How does AI impact advanced analytics?

Defining Advanced Analytics

Advanced analytics is a set of statistical, computational and machine learning techniques and tools used to analyse complex and large data sets in order to discover patterns, identify opportunities or pain points, make predictions and generate insights that will drive future business decisions. Techniques used are beyond the capacities of traditional Business intelligence (BI), which is giving a snapshot of historical data.

Some components of the advanced analytics are:

• Predictive analytics: the ability to forecast future trends or outcomes
• Prescriptive analytics: points out the best action to take to optimise the outcome
• Descriptive analytics: identifying opportunities and pain points with historical data
• Data mining: Identifying patterns and relationships
• Machine learning: developing algorithms to make predictions based on data

Advanced analytics involves a much broader range of techniques. Some of them, like the two last points above, can be grouped into one technique which is Artificial intelligence (AI). 

The role of AI in advanced analytics

Artificial intelligence refers to the simulation of human intelligence in machines that are designed to think and act like humans. It can understand human language in a way that is remarkably similar to human understanding.

Artificial intelligence can be considered one of the key components of advanced analytics. Often it is considered to play THE key role. AI can perform tasks that are time-consuming and complex for humans to carry out, such as making predictions, identifying patterns and trends, as well as understanding natural language.

The most common aspects of AI in advanced analytics are:

• Predictive modelling: used to forecast likely future outcomes and behaviours by analysing patterns in current and historical data
• Machine learning: automates predictive analytics by creating trained algorithms to look for patterns, anomalies, and behaviour in data
• Natural language processing (NLP): analysing and understanding how computers and languages interact
• Data mining: reveals patterns and correlations within big datasets

With huge amounts of data being generated, businesses are searching for new ways to use it. While traditional business intelligence (BI) creates insights based on historical data, a need for advanced analytics that will drive decisions is becoming more and more common within organisations.

Benefits of AI in advanced analytics

AI has a very strong impact on advanced analytics, due to its ability to process and analyse large amounts of data in a very short time while running automated and highly efficient methods.

Here are some of the top reasons why AI is benefiting advanced analytics:

Benefit 1: Automatisation

Time-consuming tasks that analysts need to produce manually can be automated by using AI techniques. One benefit is reduced likelihood of human errors and bias. Another benefit is reducing the time and effort required for manual analysis and allowing data analysts to focus on more strategic tasks and storytelling.

Benefit 2: Increased efficiency

AI models can process and analyse large amounts of data much faster and more efficiently than humans. That way, results and insights can be presented and interpreted much faster compared to manual methods. Additionally, AI algorithms can process data in real time, allowing businesses to respond and adapt more quickly.

Benefit 3: Improved accuracy

AI algorithms can identify correlations or complex patterns within data that the human eye may have missed. This reduces the risk of human errors while preparing and analysing the data, which will lead to more accurate insights and predictions. They are also trained to continuously learn and adapt from the new data that is flowing to the model, leading to improved accuracy over time.

Benefit 4: Predictive modelling

Predictive models can offer actionable insights and predictions about future outcomes, making business decisions more informed and planned. By operating predictive models we can improve risk management by catching potential risks and mitigating them in advance, which will minimise potential losses. Additionally, predictive modelling can be created in a way to provide personalised recommendations for a specific group or behaviour, which can again, improve accuracy and lead to stronger predictions.

Challenges of AI in advanced analytics

There are multiple challenges that are associated with Artificial intelligence impacting advanced analytics. If properly monitored and addressed, the risk of running into it again will be reduced.

These are some of the common challenges in implementing AI in advanced analytics:

Challenge 1: Data quality

Data issues such as poor data collection, data corruption, data incompleteness, or data incompatibility can all affect a model's accuracy and in that way affect the result. It is important for businesses to ensure the right data that is being used to train the model and ensure that AI runs appropriately.

There are multiple ways to ensure data quality such as:

• Data preparation: ensuring that data is well prepared, free of errors, complete and consistent. Checking the formatting of data, for example, transforming numerical fields into categorical format when preferred
• Data profiling: investigating issues like duplication and lack of consistency, but also looking at statistical information like mid, avg, max, and outliers to check the distribution of the data
• Data balancing: ensuring that every category of the data population is represented by the same amount of data
• Data governance: managing and monitoring the quality of data to ensure that accuracy remains throughout the use

Challenge 2: Bias

Bias can, unconsciously or consciously, find its way into AI systems through their algorithms. One way is through data. If the training data contains bias, such as biased human decisions or historical and social inequalities, the AI model will also be biased towards specific groups. Another way is through humans. Algorithms are built and programmed by humans and their biases can be reflected in the model.

It is important for businesses to be aware of potential biases and act up front to reduce them or exclude them fully, which is also possible.

There are some ways we can mitigate biases:

• Selecting an unbiased data set
• Including diversity in the data set
• Increasing the awareness of biases in AI among the team
• Regular auditing of the models in search of bias

Challenge 3: Privacy

With rapid growth, privacy and security of the data became one of the biggest concerns in today’s world. Artificial intelligence absorbing large amounts of data is no exception. Data collected can include sensitive information such as personal, financial or health records. Storing this data raises the security risk of unauthorised access or hacking. AI models can be built without transparency and individuals not knowing or not understanding their data is being used and analysed.

It is crucial for businesses to be aware of potential security risks and take appropriate measures to ensure the privacy of the data such as:

• Implementing data security measures
• Receive consent for sharing and analysing the data from a third party or individuals
• Being transparent about the data use

Will AI replace data analysts?

It is unlikely that Artificial Intelligence will ever fully take over data analytics and replace the human aspect behind it, the data analysts. Even with all the benefits that AI is providing, like automating repetitive, time-consuming or highly inefficient tasks, it is still missing the capacity to contextualise and understand the data in a way that is the most valuable for making data-driven business decisions.

Artificial intelligence is still lacking the ability to base decision-making while understanding how the data fits into different industry environments, how the data results align with the market or vice versa and how the market factors impact decision making. Customer-oriented companies need to take customer dynamics and rapidly changing consumer trends into consideration. All of that is part of logical thinking and creating insights taking into account multiple factors.

On the other hand, AI will enable data analysts to dedicate a lot more time to their actual core role, ‘data analysing’. Currently, data analysts spend 80% of their time on data cleaning, preparation, and creating visual dashboards, and only 20% on actually looking at the final outcome and trying to extract the most valuable insights, and helping businesses to make better data-driven decisions. This is not highly efficient, data analysts need to be used more as a ‘tool’ for conducting strategic decision-making. They also need to act as a communication bridge from data to management by using their skills of storytelling.

Additionally, AI needs to be monitored by humans to ensure everything works properly.  Due to AI’s challenges, there is a need for human actions in preventing biased results and ensuring the efficiency and accuracy of the AI models.

Conclusion

Artificial intelligence is revolutionising the way businesses handle their data today. It is a powerful tool for advanced analytics, allowing businesses to automate routine tasks and improve accuracy which will ensure efficient and data-driven decision-making. With AI, businesses can gain a deeper understanding of future needs and market trends that can improve their growth. Even though AI carries some challenges, when properly managed and monitored, risks can be reduced to a minimum.