR analytics is felt even more because every company has a broad array of people to manage. Proper management of this varied workforce is a prerequisite for greater synergy and productivity within an organisation. Companies must look towards HR analytics as the new tool to power an organisation’s success through smart people management.

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Are You Ready for Personalized Predictive Analytics? 05-11

Are You Ready for Personalized Predictive Analytics?

Predictive analytics have the potential power to "produce remarkable services and longer lives," says James Heskett. But can businesses make bets in this area without first understanding the social consequences? What do YOU think?

In 2002, the film Minority Report introduced many of us to the world of predictive analytics. In it, an innovative technology allows Washington, D.C. to go without a murder for six years by helping Tom Cruise, chief of the Precrime Unit, to identify, arrest, and prosecute killers before they commit their crimes.

This was a case of the movies catching up to the business world. At that time, predictive analytics had been applied to the continuing maintenance of everything from CAT scan machines produced by GE to elevators made by Otis. It enabled these firms to sell "up time" rather than just products, thanks to a number of sensors and the continuing remote surveillance of the performance of these products.

Predictive analysis applied to humans is now one of the hottest concepts to come along. It is being made possible by a system of customer loyalty programs, big data, and cloud computing that enables the continuous collection, storage, combination, and analysis of data about each of us from a number of disparate sources. Pretty exciting, no?

Some years ago, we heard the story about the GE maintenance engineer who, based on information from the firm's advanced monitoring and predictive analytics, visited one of his hospital accounts to repair a CAT scan machine that had not yet failed. As he was confronted by puzzled hospital administrators, the machine indeed stopped functioning. More recently, many of us have heard the story about the Target customer who was sent information about products of interest to pregnant women before she knew she was pregnant. Target's Big Data analysis of hers' and others' purchases, combined with related information, had placed her in a cohort with other women known to be pregnant.

Predictive analytics will be essential to the development of concepts such as 30-minute package delivery that companies like Amazon have been contemplating. For years, logistics have been managed by principles such as that of "postponement and speculation." The idea is that to approach the best match between supply and demand at a reasonable cost, a supplier has two basic choices. One is to delay (postpone) committing inventory to a particular supply point for as long as possible through such things as careful forecasting of demand, rapid manufacture, and fast transport. The other is to invest (speculate) in long but economical production batches, slow but economical transportation, and large amounts of inventory that ensure an in-stock position when an order is received.

An argument can be made that any forecast and inventory is based on predictive analytics. But in the past, these analytics were applied to data that described behaviors of large groups of decision-makers. By contrast, tomorrow's version of this technique will be based on the analysis of massive files of individual profiles, from which predictions will be built that establish stock levels needed to support 30-minute deliveries. Personalized logistics will take a lot more than just drones.

Predictive analytics have the potential to produce remarkable services and longer lives. But before we become too enamored with them, it's important to remember what happened to Tom Cruise in the movie. He is eventually accused on a precrime basis of murder, with only 36 hours to determine whether the charge is accurate and, if not, who implicated him wrongly.

How important are these concepts to our future? Is this a big deal or just another buzz term in business for the next several years? Are you ready for predictive analytics applied to you? If not, what are you going to do about it? What do you think?

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Big Data and Data Center Operations 02-12

Big Data and Data Center Operations

Scott Koegler recently wrote a post titled ”Use Big Network Data to Predict and Avoid Network Problems” where he describes the use of data analysis and predictive analytics by IPSoft. Scott wrote:

"By turning predictive analytics inward to track where breaks happen most frequently, IT and network admins can set more accurate thresholds for recurring issues, positioning themselves ahead of any damage.”

Big data and predictive analytics are a great fit for the data center and IT operations, especially within the modern data center. There’s a great deal of data being generated and managed within IT operations, and the approaches and systems found within big data can help better understand and manage operations.

The example that Scott (and IPSoft) used is one that can provide value for any organization because it allows IT operations to understand (and predict) when breaks or issues might arise within the data center, network or remote office.

Imagine how impressive it would be for an IT specialist in a central office to get a notification that something in a far-flung branch office is amiss. Imagine again how that notification could tell IT staff exactly what was wrong and provide a recommendation to “fix” the problem before it actually became a problem.  This capability is available today with predictive analytics and data analysis.

Using predictive analytics and other big data approaches to identify bottlenecks, manage incidents and fix issues faster is the next logical step for data center and IT operations.

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