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Top 10 Analytics and Business Intelligence Trends For 2025

23 December, 2024
48 mins read
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Business intelligence trends for 2025 blog by RIB Software

Over the past decade, business intelligence has exploded. Data has become gigantic, and just like that, we all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly, advanced analytics isn’t only for analysts.

2024 was a banner year for the business intelligence industry. The trends we presented last year will continue to play out through 2025. However, the BI landscape is evolving, and the future of business intelligence is happening right now, with emerging trends to watch. In 2025, BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific needs?

Businesses are no longer wondering if visualizations improve analyses but what the best way to tell each data story is, especially with the help of modern BI dashboard software. 2025 will be the year of data security and discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and AI, where multiple industries will dive into analytics to benefit from the power of data, such as the case of construction business intelligence, which we will discuss in detail later in the post.

We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2025!

The Top Trends in Business Intelligence

Top 10 business intelligence trends for 2025
Business Intelligence Trends for 2025

1) Construction Analytics

As mentioned in the introduction, construction is one of the many industries that has jumped onto the BI train and benefited from advanced analytics. The construction case is interesting because this massive industry has always been recognized as reluctant to change, probably because it is one of the oldest industries in the world, with processes that have been tested repeatedly with positive results, at least until now. However, as the world becomes more digitally driven, the construction industry must keep up with changes. In 2025, we can expect an even bigger adoption of digital construction technologies, like AI, digital twins, sensors, and more, that will drive the industry forward.

Construction analytics can solve many challenges for companies in the building sector, including disparate data sources, outdated reporting, and decentralized teams and systems. These issues lead to costly errors that make projects longer and more expensive. By integrating data management practices and technologies into their workflows, construction companies can benefit from real-time data to make projects more time and cost-efficient from preconstruction planning to completion and handover.

This is possible with the support of professional BI software, which offers a centralized location to aggregate data from multiple sources and visualize it in interactive construction reports that anyone can access and understand. Plus, many processes once done manually, like a quantity takeoff or a bill of quantities, can now be automated, saving teams hours of work and significantly reducing the risks of manual errors.

That being said, the benefits are not purely operational. From a teamwork perspective, implementing analytics can also be a significant driver of productivity. It is no secret that collaboration and communication in construction projects have always been a challenge. With project stakeholders working from different locations, it is common for communication barriers to bring bigger issues. The proper construction analytics software can easily solve this issue by offering 24/7 access to real-time data in an online environment. This means anyone, regardless of device or location, can access and share the data with others to collaborate and communicate, ensuring everyone is working from a single source of truth.

However, adopting new software and practices is not easy. Leaders in the building industry will need to implement well-thought-out construction change management strategies to ensure the process goes smoothly. Involving all employees in the process can boost engagement and make it more effective.

2025 will be an exciting year for the building sector. As the importance of construction project management grows, industry leaders need to invest in technology that will boost teamwork and modernize processes that can no longer be carried out analogically. We look forward to seeing it happen!

2) Artificial Intelligence

Artificial intelligence (AI) is the science that aims to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of humans in movies (Skynet in Terminator, The Machines of Matrix, or the Master Control Program of Tron), AI is not yet on the verge of destroying us, despite the legitimate warnings of some reputed scientists and tech entrepreneurs.

Artificial Intelligence
Artificial Intelligence Trend

While we work on programs to avoid such inconvenience, AI and machine learning are revolutionizing how we interact with analytics and data management. In addition, increments in security measures must be taken into account. The fact is that it is and will affect our lives, whether we like it or not.

It is expected that AI will evolve into a more responsible and scalable technology in the coming year as organizations will require a lot more from AI-based systems. According to Gartner’s Data and Analytics research for 2021, with COVID-19 completely changing the business landscape, historical data will no longer be the primary driver of AI-based technologies. In change, these solutions will need to work with smaller datasets and more adaptive machine learning while also being compliant with new privacy regulations. This concept is known as ethical AI, and it aims to ensure that organizations use AI systems in a way that will not break the law. To this day, many organizations have faced legal issues for illegally collecting user data. The Facebook and Cambridge Analytica scandal is a perfect example of that.

In that sense, implementing systems and models to ensure the correct use of AI-related technologies will become even more important in the coming years. In fact, the US government released a blueprint for the “AI Bill of Rights,” presenting 5 principles that should guide the design, use, and deployment of automated systems “to protect the American public in the age of artificial intelligence.”

