How to Use AI Analytics to Speed Up your Workflow

Artificial Intelligence
Dec 2, 2023
How to Use AI Analytics to Speed Up your Workflow

Artificial intelligence is pouring into every area of our lives and saving time and money for businesses. From using ChatGPT to help with creating social media posts to summarizing complex information in plain language, AI is making lives easier.

It’s no different with business intelligence. Companies are now using AI analytics for data analysis and making more data-driven decisions.

But can AI really be used for data analytics and how? And is it safe to do so? These are some of the questions we’ll answer today.

What is AI analytics?

AI analytics is the process of using language learning models to analyze business data.

With traditional business intelligence tools, the process looks a bit like this:

  • collecting the data from various data sources
  • cleaning the data
  • transforming the data
  • data modeling
  • data visualization
  • interpretation of the data from the visualization

AI analytics can speed up any of the stages of of the process and make them simpler. Today’s AI tools can create visualizations from clean and prepared data. They can also draw conclusions, either from clean and structured data or from the visualizations.

This saves considerable time and money data scientists as machine learning algorithms can not only interpret data but also choose the best way to visualize it to get the most useful insights.

To achieve this, there are multiple artificial intelligence processes and tactics you can rely on:

Machine learning: use predictive analytics to make accurate predictions on future outcomes based on current data.

Generative AI: asking AI tools to provide data insights in plain English. Instead of bothering your data analysts, simply ask a question such as “what is our top-selling product in the last quarter?” and AI provides the results.

The end goal of AI analytics is to get better insights from raw data and use those insights to make smarter business decisions. 

AI analytics can help you spot trends, monitor data in real time, find new business opportunities from existing data sets and more. You may be thinking - sure, but you can do this with traditional analytics tools too.

This is true, but the addition of AI helps you finish the job more quickly and accurately.

Key benefits of using artificial intelligence for data analytics

If you’re already using an analytics platform, you may be wondering if there is a business use case for introducing AI in your workflows. No matter how simple or advanced your BI setup may be, you can benefit from AI.

Faster decision-making

Getting from raw data to insights can take a bit of time. Especially if you’re dealing with large volumes of data, a small team of people and you need insights fast.

AI allows you to get easier access to relevant data for all users in a company. Instead of data analysts, anyone with a basic knowledge of business analytics can ask an AI tool to tell them what a certain data point could mean.

Faster insights create shorter feedback loops and allow the entire company to move more quickly. Eventually, this makes for a competitive advantage.

Automated data visualization and faster data analytics

AI brings the kind of automation that allows anyone to create dashboards and visualizations. You no longer have to wonder if a scatter plot or a donut chart is the better way to visualize your data. AI makes that decision for you, based on massive amounts of training data.

Scalability and flexibility

If your data grows, you don’t have to rethink your entire data stack. With AI, you can explore data at scale, whether it’s 100 or 100,000 rows of data. Also, you don’t need amazing data science skills to get started finding insights in your data.

Competitive advantage

Compared to organizations that need to fire up traditional BI tools, you’ll be leaner and faster. These tools and methods are slower in general and because of the many processes involved, also less accurate. 

Here is how.

  • Manual data integration: Traditional BI tools typically require manual integration of data from various sources. This is time-consuming and makes you prone to errors. In contrast, AI-driven tools can automate data integration, ensuring faster and more reliable consolidation of diverse data sets.
  • Limited data processing capabilities: Traditional BI tools often struggle with processing large volumes of data or real-time data analysis. AI-driven tools, on the other hand, are designed to handle big data efficiently, providing quicker and more accurate insights.
  • Static reporting: Traditional BI tools generally offer static reports that don't adapt to changing data or business needs. AI-driven analytics tools, however, employ adaptive algorithms that can learn from new data, providing dynamic, tailored insights that are more accurate and relevant to current business scenarios.

Risks of AI analytics

While AI analytics can show you key metrics in seconds and make anyone a data scientist, it’s not all sunshine and roses. Let’s look at some of the risks involved.

Primarily, system bias. AI removes the time-consuming processes from the equation, but those processes are done by humans.

AI analytics tools need humans to provide context and question the insights that the tool provides. As good as AI can be when it comes to statistical power, it is not all-powerful and it does not have the creative thinking of a data scientist or the overall business context for your company.

