Artificial Intelligence, or AI, is the term for a machine or a system that can mimic human intelligence in order to execute tasks that can improve themselves as additional information is collected. When used in data analytics, AI provides a set of technologies that can locate patterns and insights from within a large dataset, then use this information to offer predictions. Data from such places as automation platforms, content management systems, and Google Analytics can be collected and analyzed with AI software.
AI Analytics is a branch of business intelligence that uses software capable of behaviors commonly associated with humans, like reasoning and learning, and applies these behaviors to the data analysis process. By automating this process, AI is able to exhaustively test data to find hierarchies and highlight relationships between different points of data. The speed at which this can be performed is far quicker than any human could work.
As the amount of data being created each day continues to grow, the use of AI technologies in businesses is skyrocketing. It is estimated that the AI market’s annual growth rate will increase 33% between 2020 and 2027. What’s more, four out of five retail executives predict that their companies will use AI-powered intelligent automation by 2027.
Given this growing movement toward using automated technologies to handle data, it’s important to consider some of the main ways this technology is used, as well as the potential hurdles of incorporating it into your workplace.
Uses of Artificial Intelligence for Data Analytics
AI has a wide range of applications in the field of data analytics. Here are a few of its most common uses in 2021:
- Locating actionable insights. AI is a powerful tool for locating patterns and useful insights that people cannot always find within a dataset. The speed at which AI technology can locate these insights can rapidly accelerate the analytics process.
- Predicting outcomes. Hundreds of sources can be quickly analyzed by AI-powered systems in order to offer business predictions about viable outcomes and potentially successful courses of action.
- Improving efficiency. The insights gathered from AI-generated data can be used to weed out inefficiencies and create a system to provide fast, cost-effective results. By using machine learning algorithms to predict changes that may affect business systems, AI analytics enables companies to take quick, effective action with the least amount of disruption.
- Enhancing customer service. It’s possible for most companies to automate customer interactions using AI, which leads to better, more reliable service. Chatbots that use natural language processing are common in many businesses for fielding basic customer inquiries.
- Enabling cross-platform unity. AI can bring together data from a variety of sources, even those that are hard to track. Unifying data across platforms allows companies to combine customer data into a unified view.
- Automating reports. Natural Language Generation allows AI to automate the process of generating reports and ensures that the reports convey data in a way that is accessible to a non-technical audience.
As AI technology continues to become more mainstream, its many uses and applications are likely to increase as well.
Challenges of Using Artificial Intelligence for Data Analytics
Along with the many benefits of incorporating AI into the data analytics process, there are several drawbacks to using this technology. If you are considering implementing AI analytics into your workplace, here are a few hurdles to be aware of:
- Cost: It can be quite expensive to set up computers, AI-based machines, and the other tools necessary to perform AI analytics. Not only can setup be costly, but maintaining and repairing AI systems can cost thousands of dollars. In addition, AI-based software programs must be regularly updated to properly function in the ever-changing data environment.
- Security: AI programs sometimes have loopholes that can affect the safety of data.
- Accuracy problems: Even AI programs can make mistakes, especially if they are not properly programmed. Often, these errors aren’t caught in time. This is why it’s essential for humans to double-check the results to ensure accuracy.
- It’s not possible to fully replace humans. There is no disputing that machines are faster and more efficient than people. Yet, at least for now, it is not possible to provide a machine with human intelligence. This means that regardless of how much a machine learns, it is not able to fully replace a human, especially in terms of the creativity the human component can bring to the analytics search.
As is the case with any technology, implementing AI into the data analytics process comes with its own challenges and drawbacks. It’s important to consider factors like cost, security, and accuracy concerns before integrating AI software into your workplace.
Hands-On Data Analytics & Automation Classes
For those who want to learn more about how automation can help find insights and patterns in big data, Noble Desktop’s data science classes provide a great option. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses available for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.
Are you interested in finding out how Python can be used for automation? If so, Noble’s Python for Automation class is a great option. This six-hour course teaches students how to collect, store, and analyze web data using Python.
Students who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech.
For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows u