Across all sectors, the amount of data produced is now staggering; retail, financial services and industry have seen the potential of data and the benefits to be derived from it, and it is now affecting people-focused departments such as HR. The arrival of a new category of employees within companies; data professionals, has fundamentally changed the way the HR professionals work and the value they can deliver to their employers. We are already starting to see how forward-thinking HR departments are implementing data engineers within their teams.
These engineers are the curators of company data, preparing it, standardise it, "cleaning" it and making it usable for analysis. Using data as a decision support tool will become the norm across all departments and for all types of business. This drives the need for data engineers with specialisms in talent, leading to a recurring problem of recruitment and retention.
Lack of training and acceleration of transformation
The lack of data engineers working on talent is due to the fact that until around three years ago, data did not occupy such a place within companies, and especially with HR. New hires in the talent space, freshly graduated, have not received specific training to cope with data processing, which requires mastering new solutions and new languages. But today the amount of data our workforces are generating is causing management to call for data-driven recruitment and training, and to move past traditional spreadsheet reporting to using analytics tools, far exceeding the technical capabilities of regular HR professionals who have traditionally been tasked with qualitative requests. We now see all "business" functions in companies wanting to understand and consume data about their employees.
While changes are being made in the education system to make up for this shortage, particularly in the post-baccalaureate streams, more is needed to address this shortage. It would seem that everyone has been caught unawares by the speed of technological change overall, and analytics adoption within HR specifically. Analytics platforms, advanced reporting capabilities, augmented analytics and even more recently the rise of Machine Learning and Predictive Analytics were cutting edge in software development just a few years ago, and have far exceeded the expected skills needed to work in HR. But this is what is now being demanded within the sector. All these phenomena have led HR into a new space: becoming the go-to reporting function within all business enterprises. The value that HR data holds far exceeds the basic headcount and cost analysis traditional company reporting would use to measure human capital. Coupling HR data with finance, procurement, facilities and sales data, for example, is driving the ability for businesses to re-evaluate how they make decisions. This centralised and ‘clean’ data, delivered through straightforward self-service analytic tools, is putting human capital based decisioning in the hands of managers in real time.
What should HR look for in a data engineer?
This real need for data engineers is causing companies to ask what skills are needed to excel within this specialism. The role requires not only technical knowledge, but also sector-specific knowledge of how to apply this to commercial and human factors. This function requires specific skills such as the ability to have an overview of the company's activities and employee data, and to link the two in line with the company mission. Indeed, when a data engineer looks at data, they do not process it for a single use but for several; they must therefore be able to have an overall view, to know where this data can be applied and how to optimise its use. They must also question the origin of the data, its reliability, and its ability to be interpreted in several ways. These are all essential questions in preparing the data to give it real value, all within the confines of strict privacy laws and governance – like the EU GDPR legislation for example.
How to recruit data engineers and retain them in the company?
When recruiting, particular care must also be taken with the typology of the positions offered and the way in which they are qualified. Indeed, the lack of standardisation in terms of role naming can sometimes lead to confusion in a large and varied data ecosystem. There is therefore a real need to standardise, classify and clarify the role of the data engineer during recruitment. Once the right people have been recruited, it is important to set up a dynamic to stimulate and retain these engineers. This can be done in harmony with systems that have already proved their worth in terms of strengthening the involvement and commitment of employees: issuing constant challenges, providing the necessary means to accomplish tasks and valuing people.
Another essential point for retaining this talent in HR specifically is to develop soft skills. The ability to communicate, collaborate and express oneself in public, for example, are among the skills that are rarely taught in initial technical data training courses. They are essential in the business world, especially for technical professions that work in talent roles and require collaboration with other business departments. Being able to communicate effectively around technical concepts is essential, both to understand the needs of management, and to explain to them the results of the analysis being performed and solutions envisaged.
Continuous training in behavioural skills is also a means of motivating and retaining employees, especially data engineers, because developing human and relational qualities is a differentiating factor and gives even more value to their profile.
Data engineers are indispensable in companies, but data skills are also important across the whole company. Data literacy, the ability to read, analyse and work with data, is more than ever a skill to be developed, to ensure that employees, regardless of their level in the business, are able to get the most out of the data they use.
Tom Ricks, Senior Director of Culture and Talent Systems and People Analytics, Qlik