In response to this increasing need for AI accountability, Gartner presents AI TRiSM as one of the concepts that will help organizations ensure “AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection.” This cross-functional framework must be implemented from the earliest stages of system design and involve people from compliance, legal, IT, and analytics for a successful approach. By 2026, businesses that apply this framework to their AI models are expected to be 50% more successful in adoption, business goals, and user acceptance.

It can’t be denied that AI is still a topic of concern even today. The number of AI-based applications has become so big that many IT professionals don’t even know how to use or interpret them. This leaves the door open for breaches and financial losses that can significantly impact companies and customers alike. As a response, terms such as explainable AI (XAI) will be at the center of the conversation during 2025. XAI is an emerging field that aims to apply specific processes and methods to allow humans to understand the results and outputs created by machine learning and AI algorithms. The end goal of this field is to ensure trust and transparency with these systems to give humans control over them.

AI-based business analytics

When it comes to analytics, businesses are evolving from static, passive reports of things that have already happened to proactive analytics with dashboards that help them see what is happening at every second and give alerts when something is not how it should be. Solutions such as an AI algorithm based on the most advanced neural networks provide high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be immediately registered, and the system will notify the user.

Another feature that AI has on offer in BI solutions is the upscaled insights capability. It basically fully analyzes your dataset automatically without needing effort on your end. You choose the data source you want to analyze and the column/variable (for instance, revenue) the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

Time gain is also present in the form of AI assistants. Tools have started developing AI features that enable users to communicate with the software in plain language—the user types a question or request, and the AI generates the best possible answer. If you are interested in this, keep reading; we will dive into it in more detail later in the post with the natural language processing trend.

The demand for real-time online data analysis software is increasing, and the arrival of the IoT (Internet of Things) also brings countless amounts of data, promoting statistical analysis and management at the top of the priority list. However, businesses today want to go further, and Adaptive AI might be the answer. As stated by Gartner, Adaptive AI systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise.” These systems are so interesting for companies today because they can learn from behavioral patterns and adjust to real-world changes, making it easier to make fast and improved decisions.

In that same realm, Generative AI is another technology that has revolutionized the industry in the past few years and will continue to do so in 2025. It basically enables AI systems to generate text, images, audio, and other types of content based on human-generated input. A famous example of Generative AI is ChatGPT. In 2023, the tool revolutionized the industry with its ability to generate well-written texts based on a short input. However, as with many AI-related innovations, ChatGPT was quickly scrutinized because it could generate biases, copyright infringement, fake news, and more if not used ethically.

From a business perspective, using technologies like Adaptive and Generative AI has facilitated several processes, including data collection, cleaning, and analysis, which can be automated and tailored to the company’s needs. Risk management is another area in which these technologies thrive. Businesses can use Generative AI to predict any kind of fraud or attack, as well as generate risk simulations and test strategies in an imaginary scenario.

Agentic AI has also emerged as a valuable innovation for AI in business intelligence. It refers to systems and models that “can act autonomously to achieve goals without the need for constant human guidance.” This is a significant shift from existing rule-based AI models that cannot act independently. Agentic AI is programmed to achieve a goal and act on behalf of a human to achieve that goal. The technology harnesses the creative abilities of Generative AI but differs fundamentally as it does not focus on creating content but on making decisions. In 2025, Agentic AI is expected to improve human-machine collaboration due to its supercharged reasoning and, most of all, its advanced execution capabilities.

Overall, we cannot deny the value of AI and its continued development over the years. A recent study by IBM shows that companies are investing and adopting AI technologies to boost productivity and efficiency, with 63% of executives stating that their AI portfolio will financially impact their organization in the next two years. Still, the process is not without challenges, especially when training employees or scaling existing IT infrastructures to support AI with the risk of technical debt. On top of that, regulators and decision-makers must ensure ethical and secure measures are imposed when implementing these systems. Ultimately, it all comes back to security; we will discuss it in more detail in our next trend.

3) Data Security

As you have seen with our extensive AI trend, data and information security were hot topics in 2024 and will continue to be so in 2025. The implementation of privacy regulations such as the GDPR (General Data Protection Regulation) in the EU, the CCPA (California Consumer Privacy Act) in the USA, and the LGPD (General Personal Data Protection Law) in Brazil have established the foundation for data security and the management of customers’ personal information.

Moreover, the European Court of Justice’s overturn of the legal framework called Data Privacy Shield hasn’t made software companies’ lives much easier. The Shield enabled them to transfer data from the EU to the USA. Still, recent legal developments have invalidated the process, so companies with headquarters in the US don’t have the right to transfer EU data subjects.

Actually, a similar situation happened in 2015 when the EU and the USA had no legally valid agreements on this matter for a while. Many US-based (software) providers argue that they use European servers, and there is no data transfer to the US at all. However, from a legal perspective, even this solution is questionable, as, in theory, the US judiciary could force US-based businesses to reveal even data from EU-based servers. In essence, the information that is in the EU needs to stay in the EU. In practice, that means that EU-based businesses that use it in the current situation and US-based software vendors that store any kind of data for them are taking hazards as they operate in a legal grey area.

Taking all this into account, businesses have been forced to invest in security to stay compliant with the new regulations and also to protect themselves from cybercrime. In fact, global spending on information security is expected to reach $212 billion by 2025. This is not a surprise to the experts as, during 2020 and the beginning of COVID-19, companies of all sizes were forced to mutate from physical to digital, and, to accelerate the transformation, they relied on online services, leaving a gap for cybercriminals to attack. The advancements in AI have also left the door open to more sophisticated attacks that require even more refined levels of security.  According to the 2024 KPMG CEO Outlook Pulse survey, cybersecurity is among the top “risks to growth” topics for CEOs in the coming years. Even more concerning is that only 54% of CEOs say their organization is prepared for a cyberattack, with 37% of them thinking they are unsure their current security systems can keep up with AI advancements and trends. Gartner predicts that by 2027, 17% of cyber-attacks will involve Generative AI, leaving organizations in a difficult position. That said, software developers still see this situation as an opportunity and will continue developing robust security solutions to tackle these challenges.

Luckily, with the current situation, company boards recognize cybersecurity as an overall business risk more than an IT-related issue. According to Gartner’s Cybersecurity Predictions for 2023-2024, by 2026, 70% of boards will include one member with cybersecurity expertise.

Amongst the measures organizations are taking in the coming years, we will see an increase in adopting the Zero Trust framework. Zero Trust doesn’t describe a specific technology but an approach in which businesses remove the “implicit trust” from all computing infrastructure by verifying every stage of digital interaction from devices to users, regardless of location. This means every user who wants to interact with the company’s systems needs to be validated and verified. According to Gartner, by 2026, 10% of large enterprises will have a “comprehensible, mature and measurable” Zero Trust program in place, compared to the less than 1% that have one today. However, almost half of them might fail as a Zero Trust approach requires full organizational involvement and connection to business goals to succeed.

The concern in cybersecurity also presents a challenge for SaaS BI tools as they need to ensure they offer a secure product that clients will trust with their sensitive data. Like any other cloud BI solution, online business intelligence software is also subjected to security risks. Some of them include processing data quickly to provide real-time insights that might be subjected to regulatory compliance, vulnerabilities when moving data from user’s systems to the BI tool’s cloud, or when the tool provides access to data from multiple devices that may be unsafe and exposed to attacks. To prevent any of this from happening, BI software needs to have a clear focus on security.

Cybersecurity mesh architecture is one of the latest trends in business intelligence that helps SaaS BI solutions stay safe. Cybersecurity mesh is a composable and scalable security control that protects digital assets that reside in applications, in the cloud, IoT, and others. It seeks to establish a defined security perimeter around a person or a specific point with a more modular approach, enabling users to access data from their smartphones securely. One of Gartner’s cybersecurity predictions for 2021-2022 stated that by the end of 2024, organizations adopting cybersecurity mesh architecture will reduce the financial impact of security incidents by around 90%. Since data breaches have been regularly in the news, buzzing industries, and average users, the demand for security products and services is understandable.

With these security threats increasing, businesses must adopt an organizational approach to protect their data. That is why data governance will remain one of the hottest topics related to security in 2025. This concept refers to a set of processes, policies, and roles that ensure appropriate valuation, creation, consumption, and control of business data at a strategic, tactical, and operational level. It establishes roles and responsibilities regarding who can manipulate the data, in which situation, and with what tools and methods to ensure a secure and efficient data management process.

In the past years, tighter regulations, such as GDPR, have obligated organizations to ensure a secure environment for sensitive data, enhancing the need for stronger governance processes. As we mentioned earlier, companies of all sizes are exposed to attacks and breaches, leaving massive amounts of sensitive information from customers, suppliers, employees, and others exposed to misuse. In that sense, implementing a well-crafted governance plan will help organizations comply with government regulations while setting the perfect environment for using quality data and achieving their goals.

The recent implementation of NIS2 on October 18th, 2024, is another example of actions that are being taken by regulators to fight cybercrime. This framework aims to enhance the “security of network and information systems within the EU by requiring operators of critical infrastructure and essential services to implement appropriate security measures and report any incidents to the relevant authorities.”

In the coming years, we expect more frameworks like NIS2 to be implemented and companies investing more resources in protecting their data.

4) Synthetic Data

Next, in our overview of the top business intelligence trends, we have an interesting and relevant topic that relates to the two trends we already discussed, AI and security, which is synthetic data.

Gartner defines synthetic data as artificial data “generated by applying a sampling technique to real-world data or by creating simulation scenarios where models and processes interact to create completely new data not directly taken from the real world.” In other words, it is “fake” data created by Generative AI models that mimic real-world data but remove all the personal information from it.

To this date, there are two types of synthetic data: partial and full. On one hand, partial synthetic data only replaces a portion of the real data for privacy purposes, for example, contact details about customers. On the other hand, full synthetic data is where new data is generated from scratch, still sampling from existing data but with no actual real-world information. Synthetic data can take various forms, including text, numbers, images, videos, and more.

This innovative approach has many benefits. The main one is the capability to generate unlimited amounts of labeled data on demand without having to wait for it to be generated in reality. This massive amount of information is more cost-effective. It can also be used to train AI and ML models, allowing organizations of all sizes and resource levels to capitalize on the power of artificial intelligence and machine learning. In fact, Gartner predicted that “by 2030, synthetic data will completely overshadow real data in AI models.”

Synthetic data can also be used to complement real-world data and test different scenarios even if there is no evidence of that scenario in the real dataset, opening the doors for creativity and innovation that can drive success. Lastly, synthetic data is a great tool to protect sensitive information and stay compliant with privacy laws. For example, a medical researcher can generate synthetic information that shows the same statistically relevant information as real data but with fake patient names, addresses, etc.

That said, while there are many benefits, using artificially manufactured data does not come without challenges. These include quality control issues, a lack of technical expertise to generate and manage the data, and a lack of buy-in from stakeholders. Companies that want to adopt this approach successfully must be willing to invest in training and tools to make it truly effective and beneficial.

Even though synthetic data has recently gained visibility, many successful applications are happening across industries. For example, healthcare organizations use it to detect conditions, while financial entities use it to train fraudulent activity detection systems without exposing financial information. 

5) D&A Sustainability

Moving on with our list of the business intelligence market trends, we have data and analytics (D&A) sustainability. The topic is one of the most important ones we will discuss in this post, as climate change will remain a global concern in the coming years.

In recent years, businesses have started exploring sustainability, mostly as a marketing tactic to brand themselves as “conscious.” As the topic becomes increasingly important, with new regulations forcing organizations to report on their ESG initiatives, decision-makers have realized that sustainability also represents a big way to reduce operating costs and increase overall profitability and efficiency. That is where D&A sustainability comes into the picture.

Now that businesses of all sizes and across industries have realized the hidden potential of sustainability, we will start to see many using data and analytics to boost their strategies and maximize their efforts. By tracking important metrics like energy consumption, gas emissions, labor rights, supply chain performance, and others, organizations can extract valuable insights to guide their sustainability journey.

In 2025 and beyond, organizations can expect to use D&A sustainability to anticipate changes in demand and adjust their resource purchases and usage to be more financially intelligent. However, other factors will also come into play besides purely resource-related data. Production levels, sales volume, employee headcounts, and even weather data will help paint a more accurate picture to facilitate real-time decision-making.

We can also expect to see different tools emerge to help track sustainability data from a past, present, and future perspective, providing a big competitive advantage for companies that manage to adopt it correctly. That being said, ensuring all employees and relevant stakeholders are involved in the process is also necessary. Implementing training instances to engage employees with the process is a good way to start.

Linking ESG initiatives to business outcomes is a challenging task. As of today, sustainability analytics is valuable for three main reasons: the first one is to stay compliant with the law, the second one is to track the performance of ESG goals, and the third one is to uncover new opportunities to keep integrating sustainability into operations. Organizational leaders must take charge to ensure all these aspects are covered and supported with the best tools and technologies.

At RIB Software, we are committed to making the construction industry greener by providing our clients with the best solutions to track their carbon footprint with the help of our professional carbon estimating software, RIB CostX. The platform allows companies in the building industry to estimate the carbon emissions of different material choices thanks to a carbon database. This level of transparency ensures decisions are made with sustainability in mind, mitigating the project’s environmental impact. Carbon emissions can also be tracked during the project to extract improvement opportunities to ensure goals are met.

It is no secret that sustainability has transitioned from a buzzword to a mandatory practice in the business world. This growing trend will be evident everywhere in 2025 and for many more years to come.

6) Data Collaboration

Data and analytics have become a business’s most valuable competitive asset. Making informed decisions based on accurate insights can skyrocket success to a whole new level. That being said, analyzing data and extracting insights is not enough. Especially considering how accessible it has become to extract and manage valuable business data. To extract the maximum potential from your analytical journey, it is necessary to ensure full organizational adoption through powerful data collaboration and sharing practices, leading us to our next trend.

While the importance of data collaboration might seem obvious to some, it presents a challenge for most organizations as, for decades, it was the norm to say, “Don’t share data unless….” The issue is that in today’s context, where most businesses are undergoing digital transformations, not sharing data can be detrimental, as everyone across the company needs to be united to connect analytics to general business goals. In that sense, Gartner advises organizations to switch their mindset to “must share data unless..”. Doing so will enable more robust data and analytics strategies, empowering stakeholders to make agile and informed decisions.

Changing the mindset might not be easy, and organizations that don’t take it seriously might fail in the process. Gartner suggests establishing trust-based mechanisms to ensure decision-makers trust the data they collect and use to inform their strategies. This way, they will feel confident in using it, sharing it, and re-sharing it with those who might need it. This can be easily done by tracking data quality metrics and implementing catalogs to compile all the information related to the trustworthiness of the data.

When discussing data collaboration, the term “self-service BI” quickly pops up because those solutions do not require an IT team to access, interpret, and understand all the data. These online BI tools make sharing easier by generating automated reports that can be scheduled at specific times and to specific people. For instance, they enable you to set up business intelligence alerts and share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices, which enhances the decision-making and problem-solving processes critical for today’s ever-changing environment.

Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions. However, data collaboration does not only occur around the exchange or updates of some documents. It has to track the progress of meetings, calls, e-mail exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept.

In fact, it is expected that, in 2025, data sharing will move further from just sharing insights and will start from earlier stages. Starting from data exploration and spreading across the entire analytical workflow for a more efficient decision-making process that includes every stakeholder, regardless of location. This last point is especially important when considering the growing security concerns many businesses face today. Implementing a collaborative BI approach enables every stakeholder and data user to be accountable for the decisions he or she makes, ensuring a more secure workflow.

What about construction?

Data collaboration is another of the BI trends on this list that is highly relevant to the building industry. Construction collaboration has long been considered one of the most challenging areas, as projects are composed of multiple processes, people, and tools, and coordinating all of them takes work. At the same time, effective collaboration can be the key to success in the construction industry. It can keep everyone working from a single source of information, eliminate the risk of rework due to miscommunication, and help projects finish on time and within the expected budget.

In 2025 and beyond, construction companies of all sizes need to turn to data as a crucial asset to boost collaboration and communication. Professional construction collaboration software offers the perfect online environment to manage all documentation and information in a single place. Reports, documents, drawings, and more are updated in real time, ensuring everyone is working with the latest data available. An audit trail tracking who changed the data and when ensures a culture of transparency and accountability that improves the efficiency and productivity of construction projects.

By implementing a collaborative approach supported by the right tools and processes, developers and average business users are expected to work together under the same analytics umbrella, enabling more united communication and a productive work environment. Let’s see how it will be developed in the business intelligence trends topics of 2025.

7) Edge Computing

Edge computing is a distributed IT architecture in which user data is processed near where it is generated. This enables faster processing of large volumes of data for better real-time insights. Examples of “edge” devices include autonomous robots, self-driving cars, intelligent bots, and other IoT devices.

The increased analytics capabilities of edge computing solve some common challenges of on-premise data centers, like bandwidth limitations, unexpected network disruptions, and latency issues. This, combined with the growth in the number of devices generating data, particularly IoT, and the volumes of data being produced, makes the capabilities of traditional data center infrastructures insufficient, generating a need for an edge approach.

