In the field of analytics, Artificial Intelligence (AI) is exploited to automate the analysis and interpretation of large amounts of data. This can be done using machine learning (ML) algorithms to identify patterns and trends. These algorithms can then be deployed to make predictions or recommendations based on the data.
What is Artificial Intelligence (AI)?
AI refers to the ability of a computer or machine to mimic or replicate human-like intelligence and behaviour. This includes the ability to think, learn, and solve problems in ways similar to humans.
AI combines machine learning and deep learning techniques to produce models that have the ability to make intelligent decisions using vast volumes of data.
Applications of AI
- Gaming: Artificial intelligence has found prominence in the gaming sector. It is used to create smart non-playable characters (NPCs) that mimic human behaviour to interact with human players in a realistic way.
- Transportation: Self-driving cars have been a rage for the past few years with some scientists predicting that they could take over the industry in the next few decades. In conjunction with the vehicle’s camera, radar, cloud services, GPS, and control signals, artificial intelligence can be used to autonomously operate a vehicle.
- Marketing: AI-powered chatbots, natural language processing, generation, and understanding can analyse a user’s language and respond in a human-like manner.
- Healthcare: To construct powerful machines that recognise tumours and infections. AI can assist in diagnosing chronic conditions by aggregating lab data with historical patient information. New drugs can also be discovered using AI.
- Education: With the help of artificial intelligence, academic content like video lectures, conferences, and textbook guides can be digitised. In fact, educators can produce different interfaces, such as animations and learning content to satisfy the diverse needs of students of different grades. It can produce audio and video summaries and lesson plans to create a rich learning experience.
- Agriculture: Computer vision, robotics, and machine learning applications are being used to locate nutrient deficiencies and pests in the soil. AI is also being used to identify where weeds are growing on farms and to help farmers harvest crops at a higher volume and faster pace than human labourers.
- Robotics: This is the most well-known use of artificial intelligence. AI-powered robots are pre-programmed to make instant real-time adjustments to avoid obstructions in movement and to perform tasks as humans would.
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What is data analytics?
Data analytics is a technical discipline that encompasses multiple diverse techniques and processes for deriving actionable insights from historical data. It helps to predict outcomes, evaluate performance, and design or optimise systems for better business decision-making.
The goal is to gain insight from large amounts of data and make better-informed decisions.
Many industries and sectors can benefit from data analytics skills. The key actions involve cleaning, organising, transforming data, and applying statistical algorithms to make decision-making easier.
This discipline helps organisations gain valuable information about their customers, markets, and operations by analysing the data collected, thus making more informed decisions.
Is artificial intelligence part of data analytics?
The terms artificial intelligence and data science (and, by extension, data analytics) are often used interchangeably, but they are not the same. AI, as defined above, refers to the ability of a computer or machine to mimic or replicate human-like intelligence and behaviour.
On the other hand, data analytics is the process of analysing and understanding large amounts of data in order to gain insights and make better decisions. In a way, though, the two might depend on each other.
AI is often used in analytics nowadays to process data faster and come up with more accurate and precise information and insights. It might also be used to improve the decision-making of machines.
Machine learning, an important aspect of AI, involves training machines on large volumes of data to identify patterns and abstract them into generalised rules. These can then be used to make better decisions.
So, to answer the question above, yes, artificial intelligence is increasingly becoming a major part of analytics. AI-powered software for analytics does most work automatically with minimal to no human effort required.
This is particularly attractive to analysts and companies because it reduces the time spent on analysis and manpower costs as well. Understanding the role of AI in data-driven analytics can provide valuable insights into how this technology is shaping the business landscape.
Because traditional software requires constant human input, engineers have to physically edit the code whenever a new process or function needs to be added. This is in absolute contrast to AI software, which requires only initial human input to learn.
As a result, machine learning software is able to “learn” by “feeding on” data, then exploit this information to spot patterns and extract knowledge from it.
How to use AI in data analytics – Examples
AI-based predictions and business insights
Beyond quantitative data analysis, AI-based analysis can also handle qualitative data for diagnostic, predictive, and prescriptive analytics.
