How Data Analytics Can Drive A Better Talent Retention Strategy
Shared Services need to build up analytics skill setsAdd bookmark
Shared Services Making Data a Winning Factor
For the past three years the headlines have been full of nothing but automation and, indeed, RPA has proved itself a game-changer in driving more efficient processes and productivity. However, we may yet find that RPA is nothing more than an enabler for more intelligent business services.
The true game changer, as we are discovering, promises to be “data”.
Many organizations today are waking up to the opportunities presented by more effective data mining, data management, and data analytics. The resulting insights are driving entirely different decisions that not just save time and costs – but promote a competitive advantage. By comparison, process efficiencies gained through automation, while significant, are of far lesser impact.
Data scientist roles are growing within SSOs, combining functional expertise with technology training to offer second-to-none insights. Data analytics are helping businesses stem the loss of valuable human resources, identify the most appropriate resumés out of thousands, and identify high potential talent during the recruitment stage. Data analytics, in other words, is enabling HR services to recruit top talent and prevent it from walking out the door.
However, While Finance operations tend to be fairly advanced in data analytics, HR is still at the early stages. One thing holding it back is the lack of talent and experience with business analytics, explains Narendran VR, Head of Human Resources, Technology, Global Business Services at Standard Chartered Bank APAC center. "We have plenty of employee data, and we also have strong analytics capabilities, but what we have not yet developed sufficiently is the inference competency," he explains. "That is something we are actively working on, so that our analysts can cross reference and better connect the dots to provide better insight."
As the custodians of Standard Chartered Bank’s HR data, it's up to Narendran’s team to develop more meaningful policies based on this data. Having access to incredibly granular data is driving completely different conversations with the business, he explains. "It shifts us from order-takers to a new role, by way of driving a cultural shift. Our GBS is completely integrated into the business, not separate, so we see ourselves as ‘one bank’ and provide solutions on site, right where they are needed.”
The enormous volumes of employee data created are forcing the GBS team to consider three critical questions:
– What data is being created?
– What data is being communicated?
– Who gets access to the data?
A robust governance model determines three levels of data, namely: Platinum, Gold, and Bronze, that determine classification and access. “Platinum level data is most sensitive and therefore has highly limited access; Gold is predominantly performance based; and Bronze includes learning and training-based information.
"What is currently lagging is the HR analytics competency.”
The real value comes from leveraging all three, but traditionally the restrictions around Platinum and Gold prevented comprehensives analytics. “Automation changes this,” Narendran says.
The value of data derives from Access; Reporting; and Analytics. “The architecture governing Access is robust,” Narendran explains, “as is our reporting capability. What is currently lagging is the HR analytics competency.”
You have to not just build up your skills base, he says, but also learn to ask the right questions of the data.
Many executives spearheading data analytics as a competitive advantage wear two hats: One as a data scientist, and the other as a functional expert and lead. A growing trend is to develop groups of "citizen data scientists" based on cross-functional peers that combine domain knowledge with data science skills.
These new skill sets are driving a number of benefits:
1. Predicting and Limiting Employee Churn
A key concern for businesses is to identify employees that might be susceptible to leaving and prevent them from doing so. Most organizations have excellent data on their employees and, in collaboration with IT, are identifying key parameters that identify those susceptible to churn. This is important not only because it can accurately predict turnover – but also, and crucially, so that preventative action can be taken.
In analysing this data, teams are learning a lot about the differences between those who leave and those who stay and can do something about it – for example by reassessing roles and taking action to prevent premature departures. This is especially important for organizations that have a leading role within their location or their industry. Other businesses may be keen to poach their valuable talent, as pioneers present a primary target for any followers. As key talent incubators, these organizations have to take talent retention very seriously.
2. Smarter Recruiting
Another example of data analytics improving performance is in recruitment. Neuro Linguistic Processing (NLP) can deliver real wins by producing a list of resumés that are more likely to be hired. This is important today, as free-format PDF resumés are a fact of life and many organizations receive thousands of graduate resumés alone, every year. NLP models can be trained on the basis of last year’s data, to identify key resumés with significant accuracy, and thereby cut down on recruitment cycle time. That is a significant benefit as it saves costs, and it gets the right candidates in the door faster.
3. Identifying High Potential Talent
An extension of the above strategy is helping organizations identify high potential individuals from a pool of candidates. While this lends itself most easily to the existing workforce it can also be applied to applications for potential intake, as well.
That means, not only identifying a good match for in terms of recruiting, but also recognizing high potential talent for the enterprise, and prioritizing these applicants. Again, there are dozens of parameters to assess to identify these high potentials.
4. Data Dashboards
Much of the relevant HR data can today be showcased via dashboards. These include specific metrics and KPIs as well as numerical data across a number of categories. It is similar to the data that Finance and Sales have already been accessing for a while, but HR is somewhat newer to. Accessible via tablets and mobile phones, senior executives can tap into these dashboards to manage their HR-related agendas more effectively, based on real time insights.
Summary: Insights via Technology
Technology lies at the root of emerging analytics competencies and most organizations are actively taking steps to train their employees to be proficient in data analytics solutions.
One challenge is that although the algorithms exist to format the data, the human element is still a critical part of the equation. It's not a matter of, suddenly, relying completely on the technology. It's about getting the balance right.