Enterprise business intelligence revolves around data collection, transformation, analysis, and governance across a large organization with complex business processes and workflows. Compared to departmental BI software that addresses the needs of individual teams, such as marketing, financial, and HR, enterprise BI provides insights into organization-wide metrics.
With its help, numerous departments within one organization can use, process, and apply all generated information to their specific tasks. Thus, different stakeholders, from C-suite executives and department heads to the members of individual departments, can benefit from such a tool.
To process voluminous and varied data and make data-driven decisions at all company levels, a business needs to develop a good enterprise BI strategy. For this strategy to be effective and remain so over time, BI adopters need to consider both the company’s current and strategic needs and market trends. Here, we share some recent technology trends gaining traction for you to factor in when setting up or upgrading a BI solution.
3 Major Trends in Enterprise-Level Business Intelligence Tools
- The increasing impact of artificial intelligence and machine learning, leading to greater democratization.
- More self-service BI tools.
- More cloud-based BI solutions.
The Increasing Impact of AI and ML
Artificial intelligence (AI) and machine learning (ML) are two widely discussed technologies these days. AI and ML rely on continuous learning, enabling the underlying algorithms to get smarter over time by adjusting their performance based on real-world outcomes and new information.
The integration of these technologies into BI tools enables companies to manage extensive, complex data sets far beyond the capacity of human analysts. Thereby, they allow for deriving previously inaccessible insights from unstructured raw data (such as emails, text, social media posts, or Internet of Things (IoT) sensor data) that can otherwise be overlooked.
These capabilities can then be applied for the following:
- Modeling business scenarios under varying circumstances to define the optimal course of actions
- Conducting prescriptive analysis to craft an action plan for a company to achieve its desired goals
- Creating personalized content recommendations
- Improving the search experience within a platform with autocompletion, understanding mistyped requests, and suggesting similar results
- Generating content, such as writing summaries
- Detecting abnormal and potentially fraudulent activity
AL and ML also drive augmented analytics, automating data processing, cleansing, classification and analysis to minimize human involvement and pave the way for BI democratization. This translates into two main benefits:
- People with little to no experience or specialized skills in areas such as data science and statistics can generate basic reports and run analytical queries themselves without creating a long queue of requests to data experts and IT teams and disrupting the organization’s workflow.
- IT professionals and data experts can allocate more time to perform advanced tasks like processing and managing complicated data sets.
Thanks to the integration of AI and ML technologies, BI software can feature natural language processing (NLP) capabilities. This allows users to communicate with the BI system using natural language, ask questions in a human-like manner, and receive answers without needing to know SQL or other data query languages. NLP also contributes to data democratization and allows for a better user experience.
More Self-Service BI Tools
Most modern enterprise business intelligence platforms offer robust self-service capabilities, which simplifies data analysis and report creation. Therefore, non-technical users are able to access, interpret, and manipulate data. By using automated report generation, a specialist can schedule reports to be prepared at specific times and tailored to their requirements.
Once ready, they get a notification and can share the results with others. If needed, BI users can configure access to the report or dashboard for their colleagues and restrict their ability to change or delete the content. Self-service BI platforms can also be available on smartphones and tablets, giving employees more flexibility and freedom to compile, view, edit and share reports on the go.
Here are the worthwhile features of self-service BI tools:
- User-friendly interfaces
- Intuitive drag-and-drop features
- Customization capabilities to craft bespoke reports and alerts
- Ad-hoc querying
Data visualization and data storytelling capabilities can also simplify the interpretation of data. The former is about presenting insights in the form of pie charts, bar graphs, heat maps and scatter plots. The latter crafts a narrative around the reports, helping convert insights into well-defined, actionable strategies.
For self-service capabilities to deliver maximum benefit, the company needs to put additional effort into making data analysis more controlled and secure. Namely, you need to implement solid data governance, which ensures:
- Only high-quality data is used
- Fine-grained data access is imposed to ensure users access corporate data according to their roles
- Data in transit and at rest is encrypted, protected and indecipherable by third parties
- Data used for analysis and reporting is cleansed, standardized, enriched and structured
- Regulatory and compliance measures are applied to prevent data leakage
Cloud-Based BI Solutions
Enterprise BI software can be deployed on-premises, in the cloud or a hybrid environment. Although the first option grants more control to operate the system, more and more organizations opt for a cloud-based BI set-up. And there is a solid reason behind that.
Cloud deployment implies hosting BI software components on the vendor’s servers rather than the company’s hardware. It brings the following benefits:
- Cost savings: You don’t need to purchase hardware to accommodate the BI system. Apart from that, cloud BI offers more flexible pricing options. Depending on the required functionality, availability and performance requirements, you can choose the optimal licensing option and then scale it up or down when required.
- Comprehensive security: As more data is accumulated and processes get more tangled, ensuring company-wide security yourself can get daunting. With cloud deployment, you’re not the only one who should take care of system protection. Cloud solution and BI system providers (if you opt for a SaaS tool) also bear the responsibility for your data security.
- Faster roll-out: By purchasing the necessary SaaS package, you instantly get access to BI software and its analytical tools. Thus, you can extract insights without spending time on a laborious set-up.
- Facilitated collaboration: Cloud applications are accessible by different teams from anywhere with an internet connection. This ensures seamless, real-time cross-departmental cooperation in analyzing data, crafting reports, and exchanging insights.
- Easier scalability: You can adjust data storage and processing capacity based on the changing needs. This can be especially helpful for managing big data sets when processing power and storage requirements fluctuate.
- Automated updates: The BI software and cloud solution provider handles ongoing maintenance and schedules regular system updates. Automated upgrades can save you countless hours of platform upkeep.
According to Statista, annual spending on cloud IT infrastructure steadily grows (from $104.8 billion in 2023 to $129.9 billion in 2024 and $133.7 billion in 2026). As more businesses migrate to the cloud and choose SaaS, PaaS, and IaaS products, they need to implement a connected cloud strategy. A connected cloud is a platform that unites several cloud solutions and services into a functional, economical and cooperative package for more holistic and intelligent analytics. Enterprise BI systems can become a part of this strategy as they centralize data from disparate sources.
The Future of BI
Enterprise business intelligence implies the transformation and analysis of the large amount of raw data aggregated by a company into meaningful insights and understandable reports. According to Oracle, 78 percent of more than 14,000 employees and business leaders surveyed are bombarded with an increasing volume of data from more sources than ever before. Enterprise business intelligence software is a great solution for making sense of all this information.
Integrated with AI and ML software, amended with self-service capabilities and deployed in the cloud for superior scalability, enterprise BI tools will be more impactful, user-friendly, and widespread, making teams more empowered, autonomous and informed.
