The future of business intelligence: 10 top trends in 2024

1. Governance of BI use

A consequence of the successful implementation of business intelligence systems is that more business users than ever have access to BI data, not just in reports but as an analytics resource. That's especially the case in self-service BI environments, which enable business users to analyze data themselves instead of relying on skilled BI professionals to run queries for them. This increased data access is a good thing overall. But combined with the increasing frequency of cyberattacks and new regulatory requirements, it's driving a heightened focus on data security and privacy protections.

In fact, data security and data governance are now the most critical concerns for many businesses when they deploy BI applications. To help address those concerns, BI vendors are incorporating specific compliance features for regulations such as HIPAA and GDPR into their management consoles. In addition, new technologies that can help govern BI use are emerging -- analytics catalogs, for example.

Just as a data catalog provides an inventory of available data sources, an analytics catalog is a centralized application where users can find the most relevant -- and most appropriate -- BI dashboards, reports and other analytics artifacts for their work. This helps to ensure that not only data sets but the entire decision-making process driven by BI is well governed.

2. Data quality management: The foundation of reliable analytics

High-quality data is essential for effective decision-making. That's true in conventional BI practices. Moreover, as AI and machine learning become more mainstream components of BI initiatives, the accuracy of AI and ML models depends on the quality of their source data.

As a result, organizations are investing in tools and processes to ensure data consistency and reliability. Some of these data quality tools are themselves driven by AI and machine learning. For example, they use predictive analytics to impute missing data values or large language models to ensure that product names and other values are consistent.

One of the most important trends within data quality management is a new emphasis on data pipelines as a dynamic source of data for BI and analytics applications. It's often said that the best measure of data quality is whether the data is fit for purpose. But when data has diverse analytics purposes, different levels of quality might be appropriate for each use case. Data pipelines offer a more adaptive, less static approach to cleansing, conforming and consolidating data. That's making the pipeline architecture increasingly popular compared to traditional extract, transform and load, or ETL, processes.

3. Increased focus on data literacy

As data becomes more essential to our work, it's clear that data literacy is crucial for decision-makers. Organizations are, therefore, investing in training programs to improve data skills across all levels of employees, not just for BI teams and other data specialists. Corporate leaders often talk of the need to establish a data-driven culture.

A key component of data literacy is the ability to create clear and effective data visualizations that are easy to understand. To enable this, BI vendors are building guided best practices for data visualization into their applications to ensure the most appropriate use of charts and other graphics.

Another key driver of data literacy in organizations is the need for collaboration and shared decision-making. Communicating with BI data, and using it to support decisions across teams and departments, is much easier when all users have at least a shared baseline understanding of what the data and its analysis means for them as well as the organization.

4. Low-code and no-code application development

As business users become more data-literate, they also need to be able to use analytics tools and make data-driven decisions in new situations, not just while running a desktop BI application. One solution is to create BI applications that support decision-making for specific scenarios, such as sales, equipment maintenance or HR.

In the past, building such applications would have required a team of in-house BI developers. Today, many BI platforms have low-code or no-code development capabilities for generating and deploying applications directly in BI tools. These lightweight environments don't require high-level development skills, enabling business users to do some of the work themselves.

Low-code and no-code applications can run on desktop and laptop PCs, in the cloud or on mobile devices. Although they're typically simpler than fully developed applications, they're an effective means of putting BI data into the hands of every user.

5. Use of advanced analytics, machine learning and AI in BI

Enterprise use of advanced analytics, machine learning, and AI technologies continues to grow, driven partly by recent advancements in generative AI (GenAI) tools. Organizations of all sizes are exploring ways to integrate these technologies into their business operations. Increasingly, this also means integrating them into BI and analytics workflows.

Sometimes, such integration is in the form of a chatbot that uses natural language processing (NLP) to provide a simple interface to BI data, while possibly generating SQL or another query language under the hood. NLP-driven natural language query and search capabilities can help users explore data more effectively.

Another valuable use case for AI in business intelligence is to automate data preparation by replacing manual data cleansing, transformation and integration work with faster and more accurate processes. Companies are also incorporating predictive analytics driven by machine learning algorithms into the BI process to try to anticipate market trends and shifts in customer behavior.

AI also offers a degree of personalization, providing insights to individual users based on their role and previous interactions with data sets. For example, a GenAI tool might recommend appropriate reports or highlight specific KPIs in a dashboard.

6. Augmented analytics driven by AI and machine learning

While self-service BI and increased data literacy bring analytics within the reach of more business users, it can still take considerable time and skill to find patterns, relationships and other insights in data. Driven by AI and machine learning, augmented analytics aims to make data analysis more efficient, accessible and insightful by automating time-consuming tasks and surfacing insights that humans might miss.

For example, augmented analytics in a BI application might use machine learning to identify outliers or trends in data and then highlight the discoveries in the user interface to draw attention to them. Another augmented analytics capability is suggesting new avenues of data exploration or questions to ask based on data patterns discovered by a machine learning algorithm. In addition, augmented analytics features can aid in tasks such as data collection, data preparation and data visualization.

This approach doesn't replace humans with a fully automated process. Rather, it helpfully nudges business users, BI analysts and other analytics professionals to find more insights in data. This often improves their analytics skills and practices, ultimately leading to more informed decisions in an organization.

7. Data warehouse modernization and the data lakehouse

For many years, the data warehouse has been the backbone of BI, reporting and some forms of advanced analytics. Traditional data warehouses commonly store an extensive set of historical data on business operations structured as a logical model that can be efficiently queried by BI applications.

However, an increasing variety of demands are being placed on enterprise data stores by data science and AI applications in addition to conventional BI. Modernizing data warehouses to meet these demands is a significant trend, especially for best-in-class companies. For starters, that includes automating and streamlining various data warehousing tasks.

In addition, as organizations deployed big data environments, we saw the emergence of the data lake as a repository of raw, unprocessed data for use by numerous applications, especially data science ones. Data lakes sometimes are also a source for data warehouses, where the raw data is cleansed and transformed for BI uses.

More recently, the so-called data lakehouse architecture has become popular. As the name suggests, it's a hybrid of a data lake and a data warehouse, with the flexibility of the former and the performance and data management capabilities of the latter. For BI users, a data lakehouse affords access to a wider range of data and enables processes such as machine learning to more easily be integrated into BI and analytics workflows.

8. Analytics as code

Alongside these new architectural trends, analytics as code is a new approach that blends data analysis with code-based methodologies.

Traditional BI tools often use a number of artifacts, such as measures, dimensions and hierarchies, to model data. But it's difficult to reuse these objects in different dashboards or applications. As a result, common ones, such as a geographical hierarchy or a business calendar, often must be re-created numerous times -- an error-prone and time-consuming process. It's also hard to collaborate on such objects, so individual BI analysts often need to develop separate ones for their own use.

These problems of reuse and collaboration have already been solved in the world of application development, where technologies for version control and working collaboratively are standard. Analytics as code is a way of bringing such well-established practices into the world of BI. It doesn't mean that every BI or data analyst now needs to become a developer -- rather, the artifacts they create are saved as code. In this form, the artifacts can then be versioned and shared effectively.

BI vendors who adopt this approach often make it possible to save the artifacts in a data lakehouse along with the corresponding source data.

9. Emergence of decision intelligence

Business intelligence remains a form of decision support, a very old term that tells us a lot about the primary use case of the technology: helping business users make better decisions. Reports and dashboards provide views of data to support users in decision-making. But while we might describe the BI process as a form of actionable intelligence, making decisions and then acting on them typically has happened outside the scope of BI applications.

With this in mind, decision intelligence is an emerging interdisciplinary field that combines aspects of data science, BI and analytics to improve the decision-making process. Users of decision intelligence tools are able to launch actions from within BI applications and then track the progress of the affected business processes.

The aim is to close the loop between the data that supports a decision and the follow-up data that measures performance against the decision after it's made. The use of decision intelligence technology is becoming common in corporate functions such as risk management, resource allocation and budgeting.

10. Expanded use of embedded analytics

Embedded analytics is the integration of BI capabilities into other applications. It gives business users actionable insights within their operational workflows, without requiring them to switch to a separate analytics application. This is helpful for supporting real-time decisions. Because data analysis features are embedded in familiar applications, it also encourages user adoption of analytics and reduces the need for specialized BI training.

An embedded analytics deployment can be as simple as an individual page in an application that contains a summary report or a dashboard. A more sophisticated approach could embed a data visualization directly in the UI of the operational application -- right at the point of decision-making. Some embedded analytics tools might even include hints or prompts for actions to take that are generated behind the scenes by machine learning algorithms.

In many cases, these capabilities can be embedded in applications so naturally that users are unaware they're using sophisticated BI and analytics technology as part of their work.

BI remains important to businesses into the future

The current BI landscape is characterized by a strong focus on governance, an increasing range of users and various new analytics technologies. Of course, it's likely that AI will revolutionize the world of data analytics in the years ahead. However, we shouldn't expect that automated analysis will completely replace human insight as part of the BI process.

Just as static reporting still serves a useful purpose in the enterprise, BI dashboards, analytics-driven applications and other aspects of business intelligence will remain important -- even as AI becomes the more impactful, or at least more exciting, technology.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.