Edge computing is already a more established concept in industries like healthcare, factories, and retail, which require near real-time access to secure and reliable data in rapidly changing environments. What makes this out of all BI trends so exciting is its potential to revolutionize other industries and functions, including the construction sector.

Construction sites have long struggled to transfer data in a timely and efficient manner. This data is fundamental as it supports effective project management and allows for the expected levels of quality and safety to be met. Edge computing solves this challenge by decentralizing data and bringing it closer to the location where it is generated. This significantly decreases latency and facilitates real-time decision-making.

To make this possible, construction sites use IoT devices like sensors and wearables that are installed across the site on materials, machines, and workers. These edge devices generate data about workers’ movements or vital signs, equipment usage, environmental conditions, and much more to help construction managers identify potential hazards or improvement opportunities and implement strategies to streamline productivity and ensure safety at all times. Additionally, edge computing reduces cloud storage and bandwidth costs, making it especially beneficial for remote construction sites with limited connectivity.

Some common use cases for edge computing in construction include:

  • Tracking possible safety risks like structural issues, potential gas leaks, unauthorized access to restricted areas, and more, sending instant notifications to mitigate the threat or deal with the problem.
  • Performing predictive maintenance on machines, helping to identify if a machine needs something fixed, and reducing potential downtime and delays.
  • Image classification systems track the progress of various on-site activities in real-time and ensure they comply with quality, safety, and sustainability requirements.
  • Real-time monitoring of material and equipment utilization, ensuring each resource is used properly to avoid waste.  

All in all, edge computing has the potential to revolutionize businesses by helping them extract reliable and faster insights to accelerate their performance. That said, companies need to be careful and invest in edge solutions that allow them to effectively deploy software at a massive scale without unnecessary costs and tasks, can handle a diversity of equipment and devices, and have robust features to manage security concerns, among other things. We look forward to seeing how this trend will develop in 2025!

8) Natural Language Processing (NLP)

Natural Language Processing (NLP) is one of the recent trends in business intelligence that is revolutionizing how companies approach their analytical processes. Considered amongst the most powerful branches of AI, NLP enables computers and machines to understand, learn from, and interpret human language in a spoken or written form, and it can be divided into two subsets: natural language understanding (NLU) and natural language generation (NLG). NLU focuses on understanding the meaning behind text and speech, while NLG focuses on text generation based on specific data input.

The growth of this trend has been such in the past years that its $3 billion worldwide market revenue from 2017 is expected to be almost 14 times larger by 2025, reaching $43 billion, according to research by Statista. This is not surprising as language-processing applications are already present in our daily lives in the shape of car navigation systems, smart voice assistants like Siri or Alexa, autocomplete text features on our phones, and translation apps, just to name a few.

Considering all of that, it is not surprising that businesses have begun to adopt this technology to manage the large amounts of unstructured text data they gather from different sources such as emails, social media, or surveys. As a response, multiple BI software providers offer their users language insight features. There are two major use cases for which language processing is becoming increasingly popular in the BI industry. Let’s look at them in more detail below:

BI data assistant: Similar to the chatbots we see on multiple websites today, a data assistant is integrated into BI software to answer any analytical questions that a user might have. All you need to do is write a question in human language, and the assistant will provide you with the answer. As the technology matured in the past years, AI-based assistants went from simply showing search results for users to analyze to being able to filter and organize the data to generate analytical insights as an answer. This development has also helped democratize data as non-technical users can simply type a question, and the software will automatically show them an answer without needing complicated calculations or analysis.

Sentiment analysis, also known as opinion mining, is the process of analyzing text data to identify its emotional tone. Businesses often use it to analyze comments on social media, emails, blog posts, webchats, and more and determine whether the tone is negative, positive, or neutral. This allows organizations to extract useful insights regarding product development and brand positioning and understand pain points to improve the customer experience at different touch points.

NPL is one of the BI trends we will see developing in multiple areas over the coming years. BI software that exploits this capability with a self-service approach will gain a competitive advantage by allowing users to conduct efficient analysis without the need for any calculations. We will definitely be watching how this technology develops in 2025.

9) Predictive & Prescriptive Analytics Tools

Business analytics of tomorrow is focused on the future and tries to answer the question: what will happen? How can we make it happen? Accordingly, predictive and prescriptive analytics are by far the most discussed business analytics trends among BI professionals, especially since big data is becoming the focus of analytics processes being leveraged by big enterprises and small and medium-sized businesses.

