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AI in Data Analytics_ 9 Trends Every SaaS Leader Must Know

AI in Data Analytics: 9 Trends Every SaaS Leader Must Know

Data analysis is making sense of everything, everywhere, all at once. 

With millions of data available, organizations succeed when they transform any data into valuable insights and make business-critical decisions. Further, the looming threat of recession calls for a need to justify and reduce expenditures. 

This is where Artificial Intelligence (AI) comes in. Combined with data analytics, AI can be a powerful tool in every SaaS business’s arsenal. With markets in flux, executive intuition can be less reliable. Data-driven insights empower organizations to build resilience, drive innovation, and improve cost savings. 

Through this blog, we will explore the multiple benefits of using AI in data analytics, trends and predictions of 2023, and growth strategies leaders can adopt to stay relevant and succeed.

Leveraging AI to Improve Decision Making 

Data has become an essential part of a business. Analyzing large datasets efficiently and accurately allows you to answer business-critical questions. It also helps to align organizational goals and improve multiple aspects of a business.

Take for example, Amazon. The e-commerce platform uses AI to recommend products based on their purchase and browsing history, improving sales and customer satisfaction. In another instance, utility providers can get better visibility about customer lifestyles and energy usage that can be used to make personalized recommendations and optimize their consumption.

By utilizing AI in data analytics, organizations can reap multiple benefits. Here are some cutting-edge technologies you can leverage to improve data-driven decision-making.

1. Predictive analytics 

AI can help analyze historical data to predict future outcomes, which can be used to derive insights. This enables businesses to identify trends and new opportunities and leverage them for making proactive decisions.  

For instance, collecting information about customer behavior and market analysis can help sales teams. With this information, they can create quarterly and annual forecasts. Additionally, it can help them to align their sales goals.

2. Natural Language Processing (NLP)

A subfield of Artificial Intelligence, NLP studies the interactions between computer systems and human languages. It processes and interprets human speech (voice or text) to understand user intentions. 

An example will be extracting insights from customer feedback and support tickets to understand user sentiment and improve customer experience.

3. Machine Learning

Organizations can automate various data processes using AI, such as data cleaning, and efficiently analyze vast amounts of data. For instance, businesses can track CPU usage and network traffic to understand the utilization of tools and improve operational performance.

4. Data visualization

Making sense of data represented in rows and columns of tables is difficult for non-technical teams. AI can help create interactive visualizations to analyze complex data and provide insights quickly. This helps improve collaboration between technical and non-technical teams to make better decisions.

Trends and Predictions for 2023 

AI is a rapidly growing field with numerous applications. This decade alone has seen some robust use cases that have redefined how businesses leverage technology. This includes automation, chatbots, and virtual reality.

In 2023, AI will increasingly be utilized in the data analytics sector, and we can expect more breakthroughs in the industry. Let’s take a closer look at trends and predictions that can help organizations prepare for the future and stay ahead of the curve. 

1. Augmented analytics

Businesses will increasingly use augmented analytics to help analysts and users to prepare tasks faster and efficiently perform data analysis. It will also help business leaders and executives derive value from data without needing technical skills and expertise.

2. Edge computing

Edge computing is all about processing data at the edge of the network. Through this approach, we collect and analyze data closer to the source of data, a sensor or a network switch, instead of waiting for data sent to the central store. 

This reduces latency and enables organizations to enhance scalability and decentralize data. Minimizing the bandwidth required for data collection also results in reducing overall expenses. Other benefits like real-time data analysis and improved security will prompt businesses to use edge computing in different ways this year. 

3. Data sharing and collaboration

Businesses need to share data and gain insights to achieve better collaboration and business outcomes. With the growth of big data, organizations are increasingly looking to break down data silos and ensure better communication. 

Organizations will focus more on creating well-defined data-sharing policies and procedures. They will also start using AI-powered tools to regulate data security and privacy while sharing and accessing data. 

