Enabling Insights-Driven Leadership Through State-of-the-Art Analytics

It is not a big secret that data analytics and data insights have become words du jour recently. Everyone has heard about the theoretical benefits and the enormous potential of data analytics for managers, employees and all types of organizations. However, practical implementation remains fuzzy within most companies. In this blog, the Capgemini Invent analytics experts will change your perspective and describe concrete implementation examples for insights-driven leadership.

In our previous blog, we have shed some light on the insights-driven leadership and defined it based on two perspectives, namely data capabilities and data mindset. Today, we talk about the daily usage of data analytics and in particular data visualization by tackling the following questions:

• How can data be transformed into meaningful insights?
• How can leaders leverage generated insights?
• How can AI and Data Analytics help to better understand your people and organization?

Business Analytics — Ways to Enrich Leadership with Insights

Data analysts and business leaders can decide how to use data generated and stored by the company within its HR function and organizational challenges. To do so, it is key to ask the right questions. With help of BI tools leaders can get a helicopter view and / or drill down to a more granular level for further analysis. With dashboards and score cards detailed levels of information get easy to understand and interpret. This allows to understand complex connections and interdependencies. Coupled with user friendly visualization functionality, dashboards and score cards it helps to provide tailored insights to the different audience groups.

Exemplary, a question that is equally relevant for HR professionals, business leaders and all employees: is our company paying competitively? Answering this question correctly might have multiple implications on:

• Hiring process, i.e. higher job acceptance rate
• Satisfaction, i.e. performance-based remuneration aligned with peer group
• Attrition and retention, i.e. salary in line with competition

By combining data from various internal and external sources, such as employee performance evaluation tool and comparison websites such as glassdoor.com, a decision maker gets a completely new perspective. For instance, these tools include compensation, accounting and hiring tools as well as external data on average salaries. A BI tool can instantly help to recognize the interconnections between the job, grade, location, age and other attributes with the salary. In addition, the salary is shown as the mean and median, bench-marked against competition with possibility to dive deeper to individual levels.

By identifying the optimal salary span, leaders and HR professionals can improve the compensation strategy. In addition, by applying data modeling it is also possible to simulate and predict the impact of salary change on a variety of dimensions such as motivation, sick leave and over hours.

Another application of the HR data is examining the efficiency of organizational structure. By using just one dashboard a leader can instantly overview the “utilization-rate” and “manager-to-employee-ratio” or overall average. Information can be further split into geographical, organizational or other areas. Furthermore, based on the available data, recommendations such as the “recommended number of employees per manager” can be suggested. Alternatively, the data can be further linked to other indicators, such as “retention rate per manager” or manager assessment. This will help leaders to identify the bottlenecks in terms of capacity, identify top performers, better plan the recruiting campaigns, or get other tailored insights.

For instance, having a manager with over 10 direct reports in combination with decreasing satisfaction of his subordinates might indicate that the manager is overwhelmed and that the problem might be solved through allocating the employees to other managers.

Our final example is an end-to-end recruiting pipeline including the forecast for potential new hires. The data comes from recruiting software such as Taleo in real time and can help understand where the company is standing. In particular, it helps to determine insights from recruitment, the average recruitment time and average recruitment costs. Hence, HR professionals can better predict and plan recruitment campaigns, understand which departments are most likely to face employee shortages. This helps to act immediately and improve the process in the long run.

Data Architecture for HR Data Analytics

Of course, this type of analytics requires sophisticated and up-to-date data bases. Data warehouse is the cornerstone of the data analytics. There are two general architecture concepts of data warehouses:

• Offline DWH — people data is being loaded on a regular basis
• Real-Time DWH — people data is being uploaded to the DWH after each transaction

Depending on how the data storage is set up there are substantial differences in speed, flexibility, relevance and cost. Therefore, a right balance between complexity and pragmatism has to be found in order to reach the desired results. To find out more about the state-of-the-art data management read our blog.

Companies that want to enable their leaders through data analytics need to collect and manage substantial amount of data from internal and external sources.

Internal data can be derived from everything within a company’s network. This can refer to digital content such as SAP Success Factors, Taleo, e-mails or Skype messages. Another example is offline data derived from movement recordings in the office space or statistics of the cafeteria offering. Online content is easy to scan for buzz words, frequency and timing. The offline content needs to be counted and quantified. Of course, administrative HR data such as age, gender is used to enhance analytics.

External data examples among others are LinkedIn and other social media platforms, job aggregation websites, employer branding and salary review websites such as glassdoor.com. In addition to that data can be bought from hiring agencies, analytics agencies and other market research institutions.

It might seem to be an easy step, but it requires alignment of multiple stakeholders and subject matter experts from the business side. It is important to define beforehand what questions to ask. For instance, a leader might want to know what the triggers and combination are for employees that leave the company. By selecting and preparing the right data sets and attributes it will be possible to answer such question easily and quickly.

Conclusion

In this blog, we have listed just a few examples of analytics that help leaders to excel in many areas. However, despite having a limitless amount of possibilities leaders should start small and gradually develop HR data insights competency. It cannot be done by data scientists and IT specialists alone. This requires substantial support and guidance of the business leaders. Those who realize it quickly will be able to secure long-lasting data-driven competitive advantage.

If you now see the need to become a more insights-driven leader and want to know more about your journey to get there? Feel free to contact our experts from Capgemini’s Insights-Driven Leadership practice.

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About the authors:

Joana Koester and Denis Tintman are management consultants at Capgemini Invent in the Insights Driven Enterprise Team. They accompany global clients in digital transformation and insights-driven innovation with a special focus on data analytics and leadership.

PS: Want to get in touch? Check out the profile from Joana and Denis on LinkedIn.