As new technologies become ubiquitous in the modern workplace and data plays an increasingly prominent role in decision-making, human employees need the skills to work collaboratively with their new digital colleagues.
The fourth industrial revolution is underway. Data is becoming everyone’s most precious currency, thanks to an exponential increase in the use of emerging technologies such as artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA).
According to McKinsey, 50% of businesses have already deployed AI for at least one business function. Product and service development, service-operations optimization, and marketing and sales optimization using customer-service analytics are the most prevalent.
It’s a similar story with machine learning. The 2020 Kaggle Machine Learning and Data Science Survey found 45 per cent of businesses have used machine learning technologies in some way.
Perhaps most interestingly, Gartner predicts that worldwide robotic process automation (RPA) software investment will reach $1.89 billion this year, up 19.5 per cent from last year.
The division of digital labor
As these technologies become ubiquitous in the modern workplace and data plays an increasingly prominent role in decision-making, human employees need the skills to work collaboratively with their new digital colleagues.
For example, soft skills such as communication and emotional intelligence have been increasingly popular for improving collaboration across the human workforce. However, our digital colleagues are less likely to be affected by our body language or mood. In fact, when digital workers fail to complete a process or deliver inaccurate findings, they are also unlikely to recognise their human co-workers’ frustration. Yet it will be humans who are relied upon to set digital workers on the right path.
WEF research has found that by 2025, the time spent on current tasks at work by humans and machines will be equal. Yet automating routine tasks will also allow for the creation of an entirely new field of jobs based on human creativity. While automation could displace 85 million jobs by 2025, around 97 million new roles are set to emerge that are better adapted to the new division of labor between humans, machines, and algorithms.
To achieve this collaboration, data literacy will be essential for understanding how digital workers come to their decisions and how they can be pointed in the right direction.
Humans need to be fluent in data
The ability for human workers to extrapolate tangible information from data to drive actionable insights is still very much a work in progress. According to a Qlik study conducted in partnership with Accenture, The Human Impact of Data Literacy, only 32% of business executives feel they can create measurable value from their data. Furthermore, only 27% feel their data analytics initiatives deliver actionable insights.
This has ramifications for people’s inability or refusal to trust and rely on digital workers. Of course, this will change over time as more of the workforce develops a level of familiarity with AI, ML, and RPA. However, many companies need this new working partnership to blossom in line with their new investments.
As these digital workers become widespread across the workplace, business leaders need to arm their employees with the skills to succeed in an increasingly digital and data-driven workplace. If they don’t, they not only risk losing talent to organizations making more significant investments in employee upskilling, but they stand to undermine the future productivity, performance, and competitiveness of their business.
To drive data fluency in the enterprise, proactive and forward-thinking organizations are matching their AI, ML and RPA programs with data literacy education initiatives. Only by aligning upskilling with automation is it possible to achieve organization-wide data literacy. Below are some approaches that can help significantly:
- Identify employees that are already data literate and position them as leaders in raising overall organizational data literacy.
- Identify the departments of the highest importance and conduct data literacy assessments to understand current literacy and any gaps.
- Position data and analytics leaders to set the tone and educate other staff on the AI/RPA initiatives.
- Run some proof-of-concept workshops that highlight to staff the specific use cases that are most relevant to your organization
- Focus on the literacy gaps identified in workshops and upskill the relevant employees.
For more information on data literacy initiatives your organization can implement, take a look at Qlik’s Data Literacy training program and resources.