The Latest Trends in Artificial Intelligence for Data Analytics

How do you spot new business opportunities, achieve improved operational efficiency or conduct more effective marketing campaigns? To make smart business decisions, you need information about the economic environment and your target group. That’s why data analytics should be a crucial element of your company. In this article, we are going to talk about how the analysis of data helps in business and how it can be tweaked with Artificial Intelligence.

The Power of Data

We live in a dynamic data-driven world where knowledge is the way to achieve every goal you set for your business, and data is your shortest and most straightforward way to gain it. Moreover, we’re pretty sure your company gathers and processes more data than you’ve already imagined for instance:

• Website data that can be accessed via Google Analytics tells you a lot about your customers, their interests and behaviors. Furthermore, the same source shows you which one of your marketing channels is the most effective one.
• Social media data provides you with even more information concerning your customers’ interests and opinions about your products or services. Financial data provides you with vital information on your company’s performance and results. Market data gives you priceless insight into market trends, fluctuations and even potential business opportunities.

The role of data analytics

As you know data analytics guides you through the intricacies of big data in your company. Given that your data is cleaned and organized, you’re on an open course to using it in a beneficial and effective way. In most instances, companies all over the world concentrate on data in the following areas:

• Customer service
• Digital marketing campaigns
• Finances, investment and cash flow
• Human resources and workforce management
• Operational costs and procedures

It all comes to one thing–better results. They can revolve around making more money, saving time or resources, improving management, enhancing customer service, etc. When it comes to AI in data analytics, your every action has to be purposeful.

Two approaches to data analysis

Data analytics can look differently, depending on your objectives and the data you process. In most instances, though, it’s all about analyzing appropriate data sources and looking for the information necessary to make a specific decision or to achieve a particular goal.

Data analytics can be done internally, with a team of data scientists and adequate software. It can also be outsourced to an external Data and Analytics Consulting Services just like Addepto. Both these solutions have pros and cons. If you decide to go with an in-house model, you have to be prepared for some serious investments and, frequently, adjustments as well. In the outsourcing model, it’s far easier. You just go to a selected company, discuss what needs to be done and you let these experts do their job.

AI-Enhanced Techniques for Data Collection and Preparation

However, data analytics is the practice that should be implemented in every company, it’s not a universal solution. There are at least a couple of different types of data analysis, and most of them use Artificial Intelligence in order to improve the outcome. You can decide which of them would be the most suitable depending on your goal and current situation.

Leveraging AI for Predictive and Prescriptive Analytics

When you want to create a solid and profitable business, you can’t just focus on the present, but constantly study the market and plan your next steps. That’s why predictive and prescriptive AI in data analytics should be your foundation when planning your strategy.

Predictive analytics is by far the most complex version of data analytics. It’s widely used to predict future outcomes or scenarios. Companies mostly use predictive analytics to assess future sales levels, potential crises and other critical changes that can adversely affect the entire organization.

Although, when you identify any potential problems, opportunities or risks it all comes to finding the best scenario by prescriptive analysis. If you’re facing a complex business decision, there are many variables to consider, and you want to find the best solution to your problem or challenge, this service will provide you with the necessary input to mitigate potential options.

Looking back with Descriptive and Diagnostic Analytics

Your goal is to understand what happens within your company and market? Descriptive analytics is based on spotting and analyzing trends, customer behavior and other elements that have a significant influence on your company.

You can also go for Diagnostic Analysis to understand why something happened in the past. Opt for this solution if you’ve experienced some crisis or drop in the past and don’t fully understand why it happened. Surely, this service will provide you with vital insight concerning your past activity.

As you can see, data analysis can be used to analyze different critical areas of your business. However, due to the complex and voluminous nature of big data, exploring it without the support coming from Artificial Intelligence can be troublesome, to say the least. In many instances, it’s straight-up impossible. That’s why today, the vast majority of data analytics is fueled by AI-based algorithms.

How to use AI in Data Analytics?

Artificial Intelligence is all about doing jobs that in the past were reserved for humans. Thanks to the ability to learn and process gigabytes of data per second, it improves the outcomes you receive from data analytics by making it more effective, accurate, and faster.

When it comes to AI in data analytics, we have to mention three crucial technologies:

• Machine Learning: It’s a technology that allows intelligent algorithms to learn from data, especially in order to uncover trends and patterns and to improve the way these systems work.
• Natural Language Processing (NLP): This technology supports understanding human language, primarily in the textual form, in order to obtain useful insights from different forms of texts, messages and documents.
• Business Intelligence: It’s all about making accurate and informed decisions, primarily when it comes to managing a whole company.

How AI Improves Data Quality and Validation Processes?

However, let’s be more specific. Now, we will consider several aspects of how AI improves data analysis:

Natural Language Processing supports text analysis

Thanks to Natural Language Processing, you’re no longer limited to getting quantifiable data like statistics and numbers but also answers to qualitative questions. Companies use Natural Language Processing in something called sentiment analysis to get more information on what their customers really think about the brand and products. Thanks to the deep understanding algorithms scan pieces of content to provide you with accurate information.

Although, someone might say: It’s nothing difficult, but large companies get thousands of comments each day. Analyzing all of them manually would be troublesome. Instead, these algorithms can do that instantly and automatically, providing you with real-time data concerning your company’s image online. Moreover, Sentiment Analysis algorithms use not just Natural Language Processing but Machine Learning as well, primarily in order to improve the quality of their assessments.

