Innovation today is not a luxury but a necessity and a differentiator. Particularly in tech native companies, the need to solve new problems is directly tied to the organisation’s growth. It’s a rapidly changing landscape and with new technology emerging routinely, there is no choice but to constantly innovate. The problems are even more pronounced when it comes to data science and AI teams. Running a data team is like running an R&D lab at scale and with strict commitments on revenue numbers and timelines. If the right culture of innovation is not created, it could result in employee burnout and vicious cycle of attrition resulting in poor trust on AI teams to deliver. 

Ingredients of culture of innovation

Culture is not a piece of code that can be written on multiple walls and screensavers and simply set to execution. Culture can be defined by leadership but it needs to be deep rooted, in that everyone in the team or the organisation stands by and fosters it. The guardians of culture cannot be only at the top, they have to be carefully adopted and protected by every member in the team. Innovation culture can be broken into several components—talent; strong and courageous leadership; long-term thinking; willingness to experiment; and overall openness in the system.

Talent 

At the core of any organisation—big or small—are its people. Talent is the core strategic asset that companies are building. For teams focused on innovation, acquiring and retaining the right talent is increasingly important. Talent is the core building block and lays a strong foundation. Talent can be divided into two parts: acquiring talent and growing them.

Acquiring Talent 

In data context, great talent does not translate to expertise in a field. It is much more than a fitting resume to a well-written job description. The recruitment strategy plays an important role—where they hire from, the panel as well as the rounds are crucial in accumulating talent. Few pointers to keep in mind while acquiring talent for innovation at scale are:

• Keep the technical bar very high but relevant to the job role.
• Problem solving rounds that involve existing business context.
• Measure cultural fit only in specific rounds.
• Hiring manager should spend more time with the candidates.
• You may not fill a role than fill it with an unsuitable candidate.
• Spend a lot of time on leadership roles in people-related interviews—how they manage conflicts, how they lay people off, and how they retain talent.

Growing Talent

Building Talent is not restricted to hiring. It’s about how you grow people and how you retain them with zeal and enthusiasm. ‘Life at work’ is a key factor. This is different from work–life balance. The more fulfilling people’s lives at work are, the more likely it is that innovations will happen. Since HR functions manage some of these expectations, it is the leader’s responsibility to improve the quality of life at work. Creating the right environment for people to thrive, providing the right projects or initiatives, strong mentorship and supporting the team in challenging times are directly attributable to good leadership practices. Few points to ponder that could help build a vibrant data culture: 

• Encouraging the team to roadmap solutions. Data Science cannot be a pure downstream taking requirements from ‘business’ or ‘product’—Hence, encouraging the data scientists to roadmap any solution they are building is important. They need to understand and think about business problems, from data instrumentation to measurement, and how the solution itself will evolve.
• Keep the bar high for end state solutions. Sometimes, the solutions require a change in the way not only the company works but also in the way that the industry works.
• A flexible organisation structure that can provide high flexibility for people to try out different areas within data based upon their interests. 
• Proactively working on career development for the team. Don’t delegate growth of team members to the HR body. As much as they can support, the growth plan should be worked between the manager and the employee—A technical manager plays a key role both in mentoring and in growing people’s career interests.
• Having regular brainstorming sessions, hackathons, and showcase events can greatly help.
• Engineering capability within the data team. This will enable the team to do full fledged prototypes without waiting for product or engineering counterparts to show a proof of concept or take the first solution to the market.

Strong and Courageous Leadership 

An enthusiastic top-of-the-talent candidate, when nurtured properly, can create state-of-the-art products and solutions at industry scale. Often one of the things that comes in the way of innovation and against taking big bets is fear of failures. Considering how leaders have to face the consequences of failing, they can end up playing safe. Even if they don’t stop the innovation, they may delay or slow down what is happening naturally in the team. One of the key traits of leadership is taking the big step and showing the courage for standing up against many odds. Founders and CEOs need to invest in more leaders who show the courage to support great ideas, even if the risk is higher than they are comfortable with. 

Long-term Thinking

When we state the vision of organisations, we want to improve the lives of many people. However, in practice, the vision often gets buried in weekly/quarterly/annual targets. Particularly, leaders need to sandbox and insulate long-term initiatives from such aberrations. Allocating separate bandwidth for these initiatives and getting tight executive sponsorships are key. Another way to keep the lights on for long-term projects during short-term crises is to keep developing iteratively and providing adequate transparency on the initiative. Co-creating with your executive sponsor with regular cadences on the progress will also help defend the initiative during maelstroms in the organisation.

Willingness to Experimentation

Experimentation, more than tooling, is a culture. This culture needs to be present across the board not only within data. The willingness to try out different strategies and do different experiments should be pervasive. This is possibly a hallmark of data-driven organisations. An organisation that is willing to experiment would look at the experimentation infrastructure as a foundational block of their architecture. 

Overall Openness in the system

Innovation can come from any quarters. It is not top down. A leader’s role is to keep discussions open. Arguments should be welcome as a first principle to progress. However, an organisation that is building for the long term and to solve true purpose understands the compromise of speed. If voices and opinions are constricted, innovation too will suffer. People and ideas need to be heard and nurtured. They should be enabled, encouraged and motivated to speak. If there are contrary opinions, leaders should not shy away from addressing them. When concerns are addressed openly, it generally improves honesty and accountability in the system.

Conclusion

Having an innovative culture is healthy for the growth of the organisation. There are always forces that pull you into short term initiatives. But companies need a push forward to steer towards the long term initiatives as well. Innovation acts as a force multiplier for any team and simultaneously maintains the enthusiasm, energy and excitement of the overall team. Dimensions such as talent retention and overall openness within the data organisation are under the ambit of the data leader and can be totally influenced into a positive direction. 

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here