This means that even the best AI analytics tools require a level of oversight and a healthy dose of skepticism.

AI analytics use cases

There are many different ways to use an AI analytics solution to make better decisions for your business. Whether you’re in real estate, supply chain or any other industry, here are some examples of how natural language processing and AI can help with BI. You can use them to draw inspiration for your own use case.

For real estate: Predict market trends in property valuation

Feed your AI analytics tool real estate data from your designated area. In turn, it will show you predictions of market trends and give valuable insights on where the best places to invest are.

With AI data analytics, investors and property developers get more thorough insight than any Excel file with basic data could ever give them. As a result, they can make the right choice when it comes to investing in their portfolio. 

For supply chain: better inventory management

Machine learning models can take a look at what you currently have in stock, how it’s selling, and make predictive assumptions about the future. For example, if a certain item sells well during holiday time, this will show up in your data.

Over time, you’ll be able to see that certain items sell well at certain times, so you can put extra on order and improve your logistics

This allows you to optimize the stock in your warehouse, but not only that. You can also improve your product delivery and customer service, as you’ll know what items are popular and at which times.

For ecommerce: store personalization

Provide a superior customer experience to your online shoppers by using AI to analyze their behavior on your website. Take a look at things such as:

  • what pages they visited
  • what products they purchased
  • what items they searched for and left the page

Once the information is in one place, AI algorithms can create a more personalized experience catered to a specific website visitor.

AI analytics in retail

For example, it can suggest a product that a customer is likely to be interested in, based on what they previously did on their website.

When done right, this kind of store optimization can lead to increased purchases and better customer loyalty.

For marketing: customer and audience segmentation for purchases

Most websites today collect significant amounts of marketing data. And with new data coming in all the time, it can be hard to come to any kind of logical conclusion.

AI allows you to streamline your marketing operations by identifying groups of customers and visitors on your website. It can highlight groups with certain characteristics and behaviors that make up a significant chunk of your website visitor base. It can serve as an engagement calculator for various marketing channels and highlight groups with certain characteristics and behaviors that make up a significant chunk of your website visitor base.

This allows you to crate buyer personas based on actual data, rather than assumptions. You can uncover patterns and trends that might not be directly obvious through conventional methods such as purchase patterns, demographics data, and others.

For healthcare: drug discovery and development

While healthcare is a complex and highly regulated field, AI analytics can help here too. It can accelerate the process of discovering new drugs by analyzing complex biochemical data. The algorithm can then use forecasting to predict how compounds can react with certain biological targets.

This helps identify promising drug candidates more effectively and quickly, and reduces the time and costs of bringing a new drug into market. Moreover, any potential side effects can be discovered sooner.

For sustainability: energy management and waste reduction

Imagine taking a look at the energy usage in a certain area and being able to show all that data in an Excel spreadsheet. And then, imagine using AI to do the heavy lifting of discovering useful patterns, andbringing that data to life in visual charts.

AI algorithms can analyze these usage patterns to improve energy efficiency. For example, they can predict peak demand times and adjust energy distribution. That way, there is no shortage when the grid is under a high load.

Also, it can detect anomalies in energy use and spot malfunctions quickly before they spiral out of control.

The role of AI in data visualization

Data visualization is one of the most important steps in the analytics process. When the right data is visualized in just the right way, key stakeholders can learn from it more quickly and make data-driven decisions.

data visualization in Luzmo

Easier said than done, though. Choosing which data to visualize and what kind of visualization to use is very difficult. It takes years of experience with BI to get this right. But AI can help.

Thanks to generative AI, any person without a PhD in data science can ask a question in plain English, and get a visualization that answers it as a result.

For example:

  • What is our top-selling product this year?
  • Which country has the website visitors that contribute the most revenue to our business?
  • What was our most successful marketing channel in terms of new conversions AND lifetime value?

This is a groundbreaking change as it lowers the barrier to entry in data analytics. It means that anyone with access to the right tools and data can investigate and visualize KPIs to reach valuable insights.

Getting started with AI analytics

Wondering if AI analytics works for your business processes? We are ready to dispel your doubts. Find out how AI and data analytics can work together by downloading our AI paper today. It’s completely free!

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