Surviving AI’s impact on the data workspace

To prepare for the future, data professionals must develop a deep understanding of artificial intelligence and its potential influence on their work.

Let’s kick off by stating the obvious − we can’t see what the future holds; however, through the use of historical patterns, knowledge, experience and informed intuition, we can take an educated guess at what is likely to happen and plan for it.

This is particularly true in the world of data and analytics, where the rate of technological change can be exponential. Today, the question on the lips of most data professionals must be: how will artificial intelligence (AI) impact the workplace and the future of our profession?

AI has the potential to revolutionise the way data is collected, analysed and utilised. Through the ability to automate routine tasks, identify patterns and generate insights that might be missed by human analysts, AI promises to make the work of data professionals more efficient, effective and valuable.

These capabilities are applicable to data analytics in any vertical sector. For example, the Institute of Marketing Management notes AI tools are changing the way marketers analyse data. With the help of machine learning algorithms, these tools can analyse large amounts of data and provide insights into customer behaviour, their preferences and trends.

This allows marketers to make data-driven decisions and adjust their marketing strategies accordingly. For instance, if a particular trend in customer behaviour is identified, marketers can adjust their marketing campaigns to take advantage of it.

According to McKinsey, AI is proving to be a double-edged sword. While this can be said of most new technologies, both sides of the AI blade are far sharper, and neither is well understood.

On the positive side, these technologies are starting to improve our lives in myriad ways, from simplifying our shopping, to enhancing our healthcare experiences.

However, McKinsey says the consequences that arise directly from the development of AI solutions − from either their inadvertent or intentional misapplication, or from the mishandling of the data inputs that fuel them − can be dire.

McKinsey highlights the much-discussed potential for widespread job losses in some industries due to AI-driven workplace automation and note there are also second-order effects, such as the atrophy of skills; for example, the diagnostic skills of medical professionals as AI systems grow in importance.

Speaking from the perspective of the data professional, it’s important to highlight the fact that AI has the potential to replace some of the work that these skilled people do, which can lead to job losses, but even more importantly, it can result in a decline in the quality of analysis.

To prepare for the future, data professionals need to develop a deep understanding of AI and its potential influence on their work. In the analytics arena, automation has been identified as one of the biggest areas that AI will impact, with new capabilities and technologies already being used to mechanise routine and repetitive tasks, such as data cleansing and pre-processing.

This is likely to free up time for data professionals to focus on higher-value tasks, such as analysis and strategic planning. However, it’s also true that AI has the potential to replace some of the work data professionals do. For example, machine learning algorithms can be used to develop models and generate insights, negating the need for human input. This could lead to job losses in areas like data entry, model development and reporting.

Another possible impact of AI on the data and analytics workplace is likely to be in the area of analysis. AI technologies have the potential to identify patterns in data that might be missed by human analysts.

For example, machine learning algorithms can be used to identify correlations between variables that might not be immediately obvious to human analysts. This in turn can generate new insights and identify new opportunities for growth and innovation.

However, it’s also true that increased reliance on AI technologies could lead to a decline in the quality of data analysis. If data professionals become too reliant on it, they may lose the ability to think creatively and develop new ideas.

To prepare for the future of AI in the data and analytics workplace, data professionals need to develop a range of skills and competencies. These include technical skills, such as machine learning, data visualisation and programming.

However, they also need to develop non-technical skills, such as data storytelling, technology translation and guidance. Data professionals who can effectively communicate data insights to stakeholders, translate technical concepts for non-technical stakeholders, and guide stakeholders through the process of adopting and integrating AI technologies are likely to be highly valued in an AI-infused world.

In conclusion, the impact of AI on the data and analytics workplace is uncertain, but data professionals will only be prepared if they have acquired a deep understanding of it and further ensure said technologies are adopted in an ethical and effective manner.

Only in this way will they be able to ensure they remain valuable contributors to their organisations in an AI-infused world.

Written by: Adriaan Rossouw, iOCO Qlik, Business Unit Executive

Originally featured here