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DataIQ Leaders briefing – Developing the next generation of data leader

At a DataIQ Leaders roundtable in September 2021, members discussed the challenges they face when progressing the next generation of talent up the career ladder.
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In this rapidly evolving environment, defining a clear path to leadership can be a difficult process, particularly within organisations that have yet to make any significant strides on the journey toward becoming data-driven.

At a DataIQ Leaders roundtable in September 2021, members discussed the best methods for developing the next generation of data leaders.

The conversation focused on the necessity, and difficulty, of establishing career pathways and defining job roles, as well as the virtues of exporting data practitioners into roles throughout the wider business.

The HR hurdle

Because the data office is, for most organisations, a relatively new concept, it is essential that leaders continue to strive for proper recognition within existing HR frameworks.

A common complaint among data practitioners is that they have historically been lumped in alongside IT, business quantitative or business analyst functions by HR professionals who haven’t quite grasped the role of the data office or the types of positions within it.

A key task for any data leader is establishing autonomy for the data team. Doing so will ensure that job functions are properly recognised, allowing for benchmarking against job families and progression chains within existing HR frameworks.

This, in turn, will affect both the quality of recruitment into the business and progression up the career ladder, helping the business to attract, retain and nurture the next generation of talent.  

As one member from the oil and gas sector said: “Having data recognised so that you can hire people into the right roles and have them defined accordingly is one of my eternal background tasks. Get it right and career development happens naturally.”

What is a data scientist?

With the data office still in its infancy, practitioners are working to define roles and hierarchies within the industry as they continue to emerge and evolve. What is a data scientist? What does an analyst do? How are data engineers different? The answer to these questions can vary widely from organisation to organisation.

Roles within the data office often develop organically through the talent of individual practitioners or via reaction to emerging trends within the industry. Attaching a label and definition to a position, or asking HR to fit a position into a salary band, can be a difficult task.

The question is – beyond titling a LinkedIn job posting – is there a need to define roles at all? Two distinct camps were present at the roundtable.

One member from the pharmaceutical sector stated that their federated business model sees 30 to 40 data leaders embedded throughout the business, none of whom have a specific job definition. “Ultimately, we’re looking at the business applications of these roles rather than shoehorning a defined job role into an area of the business,” the member explained.

There is a concern that by creating rigid role definitions, leaders can inadvertently create artificial silos of expertise that prevent practitioners from developing the broad portfolio of skills and experience needed to move effectively up the career ladder.

Despite being a proponent of defining job roles, one member said: “We’ve had people that had a broad portfolio become earmarked to one primary job role that doesn’t fulfil them. We’ve also had many instances of people working on a wider portfolio than they’re recognised for.”

On the other hand, data leaders are increasingly identifying role definition as a useful tool for differentiating between high and low value job functions.

One member from a broadcasting organisation said: “If your data science team has done nothing but produce BI reports for the past month, clearly defining roles can make it easier to confidently say that that wasn’t what you assembled the team to do.”

In other words, clear categorisation can help leaders to identify the skills missing from the team, and indeed where the team is over-subscribed.

As the data office moves out of its embryonic phase role definitions will inevitably crystalise. The data leaders of today are tasked with ensuring that these definitions facilitate the development of the next generation of data leader instead of becoming a straitjacket.

The next generation of data business leader

As businesses strive toward the elusive goal of becoming truly data-driven, it is increasingly imperative that data leaders are business leaders in their own right. This dynamic is well known, but there is a deficit between the common practice of importing business personnel into data leadership and the far less common approach of exporting data professionals out into the wider organisation.

Exporting data professionals into the business is an attractive proposition for two key reasons. Firstly, having data-minded individuals for internal customers would transform the relationship between the data office and the broader business and drastically accelerate an organisation’s data literacy. In other words, it would push the business toward becoming data-driven.

Secondly, as the data office grows, so too will the number of personnel capable of progressing to leadership. By encouraging the exportation of data personnel into the business, more opportunities for promotion are made available to the upcoming generation of talent, thus heightening the chances of the business retaining an individual’s services as their career develops.

One member said: “Data is one of the only business functions that sees an organisation from front to back. We make and support process change and business improvement, meaning data professionals have the capability to go into COO-type roles, operational excellence roles and beyond.”

The flow of data practitioners into non-data business roles has been impeded by scepticism from the broader business as much as it has hesitance from the data team. A criticism often landed on data professionals is that they lack commercial awareness. Indeed, this approach has fuelled the common dynamic of importing personnel from outside of the data department into data leadership roles. “I’d like to see the parachuting of non-data people in to lead the data department stop over the next 10 years,” said one member. “The justification they always use is that data people don’t understand business – but we do.”

The task for data leaders is to ensure that the next generation is equipped with undeniable commercial literacy. This dynamic will be influenced as an organisation moves up the data maturity curve, as the line between data and commercial literacy becomes increasingly blurred.

Key takeaways

  • Overcome the HR hurdle. Work with HR to establish autonomy for the data office. Ensure that career paths for data practitioners fit sensibly into HR frameworks.
  • Be careful when defining roles. Data professionals are split on the best methods for defining job roles within the data office. Ensure that any definitions do not prevent talent from developing the broad set of experience needed to progress to data leadership.
  • The next generation of data leader will be a business leader. Ensure that career paths see data professionals develop the commercial awareness necessary to influence business strategy.

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