Photo by Markus Spiske
Humans cannot easily extract the in-depth insights and patterns that AI can find in large datasets. Additionally, AI finds these insights and patterns at scale and speed. AI tools can also provide recommendations based on the analysed data.
AI text analysis
Natural language processing (NLP), a subfield of machine learning, enables machines to “understand” human communication. Text analytics is an NLP technology that breaks down text (from documents, social media posts, etc.) in order to discover insights.
Sentiment analysis
The phrase “opinion mining”, another word for sentiment analysis, describes the process of analysing text for polarity of opinion (positive, negative, or neutral). It can process vast amounts of text data from nearly any source to understand the writer’s feelings and emotions.
In practice, one can use sentiment analysis on customer surveys to categorise responses by the degree of urgency or dissatisfaction and prioritise the most critical issues.
It can also be used on customer service tickets and emails to detect urgency or discontent.
Using NLP, sentiment analysis detects the tone of an author’s text (positive, negative, or neutral). Text can be processed in enormous quantities by any source and is, therefore, able to examine the writer’s emotions and feelings in great detail.
AI bots
Chatbots are frequently utilised as a part of clientele assistance since they can answer easy-to-understand questions with information that has been gathered through ML. For example, whether certain keywords are less helpful than others in aiding customers.
Beyond this, bots continue to demonstrate advanced data analysis capabilities because they provide responses quickly/.
Forecast demand
Because of its ability to predict demand based on analytics, AI can aggregate data to forecast future product sales by looking at stock levels, seasonal patterns, and previous shopping behaviour, among other things.
Predicting marketing outcomes
AI can help you plan for success and anticipate how events will turn out because you can analyse data from hundreds of sources and predict outcomes. It may also determine what products to develop and what marketing strategies can be used to target consumers.
Unify customer data across platforms
Since data these days is collected from first, second and third parties, AI proves very useful in consolidating and analysing the data from multiple platforms and touchpoints. For marketers, it enables them to create unified customer profiles with accurate data. Data can also be quickly ingested from different online platforms and analysed to provide integrated results.
AI data analytics tools
With the growth in popularity of AI in data analytics, a plethora of tools have been developed, and the options are many. Here are a few of the good ones.
Adobe Analytics
Adobe Analytics is a business intelligence tool that enables users to aggregate, match, and analyse data from online and offline sources, with in-depth analysis, reporting, and predictive intelligence capabilities to build better customer experiences.
Tableau
Tableau, as a data analytics tools, allows data interaction without the need for coding. The easy-to-use platform and a dashboard that can help create reports in real-time and share with teams, makes it super efficient.
Google Analytics
As a free web-based service offered by Google, Google Analytics provides a medium to access detailed statistics and knowledge about website traffic and user behaviour. It enables website owners and advertisers to track and analyse the performance of their websites. They can analyse data sets like the number of visitors, their actions on the website, and the sources of their traffic.
Microsoft Power BI
This business intelligence platform offers easy visualisation and the ability to create quick business insights. Microsoft BI’s super fast UI comes with the ability to import data from anywhere and create reports as needed. It also offers multiple integrations like with Excel.
Conclusion
AI can assist in analytics by performing many of the labour-intensive tasks associated with studying and evaluating data.
With data, organisations may be able to identify important trends and patterns and make better-informed decisions. It can also be utilised to identify and forecast customer behaviour, detect fraud or errors in financial information, and identify trends and patterns in sales data.
Overall, the use of AI in data analytics is an increasingly important area of study and application. It has the potential to help organisations make better use of their data to improve their operations and drive business growth.
Reach out to us at Accord for more information on how to get started with your data analytics and Artificial Intelligence journey through our training courses in Singapore.
Reviewed by
Areas of expertise: Training and consulting in technology, strategy, analytics, business management, and learning and development.
Awards: ‘Innovation for Impact Award’ 2016-17 | ‘Associate Excellence Award’ 2018-19 | ‘Innovation for Impact Award’ 2020-21 by CSC.
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