Text classification

This facet of AI in data analytics is similar to text analysis. However, the goal is slightly different. Earlier, we tried to estimate the intent behind each piece of content. Here, we focus on the topic or theme behind each piece of content. Text classification algorithms can quickly and automatically assign various texts and messages received by your company to specific groups and categories.

Suppose you’ve just received information about a new comment which claims that configuration of your platform was more difficult than it was expected. What should you do with such a message? As humans, we can assess that quite quickly, especially when we know the company’s policy in such a situation. But again, if you had to analyze hundreds of such messages daily, it would be close to impossible in the long run. That’s why you need AI in data analytics. In the same way, text classification algorithms can quickly transfer each message and each text to a specified department or team in your company so that nothing slips unnoticed. When it comes to your image and crisis management, it’s vital facilitation of work!

Text extraction

Thanks to technologies like Optical Character Recognition (OCR), you can extract specific texts and keywords from your documents. This technology comes in handy when you have printed documents that you want to digitize.

With OCR, you can scan them and almost instantly convert them to editable digital files. Text extraction comes in handy also when you need to find specific keywords or pieces of data in your documents.

The benefits of AI in Data Analytics

As we’ve already mentioned, predictive analytics is based almost entirely on Artificial Intelligence. It helps you make more informed decisions and plan the company’s development with confidence. Predictive analytics makes a case for itself also concerning various scenarios and outcomes, especially in the areas such as:

• HR: selecting the best candidates, assessing employee performance and planning the most effective training programs
• Finances: estimate future sales levels and the financial condition of your company
• Logistics: route optimization, reduction of transportation costs, drivers’ performance evaluation
• Customer service: analyzing and predicting Customer Lifetime Value (CLV) and Customer Churn Levels

AI in decision making

At this point, we ought to mention something called Decision Support Systems (DSS). These are digital information systems that are designed to help you in preparing data for decision-making purposes. It often uses predictive analytics to help you estimate the consequences of various decisions, primarily on the general business level.

Additionally, Predictive Analytics plays a critical part in Business Intelligence as well by managing and developing companies in a more informed and accurate way. This includes estimating the results of specific decisions. Thanks to Big Data analytics supported with Machine Learning, you can use this technology to uncover patterns and deviations in data and use this knowledge for your company’s good.

Chatbots

Chatbots are frequently backed with Machine Learning features, meaning they learn themselves from each conversation. You can analyze conversations your chatbot had with customers to improve your customer service and get more thorough knowledge of what your customers are usually interested in, what problems they have, and what questions they need to be answered.

With this input, you can adjust your other customer-service-related processes to enhance the overall quality of customer service in your organization.

Automated work

Thanks to Machine Learning, AI-powered data analytics systems can work automatically, uncover hidden trends and patterns in data and analyze information coming to your company in real time.

With this automation, your managers and employees can work more effectively and make informed decisions, primarily because they have instant access to all the information they need at a specific time. Thanks to AI in data analytics, it becomes more accessible, even for less tech-savvy users. Of course, you can automate more aspects of data analytics:

• Reporting and data visualization: you can present data to your clients and stakeholders in a legible and straightforward way.
• Data collection: you can automate this process and thus, save a lot of time.
• Cleaning data: modern AI-powered algorithms are capable of cleaning your data all on their own.

Read more: Real-Time Big Data Analytics

Transformative AI Applications in Data Analysis

When it comes to AI in data analytics, you have two options. You can opt for designing an entirely new system that’s fully adjusted to your needs and requirements. Or you can use a ready-made tool that will support the vast majority of tasks you set for it. If you decide to go with the second option, we invite you to take a look at these tools:

• Tableau
• Qlik Sense
• Sisense
• PowerBI
• SAS BI
• Google Data Studio

Summary

Analytics, especially of data, has become an essential component for any business aiming to spot new opportunities, improve operational efficiency, and enhance marketing campaigns. In today’s data-driven world, leveraging the vast amount of information available is crucial to making smart decisions.

In Addepto, we realized that data AI in data analytics can be difficult for many companies, especially if so far you’ve never used either of these technologies. That’s why our role is to help clients make the most of the data they possess.

We will gladly help you even if you’re not fully certain what can be achieved with your data and what for. The Addepto’s experts will guide you through the entire process, from selecting the best solution to implementing it and maintaining it for future purposes. If you’d like to find out more about our work, just go to the contact section and feel free to drop us a line. We are at your service!

FAQ: The Latest Trends for AI in Data Analytics

What is the role of AI in data analysis?

AI in data analysis helps uncover patterns, trends and insights from large datasets faster and more accurately than traditional methods. AI tools automate tasks like data cleaning, visualization and predictive modeling, allowing data analysts to focus on strategic decision-making.

How can Machine Learning enhance analytics?

Machine Learning enhances analytics by enabling systems to learn from data without explicit programming. With Machine Learning algorithms, data analysts can build predictive models that adapt over time, improving accuracy and efficiency.

How can data analysts use AI tools to improve efficiency?

Data analysts can use AI in data analytics to automate repetitive tasks, like data preprocessing or anomaly detection. Machine Learning algorithms also assist in creating models that predict outcomes or classify data, saving time and reducing human error.

Can Natural Language Processing be used in data research?

Yes, Natural Language Processing (NLP) is widely used in data research to analyze text-based data, extract insights, and identify sentiment. For example, NLP can process customer feedback or social media data to help understand public opinion.

Why should businesses use AI in data analysis?

Businesses use AI in data analysis to gain a competitive edge. AI tools provide faster, more accurate insights, automate mundane tasks, and allow Machine Learning algorithms to predict trends. This empowers businesses to make data-driven decisions effectively.

The article is an updated version of the publication from 1 Sep 2021.