AI and Data Analytics: Unlocking the Power of Data

AI is starting to change how companies do business, including how organizations analyze data. Data analytics and AI is a powerful combination that opens opportunities to create better products and services, tap into new markets, and increase security. In this article, we’ll cover how companies can use AI in their data analytics, its benefits and risks, and what the future holds for AI for data analysis.   

What is AI in Data Analytics?

AI in data analytics is the use of artificial intelligence to accelerate the processing of data to gain more insights. AI for data analysis can sort through massive sets of data much faster than people can. AI can pick up trends and patterns humans may have missed, allowing AI programs to make more accurate predictions, uncover consumer insights, find connections between ideas, and streamline processes. 

Data analytics and AI have a complementary relationship. Data analytics is heavy with massive data sets, often unsorted, that are tedious to organize and difficult to understand. Data analysis is the exact type of job AI was built for: processing and organizing numbers at high speeds. The symbiosis allows machines to do the boring yet vital grunt work, leaving humans to focus on higher-level strategy. 

Companies can use AI for data analytics in numerous ways. For example, AI can track data for manufacturing companies to analyze inventory, predict which components will need to be restocked, and analyze workflows to find more efficient ways to put parts together. Finance companies can augment data analytics with AI to understand customers’ spending habits and identify crises like credit card fraud. Organizations can use AI to better understand data gathered from their website, gleaning insights about which products are most popular and how to streamline customer experiences from click to cart. The possibilities are endless when it comes to AI in data analytics.

Why is AI in Data Analytics Important? 

AI in data analytics is important because it compounds the benefits and reduces the risks of previous methods of data analysis. With AI, data analytics can dive deeper in some of the following ways:

Data visualization

One of AI’s strengths is finding and visualizing patterns and trends in data. Using AI to create graphs and charts, you can create relevant, simple visuals that communicate important takeaways. You don’t need to know any code to get value from the easy-to-digest visuals. 

Reducing costs

AI can cut costs for your company. By identifying inefficiencies, AI can streamline workflows, manage inventory more efficiently, reduce headcount needs by automating menial tasks, and use predictive maintenance to prevent more expensive equipment breakdowns. 

Improving products

Improve your company’s products by using AI and data analytics. Based on data analytics, AI can propose new product designs, identify new services that would be popular with your customer base, and troubleshoot issues customers may encounter when using or ordering products. 

Increasing security

Beefed-up cybersecurity and better data safety are major benefits of integrating AI with your data analytics. When working with large data sets, AI algorithms can encrypt and anonymize data in transactions. AI can analyze your current privacy policies and automatically classify data to ensure the right people access the right data. 

Speed up project completion

No need to spend a week cleaning a dataset; AI can clean the data in a few hours. AI can automate workflows, make permissions and data sharing easier, and summarize data insights quickly. 

Elevating customer service

Chatbots were just the beginning of using AI for better customer service. By analyzing data, AI can create more personalized profiles and suggestions for customers. AI can also suggest new products and services that would be popular among your target clients and anticipate issues that may arise before customers encounter any problems. 

The Components of AI in Data Analytics

AI in data analytics has many pieces, but there are three main components you need to know to get the most out of your data.

Machine Learning (ML)

Machine learning is a type of AI that uses statistical models to understand data and make predictions. After training models on large data sets, ML programs can work on batches of new data and process them with the same skills it learned on the training sets. The better the original data sets, the more accurate and precise ML can be as it processes other data. Each time, ML can find patterns hidden in the numbers and glean insights you may have missed. 

Deep Learning

Deep learning is a type of machine learning. It uses neural networks designed to imitate how the human brain works. Much like people, deep learning programs can identify images and sounds. Once trained to recognize certain visuals or sounds, neural networks can be shown a new set of data and recognize those same visuals and sounds again, allowing the program to make accurate predictions. 

Natural Language Processing (NLP)

NLP is a subset of AI that deals specifically with language and speech. Perhaps the most famous example of natural language processing is ChatGPT, although you’ve likely seen NLP examples in chatbots, search engines, and text messaging autocorrect and autocomplete features. NLP uses datasets to find patterns in the way humans speak and write and learns those patterns to respond naturally and predict what someone will say or search for next.  

How to use AI for Data Analysis

If you’re wondering where to start when it comes to using AI for data analysis, most companies begin by implementing AI in three main data processes: data collection, data cleaning, and data visualization. 

Data collection

To get your AI models started, you’ll need data to train them on. The more data you have, the better your model will understand the task you want it to do. AI can help with data collection by tracking and compiling certain types of data (sales numbers, website visits, competitor updates). 

