Business intelligence helps organizations learn what is happening in their organizations. Initially, it was about producing static reports, using just basic data and numbers from multiple systems, to show organization leaders what had occurred, e.g., sales last month, quarterly revenue, etc. Although this reporting provided good information, it only provided a view of the past; therefore, there were limitations on what a business could do to prepare for the future.
Over the years, many changes occurred in how data is collected, stored, and analyzed due to technology. Thus, business intelligence transitioned from static reports to interactive dashboards and ultimately to predictive insights utilizing advanced analytics and AI.
This transition provides the opportunity for organizations to change the way they make decisions. Rather than reacting to events after they happen, an organization can now anticipate events and trends to successfully manage future risk and develop action plans with greater confidence.
What Business Intelligence Looked Like in the Early Days
Initially, business intelligence focused primarily on reporting. Businesses collected information from sales systems, financial systems, and operational systems, and compiled this information into reports prepared by analysts showing metrics for various performances of the business, including insights related to business communication effectiveness across teams and customer interactions
Alongside operational and financial data, early organizations also struggled to measure the effectiveness of internal communication. Decisions were often delayed due to fragmented communication across emails and disconnected tools. The lack of centralized, trackable communication platforms limited how quickly insights could be shared and acted upon across teams.
These reports were generally prepared manually and were issued on a weekly or monthly basis. They tended to be static in nature and were difficult to modify per the requirements of the individual requesting or reviewing the report.
Oftentimes, if an executive/manager had a new question to ask regarding performance, they had to wait until there was a new report produced in order to obtain the information needed to answer his/her question. This resulted in a slow and reactive decision-making process.
While there were limitations with business intelligence in its first form, it provided an organization with a means to measure past performance and monitor progress (i.e., past results).
However, the focus of business intelligence in its first form was on giving an organization insight into what occurred within its operations versus what could occur in the future based on forecasting.
The Shift from Static Reports to Dashboards
As both the amount of data being produced and the tools available have become more powerful, the field of business intelligence has begun to transition towards the use of dashboards.
The admin dashboard provides the ability for users to see all of their important metrics in one location and allows for updating of these metrics almost in real time.
Rather than having to wait for a report on their performance this way, leaders can log in and review their performance measurements whenever they want. These dashboards often pull data directly from a resource planning app, allowing leaders to monitor staffing, budgets, inventory, and project utilization in near real time.
The transition from traditional business intelligence to dashboard-based business intelligence has improved accessibility to business intelligence. Non-technical users can visualize their data and identify trends more quickly than ever before. The dashboard also allows teams to continuously monitor their performance versus only looking at it monthly.
Although dashboards have improved visibility into performance, dashboards are still mostly focused on showing descriptive what is currently happening and what has occurred in the past. There is still no way for businesses to look forward.
Why Traditional Business Intelligence Was Not Enough
Rapid changes in the marketplace have prompted a transformation in how companies approach change. In the past, companies relied on looking at historical data. Now, companies must be able to make decisions quickly, intelligently, and proactively.
Companies must leverage data to understand how they can proactively prepare for change rather than reactively respond to changes occurring in the market.
Traditional forms of Business Intelligence (BI) could show a company that there is a decline in sales from the previous month, for example, but it does not provide any information on the cause of the decline or whether the decline will continue. Therefore, many executives depended on their experiences and instincts to fill in the gap. This approach may work on occasion, but there is a risk involved.
The increasing amount of data available adds to the difficulty of this challenge. Companies now have access to data from multiple sources, including websites, mobile applications, social media, and other interactions with their customers. It is no longer feasible for companies to manually collect and analyze data from these different sources.
The Rise of Advanced Analytics
The subsequent phase in the progression of Business Intelligence is marked by the introduction of Advanced Analytics, which allows businesses not only to report but also to discover patterns and relationships within the data.
In addition to being able to provide answers to fundamental questions (e.g., why did this occur? what were the factors that impacted the outcome?), businesses can now take advantage of advanced analytical tools and methods to analyze Marketing Channels that drove the highest conversions, Customer Behaviors that contributed to repeat purchases, and AI-powered intelligence layers such as Manatal AI screening, supported by platforms like Skima AI for deeper insights.
Advanced Analytics provide a bridge between Data and Insight; however, Advanced Analytics still require a skilled analyst to utilize them effectively and are primarily used to work with historical data. Additionally, predicting future performance remains an ongoing challenge.
How Predictive Analytics Changed Business Intelligence
Predictive analytics changed the way companies use data by taking an entirely new approach to predicting future events. Rather than simply analyzing their previous performance, predictive models take into account all of the prior performances of a company and use that information to estimate the likelihood of a specific event occurring in the future.
For example, by developing predictive AI models for customer retention/churn, sales forecasting, inventory requirements, and potential risk (e.g., fraud), companies could not only take action earlier but also develop a more comprehensive future strategy.
Thus, organizations were able to turn business intelligence from simply a reporting tool into a form of strategic decision-making that helps leaders answer questions about what will happen in the future, with data to support those answers.
The Role of Machine Learning in Modern Business Intelligence
With the advent of machine learning, predictive analytics have taken a step further by eliminating static rule-based approaches to developing models. Machine learning models are capable of learning from data, and therefore develop and improve with time based on the data collected.
More specifically, predictions made by machine learning have the ability to become more accurate as additional data is gathered. Machine learning can adapt to shifting behaviors of consumers and changing market trends. This capability is particularly useful for industries that are experiencing fast-paced changes and competition.
Business intelligence systems that incorporate machine learning have also become more dynamic than they previously were; they no longer solely perform tasks related to the analysis of data, but now also learn from the data.