Predictive analytics is the practice of extracting information from existing data sets to forecast future probabilities. It’s an extension of data mining that refers only to past data. Predictive analytics includes estimated future data and, therefore, always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessments. Applied to business, predictive analytics is used to analyze current data and historical facts to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities. In contrast, bankers use it to generate a credit score – the number generated by a predictive model that incorporates all the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

Predictive analytics must also become accessible for everyone, and in 2025, we will witness even more relevance that will cater to that notion. Self-service analytical possibilities are becoming a criterion for BI vendors and companies alike; both can profit from it and bring more value to their businesses. The predictive models, in practice, use mathematical models, in other words, forecast engines, to predict future happenings. Users simply select past data points, and the software automatically calculates predictions based on historical and current data, as shown in the example:

Predictive Analytics Tool
Predictive Analytics Tool

Among different predictive analytics methods, two are quite popular among data scientists: artificial neural networks (ANN) and autoregressive integrated moving averages (ARIMA).

In artificial neural networks, data is processed similarly to that of biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it, and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with a considerable volume, number of variables, or diversity of the data.

ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be considered when making predictions. Each part of ARIMA takes care of different sides of model creation – the autoregressive part (AR) tries to estimate the current value by considering the previous one. Any difference between predicted data and real value is used by the moving average (MA) part. We can check if these values are normal, random, and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predict new anomalies, or help to discover underlying patterns not visible by the bare eye. ARIMA techniques are complex, and concluding the results may not be as straightforward as for more basic statistical analysis approaches. However, once the basic principles are grasped, the ARIMA provides a powerful predictive analysis tool.

Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps are taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see the effect of future decisions and adjust them before they are actually made. This dramatically improves decision-making, as future outcomes are considered in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory, and supply chain design to deliver what your customers want in the most optimized way, and these are some of the emerging trends in business intelligence that we will hear more about in 2025.

10) Embedded Analytics

When data analytics occurs within a user’s natural workflow, embedded analytics is the name of the game. Businesses have recognized the potential of embedding various BI components, such as dashboards or reports, into their own application, thus improving their decision-making processes and increasing productivity. Formerly strangled by spreadsheets, companies have realized how utilizing embedded dashboards enables them to provide higher value within their own applications. In fact, the embedded analytics market is expected to reach USD 182.7 billion by 2033, exhibiting a growth rate (CAGR) of 12.82% during 2025-2033. This is definitely one of the business analytics trends we will hear even more about in 2025.

Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations. In 2025, we will see even more companies adopting it. Departments and company owners seek professional solutions to present their data without building their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting they can offer consumers.

More than just embedding a dashboard or BI features in an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By allowing clients and employees to manipulate the data in a well-known environment, you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends on this list.

Business Wire recently published a report called “Global Embedded Analytics Market (2021 to 2026) – Growth, Trends, COVID-19 Impact, and Forecasts,” in which they mention that “organizations are deploying embedded analytics solutions to realize significant gains in revenue growth, marketplace expansion, and competitive advantage.” They also add that embedding analytics will grow significantly in the healthcare industry in the coming years. Considering the massive amounts of data that hospitals collect, which got even bigger with COVID-19 and telemedicine interactions, healthcare businesses “switch from paying for service volume toward service value.” By using powerful healthcare analytics software that can be embedded, hospital managers can extract valuable insights that will help them optimize processes from a clinical, operational, and financial point of view.

This is one of the trends in business analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexity.

Become Data-driven in 2025

In this article, we’ve summed up what the near future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2025:

  1. Artificial Intelligence
  2. Data Security
  3. Synthetic Data
  4. D&A Sustainability
  5. Data Collaboration
  6. Continuous Intelligence
  7. Edge Computing
  8. Natural Language Processing
  9. Predictive And Prescriptive Analytics Tools
  10. Embedded Analytics

Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2025 will be an exciting year of looking past all the hype and moving towards extracting the maximum value from state-of-the-art online business intelligence software.

At RIB Software, we are committed to offering construction companies the best solutions to meet their needs. Our BI software, RIB BI+, is a state-of-the-art platform with innovative features to take your analytical journey to the next level. If you are ready to boost your performance and increase the ROI of your construction projects with data-driven insights, get a demo today!

RIB BI+ ▷ The Best Construction Analytics & Dashboards
RIB BI+ ▷ The Best Construction Analytics & Dashboards

We hope you enjoyed this overview, and stay tuned for more business intelligence industry trends!