The first time I encountered the concept of organising business data was during an “Analysis and System Design” course at university in the mid-2000s. I was captivated by the idea of structuring data analytically and using it to generate insights that could aid organisations. Back then, the norm was to use custom tools developed by organisations, extensive spreadsheets with raw data, and scripts to process data into actionable insights. This was a common practice in the company where I had my initial experience.
The Rise of Data Analytics
In the early 2000s, data analytics was a groundbreaking and disruptive field. It allowed organisations to transform raw data into valuable information. However, with the rapid advancements in artificial intelligence (AI), the landscape of data analytics has changed dramatically. Today, AI is taking over many of the tasks that were once the domain of data analytics, raising the question: Has AI made data analytics obsolete?
The Challenges of Data Analytics
In data analytics projects, data scientists spend between 75% and 80% of their time cleaning, organising, and preparing data for analysis (Schroedeer, 2015). This process is not only time-consuming but also challenging, especially when bridging the gap between business users and developers. In my experience, the documents prepared by business users outlining their needs and analytics opportunities were often difficult to interpret and process. Every company has varying levels of data processing and development capabilities, and aligning these capabilities with business requirements is crucial. When there is a mismatch, there is an opportunity to either enhance the company’s capabilities or outsource the task to third parties.
The Advent of Artificial Intelligence
Artificial intelligence is not a new concept. The first neural network was created in 1950 by Marvin Minsky and Dean Edmonds at Harvard University, simulating 40 neurons (Dissanayake, 2021). Today, more than 80% of organisations see AI as a strategic opportunity, with almost 85% viewing it as a way to achieve competitive advantage (Enholm et al., 2021). In the early 2000s, terms like Business Intelligence, Business Analytics, or Big Data were rarely mentioned, but by the end of 2014, there was an exponential increase in their use (Bayrak, 2015). This rapid evolution suggests that AI could potentially eclipse data analytics in a similar timeframe.
AI’s Impact on Data Analytics
By 2010, $2.4 trillion was spent on software services in the business sector. Despite this investment, only 32% of technology projects were deemed successful (Dennis et al., 2012). The primary goal of these projects was to deliver value to the business, often favouring a working solution over a perfect one. This misalignment between solutions and company strategies is a common cause of project failure, applicable to both AI and non-AI projects.
AI Enhancements in Data Analytics
A decade ago, conventional data analytics dominated the field. The emergence of AI was expected to revolutionise various IT processes. For example, AI and machine learning could automate data preparation, unification, and cataloguing. Trained AI models could extract patterns and make predictions, while AI could also automate testing, bug detection, and code refactoring (Yao et al., 2018). By analysing historical data and current business priorities, AI could suggest actions to enhance company performance.
The Role of ChatGPT and Natural Language Processing
One significant advancement in AI is natural language processing, exemplified by ChatGPT, a large language model developed by OpenAI. ChatGPT’s ability to understand and generate human-like responses has significantly impacted the business world. Its implications include improved efficiency, cost reduction, and enhanced competitiveness, driving companies to explore how to leverage AI to deliver more value (Armanr et al., 2023).
Barriers to AI Adoption
Despite the potential benefits, AI adoption faces several hurdles. As of 2022, only 8% of companies in the EU used at least one AI technology. The size of the company correlates with AI adoption rates; larger companies are more likely to adopt AI. Common challenges include high development costs, lack of skilled staff, management and legal risks, and poor data management practices (Grünbichler et al., 2023).
Conclusion
The transition from traditional business intelligence to artificial intelligence presents significant opportunities for organisations. AI can automate many operational tasks, providing better value and efficiency. However, successful AI adoption requires a strong data culture within companies. By fostering this culture, businesses can fully leverage AI to achieve their strategic goals and remain competitive in a rapidly evolving technological landscape.
Sources
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Consulted on 22/06/2024
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Consulted on 22/06/2024
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Consulted on 22/06/2024
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Consulted on 22/06/2024
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