Moving from Insights to Action
The most significant transformation taking place in business analytics happens in the area of action-oriented analysis. Today's business intelligence solutions provide your organization with more than just insight from collected information; they assist you in making decisions and taking action.
Analysts can use predictive analysis to help prioritize activities for the Analytics Team, much like professionals who apply to jobs using AI to identify the most relevant roles and opportunities faster.
Sales personnel can determine which leads are most likely to close and subsequently focus their efforts on those customers. Customer Service employees can identify which customers may be at risk for departure. Operations employees can assess future demand before there is an actual change in demand.
Sales and support teams increasingly rely on AI-enabled calling platforms with regional virtual numbers to connect with customers while generating structured data for BI systems. Organizations expanding globally can quickly activate local numbers—such as a United States virtual phone number—to improve reach and measure performance by region.
The movement away from traditional reporting methods and toward analytical solutions allows for BI to be integrated into daily operations rather than being viewed as another reporting team.
Real-Time Data and Faster Decisions
The other major step of development for the field of Business Intelligence is the shift to real-time data access. Organizations no longer want to have to wait until the end of the day or week for their reports.
"Live" data gives teams the ability to instantly respond to changes within their business operations. For example, retailers can change pricing, marketers can improve their campaigns, and logistics teams can fix a problem while it's happening.
Real-time data and predictive insights work together to create a very powerful environment for businesses to make decisions. Businesses can see both what is happening now and what is most likely to occur next.
Democratization of Business Intelligence
Traditionally, most businesses used Business Intelligence exclusively by analytics and technical professionals. Nowadays, every employee can benefit from BI Tools that enhance the way they perform their jobs.
Through the use of self-service BI tools, employees across different functions can access BI data and create BI-based decisions today vs needing technical skills to do so. This type of empowerment to create your own BI Strategies and Use is called democratisation of Business Intelligence.
In addition to BI Access, there has also been a rise in tools containing predictive capabilities with minimal need for creating their own predictive models from scratch.
Business Intelligence in Different Industries
Business intelligence is changing all different types of industries in different ways. Retailers use predictive analytics for forecasting demands and customized offerings, while finance relies on this technology to mitigate risks and prevent fraud; hospitals utilize this technology to provide improved patient care as well as manage resources.
All of these industries share the ability to progress from merely reporting to predicting. In addition, one of the common denominators among these industries is the ability to plan better and respond more quickly.
Challenges in Adopting Predictive Business Intelligence
There are challenges associated with predictive business intelligence despite all of the advantages it offers. One of the biggest challenges is that data quality plays a critical role in predictive models as they rely on having accurate and complete datasets. Without high-quality data, any insight will also be unreliable.
The next challenge is processing the data quickly and securely. Machine learning applications, especially those producing insights in real-time, require parallel processing power that traditional servers don’t have. Many organizations are moving these workloads to rented GPU servers in order to keep pace.
Another challenge is trust because some teams do not feel comfortable using predictions from models that they do not completely understand. To help with this, teams must have open and clear communication about the models they utilize to ensure the models are being adopted.
Lastly, resources and education are needed in organizations that utilize predictive intelligence tools. Many tools used for predictive modeling have become relatively simple to operate; however, organizations must still have individuals who fully understand data sets to accurately interpret insights generated from those tools.
The Importance of Data Culture
The evolution of Business Intelligence cannot happen without not only the technologies related to it, but a strong sense of data culture, as well.
An organization can build a data culture by fostering a curiosity about information and promoting critical thinking skills. In addition, leaders must utilize data when making day-to-day business decisions and assist team members in doing the same.
When an organization is supporting a data culture, complemented by the use of advanced BI tools, then employees will be able to utilize predictive insights regularly in their daily work.
Integrating Business Intelligence Across Systems
An integrated system of modern business intelligence delivers maximum value, meaning that all elements of data (CRM, marketing platforms, financial tools, and operational systems) are linked to each other in order to provide a comprehensive picture of the entire organisation.
By linking different data sets to each other, your ability to generate predictive insights from the data can be enhanced; this is due to the use of combined rather than isolated datasets as a basis for generating insights.
The greater the integration between different systems, the more accurate and valuable the business insight that can be drawn from them.
From Prediction to Prescription
The evolution of business intelligence has moved to prescriptive analytics as the next step. While predictive analytics predict what will happen, prescriptive analytics recommend actions based on the analysis of given data.
Prescriptive analytic systems typically make recommendations for businesses on what they should be doing to meet their goals. For example, a prescriptive analytic system may suggest to a business that they should adjust prices, reallocate budgets, and/or change processes to reach their goals.
Prescriptive analytics is still in its infancy, but it will continue to change how businesses interact with customers, suppliers, and themselves in the future.
How AI Is Shaping the Future of Business Intelligence
The application of artificial intelligence keeps progressing business intelligence. Users can use natural language questions to ask questions using plain language. Automated insights provide key changes to users without requiring user intervention to conduct manual analysis.
Machine learning will help users to better understand and trust their actions as they receive explanations for their predictions. These improvements will encourage greater use across organizations' teams.
As machine learning technology advances, business intelligence will evolve into a proactive, intuitive solution for organizations.
Preparing for the Next Phase of Business Intelligence
To reap the benefits of predictive insights, companies must adequately prepare. This means developing a robust data infrastructure, enhancing the quality of their data, and educating their teams on best practices.
Additionally, it is critical to establish clear objectives for your predictive BI efforts, as they are most effective when tied to measurable business goals and questions.
By using a strategic approach, businesses will have more confidence as they move into the next phase of their business intelligence initiatives.
