Applying Talent Analytics - People Analytics Rahul Ghatak

Most HR professionals struggle to make linkages with business outcomes. A great deal of time is wasted on HR process flows, details of various programmes and

their reporting instead of asking the right questions: What critical business problem requires solving? What relevant data needs to be collected and analysed for me to provide solutions and alternatives? How will data help me, and what data should I use? Which roles impact key business drivers the most, i.e. revenue, growth, market share and profitability and how do I hire significantly more “right-fit” as opposed to “wrong-fit” in these roles? Or how do I enhance retention of key talent in these roles? Which talent drivers for instance will enhance customer loyalty?

Is there a strong correlation between employee engagement and customer loyalty?

While this is a nascent domain, it is a rapidly developing one, with a few organizations showing the way by leveraging the body of research as well as running bold experiments like what Google did with Project Oxygen. Companies that have leveraged People Analytics successfully have clearly brought out the business case through the connections they were able to establish between different threads of data across multiple domains and data types—both structured and unstructured.

Organization health—Analysts in a reputed aviation company developed a met- ric—the “crew-member net promoter score”—that monitors employee engagement and predicts financial performance.

Hot spot identification—Managers at a famous aircraft manufacturing com- pany used an automated system to collect timely performance review data and identify areas needing improvement. The company correlated knowledge man- agement and training data with performance data with a view to identifying top talent while also trying to establish which learning and development programmes were contributing to enhanced performance. These insights allowed the company to devise learning strategies around content and delivery that directly impacted performance.

Retention rate improvement—A food services organization was able to bring out on the back of analysis and evidence-based insights a correlation between employee empowerment levels on the one hand and engagement and retention on the other. This was a eureka moment for the company which then replicated its

employee first actions that enabled empowerment of its delivery associates (customer facing staff) resulting in significant enhancement of their retention levels by 20% points, thereby saving massive spends around hiring and training.

Maximize productivity—A manufacturing company that is known for innova- tive practices leverages analytics to demonstrate linkages between rewards, hiring and talent practices on the one hand and workforce productivity on the other. The analysis revealed that business units with higher engagement levels emerged more productive and profitable in the medium term.

3.7 Organizational Networks—Uncovering “Hidden and Passive Internal Talent Pools”

Organizational network analysis is a mechanism (using passive and active analysis) to uncover how employees interact with each other to get the job done. There are tools available today to lay the ground for agile networks that enable better collaboration, knowledge sharing and talent risk mitigation. This is a process that allows you to analyse informal networks and relationships within the organization to provide deeper insights into informal structures and teams that shape organizational outcomes. With much of the communication within and outside organizations going digital, an analysis of this digital footprint via data mining of communication networks is thus made possible. This enables organizations to analyse at a deeper level, the communication network which hitherto was not possible.

Passive and Active ONA

Modern organizations that are large and complex are driven by work, knowledge and collaboration. They sit on massive repositories of communication data that is hardly ever analysed or utilized. How can we slice through this complexity to truly understand modern organizations and use that knowledge to drive performance. Networks are everywhere. For example, think about the people that you know well and which of them know each other. That is a network. But, most impor- tantly, modern organizations are a collection of networks—both inside and outside of the organization. ONA allows you to collect information on these networks, to analyse them, to truly understand them and, in doing so, to drive change and improvements. Think about all the sources of network data available—e-mail communication, instant messenger conversations, 360° performance reviews, enterprise social networks like Yammer, social networks like Twitter and so on. With ONA, you can interact with it, visualize it and analyse it.

Organizational network analysis has two sides to it—passive ONA and active ONA. Passive ONA is about analysis of communication data, for instance e-mail or messenger logs to analyse how people and teams are connected within an organization. Active ONA is about using a series of survey questions regarding relationships among employees and across teams—Who do you go to for problem solving in your network? Who do you approach for coaching on the job? Who do you reach out to for guidance and advice? Who do you work with often?

ONA Technology

Today, there are cloud-based technologies that enable organizations to analyse these informal structures and networks and derive critical insights, impacting business outcomes.

Key Features—Upload, Analyse and Collaborate on any Set of Network Data

• 100% web based

• Fine-grained user permissions for collaboration

• Upload networks using Excel or in more advanced formats

• Export to Excel, PNG, SVG or more advanced formats

• Create, save and share multiple views

• Multiple network metrics

• Multiple layout algorithms

• API access.

Key Uses of ONA

ONA is used to identify key stakeholders in the network such as Influencers, key players, knowledge brokers, change catalysts as well as blockers through an anal- ysis of social and work networks in organizations. It is used to figure out how high/low-performing teams function; position employees where they can deliver the most impact; identify people to serve as catalysts of change during transfor- mation; demonstrate where work begins, stutters and halts; analyse the impact of DandI interventions; develop insights into knowledge exchange and collaboration across teams and hierarchies. This enables companies to holistically analyse social networks to enhance collaboration across the organization.

The major benefits of organization network analysis includes

• Identifying individuals who are either key risks or key talent

• Identifying hidden operating process issues

• Optimizing functional relationships to improve: cross-sell; client services

• Mobile workforce optimization

• Collaboration within and between teams, groups, etc.

Key Metrics—Some Examples

1. Identify influencers: This is about leveraging network analysis to identify key influencers who are common points of contact for multiple networks and teams.

2. Collaboration matrices: This is about understanding the nature of networks to understand collaboration levels within and across teams.

Identify Influencers

A key functionality of ONA is about the ability to identify key influencers or knowledge brokers who are connected to the maximum number of individuals in the network. This group of influencers may be leveraged as catalysts for change or may be used as a bridge between the leadership and other levels within the organization from a communication and degree of collaboration perspective.

Collaboration

ONA also summarizes the interactions between different groups in the network. It brings out levels of communication and collaboration within and between teams—

for instance sales and credit risk or customer services and shared service support;

while some teams, such as RandD, are more compartmentalized. The analysis around team working and support across location, hierarchy, functions, gender and ethnic groups also bring out the “Diversity and Inclusion” quotient of the teams and networking within the organization.

Dalam dokumen People Analytics Rahul Ghatak (Halaman 67-72)