The first Industrial Revolution (IR) introduced primitive machines at factories and the famed steam engine. The second industrial revolution sped things up through the assembly line and electrification. The third one ushered in the era of the internet, bringing digital nuances to our lives.
Now, we are at the cusp of the fourth industrial revolution. IR 4.0 will be led by artificial intelligence and ably assisted by other meta-trends like robotics, Internet of Things (IoT) and advanced big data analytics. Just like its previous iterations, the latest IR will dramatically change the way we live and the way we work. In this article, we will zero in on how AI will change the way we work with data.
What is the current state of AI and Data Analytics?
Currently, the world of artificial intelligence is witnessing breakthroughs and advancements almost every day. The industry is nascent and ripe for disruption. Currently, large language models (like ChatGPT and Bard) are leading the way with applications in natural language understanding, content generation, and automation. Beyond text, generative models like DALL-E and Midjourney are assisting in creating synthetic data. Graph Neural Networks (GNNs) are also being deployed by companies for very specific use cases and specialised tasks, mostly in logistics and supply chain operations. Companies like OpenAI, Anthropic, Google, Snowflake and Databricks are leading the charge in this domain of AI.
What are the major trends in AI and Data Analytics?
Both leading companies and emerging startups are banking on data professionals to increasingly integrate AI into their workflow. They are focusing on building AI applications and interfaces that make this integration smoother and create use cases that improve the quality of work. There are specific trends that are expected to take shape and go mainstream:
Basic Task Automation
The most obvious place to start would be the automation of repetitive tasks. This is because AI is very good at executing tasks that are narrow in their scope and need little cognition. This includes tasks like:
Data cleaning and preprocessing: Identify and eliminate duplicates and outliers (if required) as well as standardise values like date, currency, height etc.
Data collection and integration: Scrape structured data from websites with the help of tools like Octaparse.
Dashboard creation: Tools like Tableau are working on capabilities to assist dashboard creation with an AI copilot.
These developments would ensure that qualified industry professionals, like those with a Masters in Business Analytics online can focus on tasks that are higher in difficulty and importance.
Generate Synthetic Data
Artificial Intelligence can also assist by creating synthetic data i.e. data which mimics the characteristics and trends of a specific data set. This is created artificially and has no linkage to real-world events. This can be used in multiple ways:
Testing and validating data model on generated data.
Creating edge case data for model stress testing.
Create data for simulation or training of both professionals and AI systems.
Create Instantaneous Insights
One of the main benefits of working with AI is its high speed and efficiency. AI is very good at scanning huge data sets and producing insights almost instantly. It is adept at finding patterns and suggesting actionable insights from them. It can do so by:
Streamlining data from multiple sources (like IoT, social media and website form submissions)
Interpret unstructured textual data (like customer reviews) and join the dots to produce actionable insights.
Rank or order these insights on the basis of their impact, feasibility and suitability in a particular context.
Execute Predictive Modelling
Currently, predictive modelling is based on historical data wherein patterns of the past are used to predict the future. However, advancements in AI have allowed data professionals to add other elements to the equation. Market trends and other external factors (like geopolitical uncertainties and economic conditions) can now also be considered while creating models predicting the future. This would reduce the margin of error in the prediction and help create a more realistic and practical projection.
Domain Specificity
Data professionals are usually required to drill down on the foundational KPIs (key performance indicators) and measurable metrics of a particular industry while working on a project. For instance, if an analyst is dealing with the dataset of a restaurant, he will need to be aware of metrics like table turnover rate, sale per seat hour and net promoter score. Using AI can speed up the process of demystifying the industry jargon and having a good understanding of the basics.
AI is set to rewrite the rulebook of data analytics. From taking over mundane tasks, to adding a creative arrow to an analyst’s arsenal, unlocking instant insights, and boosting a professional’s knowledge base—AI is set to not only improve the efficiency of existing workflow but also add new elements to the dimensions like synthetic data and advanced predictive modelling. Businesses should look for further disruption in this space to stay on the innovation curve, or risk falling behind.
