How To Get Started In Data Analytics

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How To Get Started In Data Analytics – People analytics is at the heart of HR. Decision-making about people in an organization is now highly analytical and data-driven, and having and using people analytics that work well is essential to winning the war for talent.

Over the years, people analytics has evolved from HR systems reports, headcount, vacation leave and sick leave data to more advanced capabilities such as talent management and workforce planning.

How To Get Started In Data Analytics

How To Get Started In Data Analytics

People analytics is collecting and applying organizational, people and talent data to improve critical business outcomes. It enables HR departments to gain data-driven insights to make decisions about various people processes and turn them into actions to drive an organization’s performance.

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Organizations use people analytics in key HR functional areas such as compensation, recruitment and selection, retention, diversity, inclusion, equity and belonging, and performance management.

The terms people analytics and HR analytics are often used interchangeably. However, there is a difference. HR analytics implies that data is exclusive to HR, while people analytics goes beyond HR to include finance, customer, marketing and other data sources.

Additionally, HR analytics are often overused and service providers often focus only on HR solutions rather than holistic people analytics solutions.

There are four types of people analytics, each providing different insights. Each can be useful on its own, but provides a more comprehensive picture when combined. When deciding which method to implement, consider your needs and the data you have access to.

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Descriptive analytics (also known as decision analytics) is a basic type of people analytics that analyzes patterns in historical data sets to gain insight into what happened. However, descriptive analysis does not use this data to make future predictions.

Descriptive analytics is the most common type of people analytics organizations rely on. 83% of businesses use this type of analytics.

Diagnostic analytics takes descriptive analytics a step further and provides an underlying explanation for the insights discovered in data trends, correlations, and anomalies.

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Predictive analytics categorizes past and present data to determine insights and then uses an evaluative model to predict what might happen in the future.

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Predictive analytics is the last step that channels predictive analytics into decision options and actions to achieve success.

One of the first uses of analytics in Human Resources was human capital analysis (HCA). This discipline emerged from accounting and economics to help organizations determine the financial value of HR.

Many HR experts believed that HR should function like any other business function (sales, marketing, IT, etc.) and have operational metrics to track its effectiveness and the value created for the business. This is where the shift from HR being seen as a support function to a business function began.

The HCA has many limitations, the main one being that it is based on income and focuses primarily on the economic value of employees. This paved the way for newer, more comprehensive approaches such as people analytics, which is focused on people and efforts to solve business problems with people.

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It is also cross-functional and incorporates a variety of data sources (finance, marketing, customer, etc.) to create actionable insights. While HCA can only give you the average economic value per employee for your organization, people analytics help you understand employee performance and how to optimize it, which has an impact on your bottom line.

People analytics has also moved HR from prescriptive analytics to predictive analytics, meaning businesses can make decisions about hiring, training and other people based on data.

Let’s explore some people analytics case studies where global organizations are successfully using people analytics to make business decisions.

How To Get Started In Data Analytics

Google was among the first organizations to use people analytics in decision making. For example, their People Innovation Lab (PiLab) uses a variety of data to determine what makes a great manager and an effective team. They then apply their findings to team structure formation as well as hiring, training, and promotion decisions.

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In an organization like NASA, where specific data science skills are needed and knowledge is constantly changing as technology advances, finding the right talent is a challenging task.

By building a talent map database using Neo4j technology, NASA creates knowledge graphs to show the relationships between people, skills and projects. These charts allow NASA to identify specific skills, knowledge, abilities and technology within a job role, then translate them to an employee, their projects and training.

For example, with employee training, some knowledge must be taught. This requires a skill to be developed and to acquire that skill, certain exercises or tasks must be performed that enable the employee to become proficient in that skill or ability. This allows NASA to identify the “DNA” of an occupation, gives employees a chance to develop or change careers, and helps NASA better align its people across the organization.

Microsoft launched a tool called Manager Hub, a knowledge generation platform and one-stop center for managers to find information.

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The platform provides suggestions for managers to take specific actions and why. For example, real-time data-based requests such as whether managers have one-on-one discussions with employees and whether they have “bonding sessions”. The tool is managed through push notifications which are linked to the work calendar.

Uber successfully enabled people managers with people analytics, which resulted in greater employee engagement and improved business results. The company achieved this in three steps.

First, Uber ensured that the right people could access the necessary data and dashboards. They empowered managers to access their people analytics solutions, not just HR.

How To Get Started In Data Analytics

Second, the HR team took a user-centric approach by asking leaders what they needed and then designing their people analytics solutions around that.

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Finally, Uber optimized their employee dashboards to provide clear insights on a specific question. They removed all unnecessary visuals and panels, making it simple for anyone to interpret and set up. Previously, managers at Uber would have a two-week turnaround time for a talent decision because they had to go through HR. However, with real-time data available, they can make quick decisions and improve their effectiveness.

By analyzing internal data, research, and studies, combined with expert judgment, experience, values, and concerns, HR can make evidence-based decisions rather than relying on a “gut feeling.” This helps remove biases, temporary fixes and inconsistencies.

Through people analytics, HR can turn data into action and align facts with organizational strategy and business goals. By demonstrating how proposed people strategies can help the business thrive and meet its objectives, HR can gain a seat at the leadership table and educate leaders on how new strategies and processes will increase revenue and drive the business forward.

Dashboard and trends that highlight specific issues, including collaboration, workload, diversity and inclusion, and workplace risk assessments, all optimize performance. In some cases, people analytics has nearly doubled employee output and, therefore, performance.

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People analytics can help the organization allocate its budget more effectively by demonstrating the potential value of each dollar spent in different scenarios. For example, suppose the data show that a particular L&D program improves employee performance and earnings. In this case, it makes sense to invest more in this specific program and cut others that don’t produce the same value.

Likewise, a recruiting pipeline can continue to provide you with the best candidates who continue to be high-performing employees. You may decide to invest more in this channel and reduce spending on other channels that are bringing in low-quality candidates.

Using artificial intelligence and human analytics, IBM can predict with 95% accuracy which workers will quit. It enabled them to address these concerns and work on other areas, such as future hiring for at-risk roles.

How To Get Started In Data Analytics

Left unaddressed, skills gaps can negatively impact employee engagement, morale and turnover. People analytics allows organizations to understand the current skills of workers and the future skills needed according to business demand, thereby bridging the talent gap.

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You can identify the most suitable employees to upskill, which reduces recruitment costs and helps you use the full potential of your existing workforce. After that, you can recruit new talent to fill any gaps that your current employees can’t fill.

With this data, HR can work to reduce turnover rates by creating targeted strategies that address the root causes of why employees leave the organization. An example of this would be leaving employees around the three-year mark due to a lack of career development opportunities. Your strategy can focus on creating personalized employee development plans with career paths for your workers.

Hiring the wrong people is costly to business and organizational culture. Tracking key recruiting metrics, including cost per hire, quality of hire and candidate experience, provides valuable insight into the strengths and weaknesses of your hiring process. This helps organizations understand who to hire, improve their hiring journey and reduce recruitment turnover and costs.

HR metrics serve as the foundation for people analytics by providing the necessary data. People analytics help translate these metrics into actionable insights and identify areas for improvement, predict future needs, and develop strategies to optimize talent management.

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Employee turnover rate is a metric that tells you how many employees leave the organization during a given period, usually on a monthly, quarterly, or annual basis.

With a thorough analysis of employee turnover data, you can measure the number

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