Data cleaning

Both quantity and quality matter when it comes to data. AI can help with data cleaning so you have high-quality, relevant data. Traditional, rules-based approaches to cleaning data can be slow; AI can sort through data much faster and identify errors, eliminate duplicates, exclude outliers, and more to create a clean dataset.

Data visualization

AI data analytics can visualize data sets quickly into graphs, charts, and presentations. Because machines can summarize data and find connections and correlations much faster than humans can, the visuals are created faster too—and they’re more accurate, thanks to AI’s thorough processing. 

Enhancing Data-driven Decision-Making with AI

AI has many capabilities when combined with data analysis, but ultimately, all of its capabilities have the same purpose: to enhance data-driven decision-making. Companies can start using AI-based programs to gain a competitive advantage. 

AI can provide simple, straightforward recommendations and lists of pros and cons to make decision-making easier. Companies can use AI to derive data insights, generate reports, visualize trends, boost descriptive and diagnostic analyses, and hone predictive analytics. Companies can also harness the power of AI chatbots for data analysis and reporting. By gathering data on the interactions chatbots have with customers, companies can make better decisions: they can understand customer concerns, troubleshoot problems, stop issues before they happen, and offer more targeted services and products. It also gives businesses insights into how to create segmented and personalized offerings. 

As you’re integrating AI into your business decisions and workflows, here are some best practices: 

• Make a baseline. Before you implement AI into your business workflows, document your current workflows and productivity rates. That way, you can measure the difference AI makes and fine-tune how it’s implemented. 
• Know your business goal. AI can help gather a lot of information and offer suggestions, but it’s up to you to know what you want to do with all that information. 
• Share the data. AI is great at simplifying information and creating easy-to-understand visuals. By using AI in your data analysis, you can democratize data and increase data literacy among employees. Everyone gains more insights when you use AI to make business decisions.
• Document everything. Documenting your data helps employees track how models were created so they can make better models for the future. It’s also important to document where you got your data for ethical and regulatory reasons. As you document AI’s performance, you can use the data as evidence of how well your company is performing and where AI is making a difference for the company. 
• Keep an open mind. AI is constantly evolving, and you need to evolve with it. Keep an open mind to new strategies and techniques for analyzing data, especially if the data is giving you unexpected results or offering unconventional suggestions. 

Dangers of Using AI for Data Analysis

Some of the biggest concerns with using AI for data analysis are related to ethics. The data used for AI models may not have been gathered ethically, especially because there currently aren’t many regulations around collecting data. Other ethical dilemmas include bias; if there is misinformation, political motives, discriminatory leanings, or other biased issues in the data set that AI is trained on, then the AI model will continue to analyze future data sets with the same bias. This leads to dangerous perpetuations of prejudice, half-truths, and missing key components. 

Some other concerns lie in business risks. Using AI for data analysis has inherent cybersecurity risks because it’s another type of software you integrate into your business. There is always the potential for data breaches, privacy issues, and unauthorized access for companies and individuals. Ironically, AI can also help with some of these issues while increasing risk in some of the same security areas. 

Another business risk is simply a lack of transparency. If your data analysis AI isn’t transparent, you don’t know why the AI made the decisions it did, making it hard to understand the underlying logic and decipher confusing outputs. A lack of transparency also leads to a lack of control over the model, as employees may not understand how to adjust the model or adapt to changing circumstances. 

As companies use AI, they also may be contributing to wider concerns. Some other societal dangers are that AI can further enforce economic disparities, disproportionately benefit wealthy individuals and corporations, and exploit user data for manipulative purposes. 

The greater AI’s power, the greater its potential for misuse. While AI can clearly boost data analysis and has enormous benefits for understanding data, the benefits also come with risks. The danger of AI is inextricable from its advantages, so each company must weigh the pros and cons carefully and implement AI responsibly. 

The Future of AI and Data Analytics

AI is here to stay, and it’s already shaping the future of data analytics. The future of AI in data analytics will likely include more natural language processing and refined deep learning capabilities. Currently, AI data analytics is mostly used in finance and sales; however, as AI becomes more accessible, companies will probably start implementing AI-enhanced data analytics in more areas of the business, such as marketing, HR, production, IT, and R&D. Companies will be able to use AI-augmented data analytics to tap into new markets and create new tools that will power healthcare, manufacturing, education, and many other sectors. Companies can choose to embrace the new way of handling and processing data with AI, or they’ll fall behind.