4. Data fabric

Data fabric empowers organizations to have a holistic view of data across different systems and platforms, including both structured and unstructured data. This enables them to manage data processes effectively and reduce time and cost related to data management.

In 2023, businesses will increasingly adopt data fabric architectures to manage large volumes of data and integrate with existing systems for better outcomes and insights. 

5. Data management

Adding to the above section, organizations are finding ways to effectively manage their data and extract information relevant to their business. This includes data quality, data security, data governance, and data integration. 

Going ahead, organizations will invest more in processes and technologies that help manage data and improve productivity.

6. AI models and algorithms

By 2023, we expect businesses to develop and employ AI models and algorithms that increase accuracy of data management and analysis. There will also be a greater focus on making AI models more explainable and transparent. This can help better understand how decisions are made and remove any related bias.

7. Data science and AI systems

As technology advances, we can witness more sophisticated AI systems and models that can analyze huge amounts of data and provide more accurate recommendations and predictions. AI-powered automation will also help businesses to streamline their data processes and reduce time and resources associated.

8. Data products

As organizations try to make data-driven decisions and gain a competitive advantage, AI-powered products will become more prevalent. This includes predictive analytics and intelligent automation tools that help identify and analyze critical data. 

9. Natural Language Processing (NLP)

As discussed in the above section, NLP enables organizations to make sense of human speech and understand their intent. Organizations will incorporate NLP technologies into data analytics applications enabling them to interact with data intuitively and naturally. Extracting insights from customer interactions can be a quick way to ensure 

The Business Impact of AI and Data Analytics 

By leveraging the power of AI and data analytics, organizations can improve the way they work and achieve better outcomes. Here are some ways: 

Data-driven culture

Organizations can analyze data and extract insights that can be used for the decision-making process, thereby fostering a data-driven culture. It also helps improve operations and achieve business goals.

Competitive advantage

Using AI in data analytics, you can analyze market trends, identify growth opportunities, and develop new products that will help differentiate from competitors. 

Improve efficiency

Automating repetitive and time-consuming tasks will reduce the time and resources required to finish them. This ensures that professionals and business leaders can focus on strategizing and executing value-added tasks that requires attention. 

Talent acquisition

AI-driven selection during the hiring process is a good way to tackle time-consuming tasks responsible by human resource department. With AI-powered tools in place, organizations can offer attractive workplace for data science and analytics professionals.

Improved customer experience

Analyzing customer data and user behavior, businesses can have a better picture of their preferences and needs. This information can be used to create personalized experiences and ensure customer royalty.


AI and data analytics are revolutionizing the way businesses work and make decisions. We can expect to see more widespread adoption of AI systems and technologies, enabling organizations to gain deeper insights and improve decision-making. 

Organizations will invest in integrating NLP, augmented analytics, and edge computing technologies into data analytics. More sophisticated AI models will also become part of data analysis and processes.

To realize the full potential of AI and data analytics, organizations must consider the ethical implications of new technologies. They should hire more data scientists and analysts and provide training to users on leveraging AI-powered tools.

With the right tools and talent, you can make the most out of data. 

BI Connector is one such powerful tool that helps organizations to analyze and visualize Oracle data. This enables them to have a holistic view of data in interactive dashboards. We help you connect data from OBIEE, OTBI, OAC/OAS, and Oracle Fusion Analytics apps to Power BI and Tableau. This helps improve overall efficiency, time, and cost savings.


  • AI in data analytics can help organizations transform any data into valuable insights and make informed decisions.
  • To improve data-driven decision-making, businesses can leverage numerous technologies like predictive analytics, edge computing natural language processing, and machine learning.
  • Organizations will increasingly adopt data fabric architectures and data management systems that will help improve the existing data analysis processes.
  • There will also be a greater focus on creating data sharing and governance rules that ensure transparency and explainability of AI systems.
  • Businesses can leverage AI to reap multiple benefits including, improved efficiency, customer experience, data-driven culture, and competitive advantage.

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