10 trends that will shape the future of business intelligence

Like every other industry, many of the trends likely to shape the future of business intelligence in 2024 and beyond will be driven by the sudden widespread availability of generative artificial intelligence applications. Many, but not all.

For example, the final trend in our list addresses the need to combine BI technology (which helps identify trends in business performance) with Process Intelligence (which can explain why they’re happening, identify which processes to address, and provide the means to do so). In short, Process Intelligence helps transform BI insights from ‘actionable’ to ‘actioned’.

This article will explore some of the most important BI trends that look set to redefine how businesses leverage their data to gain competitive advantage.

Trend 1: Augmented analytics (more AI in BI)

It is predicted that by 2026 generative artificial intelligence will significantly alter 70% of the design and development effort for new web applications and mobile apps. Business intelligence tools are part of this revolution. It won’t be long before the integration of AI and machine learning technologies will be considered table stakes for a modern BI tool – some might argue we’re already there.

Here are some of the increasingly prevalent applications of augmented analytics (the use of AI technologies to collate, enhance, automate, analyze, share and activate BI data to support data driven decisions).

Data preparation and processing

Organizations have huge volumes of raw data to sift, analyze, cleanse, align, standardize and integrate in order to generate maximum value from their BI platform. AI systems can automate these ETL (extract, transform, and load) processes, providing BI teams with accurate, relevant, and correctly formatted data – saving a great deal of time and effort.

Beyond this, AI can also extract previously inaccessible insights from unstructured data (such as emails, contracts, or free text fields) – estimated to account for as much as 80% of business data – and load them into the BI model.

Predictive analytics

Advanced analytics have become a familiar calling card of generative AI. One such application within business intelligence is predictive analytics. Using historical data, live BI data and learned business context, the AI forecasts future business outcomes to inform strategic decision making.

Prescriptive analytics

Prescriptive analytics takes AI’s role in the decision-making process a step further. Where predictive analytics suggest what is likely to happen, prescriptive analytics leverage the same big data points and business context to suggest what an organization ‘should’ do in order to achieve its business goals.

Personalized BI insights

AI-powered business intelligence solutions can personalize insights based on individual user preferences and roles. Through analyzing user behavior and past data interactions, AI tailors relevant reports, highlights key metrics, and selects insights specifically for each user, enhancing the experience with contextual information.

Natural language processing (NLP)

Business intelligence democratizes business data, delivering insights to a wider cross section of an organization’s employees. Natural language processing (NLP) takes this to a whole new level and enables independent data discovery to non-technical personnel. NLP is the branch of artificial intelligence that enables computers to understand, process, and generate human language.

In the BI environment, NLP provides a more user-friendly experience, enabling individuals to conduct detailed data analysis by querying business intelligence platforms using their normal everyday language. In this ‘conversational BI’ experience, the NLP interface interprets the query, conducts the analysis and feeds back the answers. In removing the requirement for technical knowledge of how to query a BI database, NLP provides a friction-free route to insight discovery.

Trend 2: Self-service business intelligence

Reliance on IT teams, business analysts, or other technical personnel can be a bottleneck in business intelligence processes – slowing the flow of insights to decision makers. That’s possibly why self-service BI options are increasingly popular (the global self-service BI market size is projected to grow from $5.71 billion in 2023 to $20.22 billion by 2030).

A self-service BI tool enables each business user across different functions and skill levels to become a citizen data scientist. Each can access, analyze and action insights from business data independently of – or with far less reliance on – IT or BI specialists.

Self-service BI solutions typically provide user-friendly interfaces that allow the user to navigate business data easily, explore data visualization with intuitive drag-and-drop features, and set-up bespoke reports or alerts. The self-service approach enhances an organization’s data discovery capabilities, promotes data literacy, and democratizes data analytics – enabling any authorized stakeholder to draw valuable insights.

Trend 3: Embedded analytics

Embedding BI components such as visualizations or reports into users’ standard workflow and applications, rather than using a separate platform, is one of the fast emerging business intelligence trends. The embedded analytics market is estimated to be worth $68.88 billion in 2024, but by 2029 it is expected to reach $132.03 billion. Advantages to this approach include:

• Ease of adoption: With embedded analytics,BI elements are delivered into familiar apps and workplace environments – bringing BI to existing workflows, rather than creating new workflows to incorporate BI.
• Productivity / speed to value: Being able to generate BI insights from familiar applications flattens the learning curve, enhances understanding and performance, and shortens the timeframe for value realization.
• Promotes collaboration: Analytics embedded into core business applications, can bring BI to all stakeholders and enable seamless sharing of insights and data-driven discussions.

Trend 4: Data security, data governance

To deliver actionable insight, BI software must process huge volumes of business data. As BI becomes more democratized through self-service, and is activated across multiple cloud domains, the risks of unauthorized access, misuse, and lack of trust in data increase. Maintaining the security, quality and integrity of this data is therefore central to business intelligence strategies – both from the standpoint of optimizing BI outputs and for ensuring data privacy compliance with regulations such as the CCPA or GDPR. In the future this might also include laws stemming from the proposed ‘AI Bill of Rights’.

Data governance includes, for example, defining clear data ownership and usage policies, implementing encryption technologies, and deploying role-based access controls. More recently, data governance also extended to maintaining oversight and controls for AI’s use of BI data. It’s vital for businesses leveraging AI to understand the rationale driving predictive analytics and to dynamically refresh the business context that ground AI’s forecasts.

As Gartner puts it “A comprehensive AI trust, risk, security management (TRiSM) program helps you integrate much-needed governance upfront, and proactively ensure AI systems are compliant, fair, reliable and protect data privacy.”

The greater the emphasis on generating value through AI-empowered business analytics, the more important data governance and data security will become. As an indicator, the 2024 data governance market size is estimated at $3.27 billion, by 2029 this is expected to reach $8.03 billion.

Trend 5: Data quality management

A BI solution is wholly reliant on its data quality to provide accurate insight for informed decisions. Consequently, data quality management is an increasingly important component of business intelligence deployments. In fact, the market size for data quality tools is projected to grow to $8.49 billion by 2030 from $3.23 billion in 2023.

Business intelligence tools fuelled by poor quality data can turn would-be competitive advantage into actual commercial disaster, because:

• Financial investments in BI solutions are wasted
• Bad data creates bad data analytics which fuels bad decisions, corrupting business strategies
• Training generative AI applications using poor data undermines their effectiveness and decision intelligence

Data management solutions help cleanse, standardize, verify and align data sets, as well as validating information provenance. They help ensure the data in data-driven decisions provides a solid foundation for business strategies.

Trend 6: Collaborative BI

Collaborative business intelligence platforms combine BI tools with common collaboration technologies – one example of many is Power BI with Microsoft Teams – to facilitate easy sharing of insights and strategic collaboration between different business functions. Collaborative BI platforms enable different teams to work together concurrently on data analysis, often integrating discussion threads into BI reporting in order to promote mutual problem solving, data driven collaborations and shared understanding.

Read: Celonis Connector for Power BI supercharges Microsoft’s business reporting platform with process intelligence

Trend 7: Decision intelligence

Decision intelligence (DI) is defined by Gartner as “...a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.” As an emerging trend within business intelligence, its focus is the use of AI technology to enhance, accelerate, and even automate decisions deriving from BI data.

DI software uses machine learning (ML) algorithms to process a company’s big data and creates a semantic layer of business rules and contexts to ground predictive analytics which produce forward-looking insights to improve an organization’s decision making capabilities. Reports suggest that the global DI market is set to grow from $13.3 billion in 2024 to $50.1 billion in 2030.

Trend 8: Mobile BI

In 2023, it was estimated that almost 96% of the global digital population used a mobile device to connect to the internet. Given the ubiquity of cell phones and the growth of BYOD (bring your own device) in workplaces, it’s little surprise that BI solutions optimized for mobile devices are set to become more prevalent. One study, for example, forecast that between 2021 and 2026 the mobile business intelligence market will experience a CAGR (compound annual growth rate) of 22.43%.

Mobile BI provides business leaders with the convenience of on-the-go insights to support data driven decisions, wherever they are and whenever they need the information. The growth in cloud BI data sets and faster mobile internet speeds, has made interactive, mobile-optimized business intelligence an attractive reality.

Trend 9: Data storytelling

“By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.”

This prediction from Gartner illustrates the growing importance of data storytelling as a means to deliver impactful business intelligence insights – importance underscored by the fact that most of the major BI platforms now include data storytelling extensions. Data storytelling is a variant of BI reporting that introduces both narrative structure and actual narrative to BI visualizations in order to emphasize important insights, to explain what’s happening and why.

It introduces a cohesive data narrative that links visualizations to help direct audiences, rather than leaving them to infer specific insights or causality. This is particularly useful when sharing data across multiple teams with varying skill sets, who might otherwise miss the significance of the trends being communicated.