The evolution of business intelligence and analytics has been so successful that it has become fundamental to businesses today. There was a time when analytics was once performed manually with pen and paper; now, businesses utilize powerful Business Intelligence (BI) tools to analyze big data and provide decision-makers with descriptive, predictive, and prescriptive analytics in real time.

Here are five key takeaways from this article:

  • Data Quality Management is now being stressed by corporations more than ever
  • Big data and cloud computing enhance BI with accessible data storage and AI integration.
  • Modern BI platforms have embedded analytics, cross-tenant sharing, AI capabilities, and low-code/no-code tools.
  • BI as a service offers end-to-end solutions for new businesses.
  • BI and data analytics differ in actionable insights vs trends/patterns focus.

In this article, we’ll discuss the history of BI and talk about the impact of big data and cloud computing on BI operations. We’ll also discuss some modern BI features, key BI trends, the importance of BI security, and the use of Business intelligence as a service.

Table of Contents


Business Intelligence (BI) operations have evolved significantly over the years, thanks to advancements in technology and changes in business practices.

Here are some key developments in the evolution of BI:

Emergence of Data Warehousing: In the 1980s, data warehousing emerged as a way to store large volumes of data in a central repository, making it easier to analyze and report on data.

Growth of Data Mining: In the 1990s, data mining became an important aspect of BI operations. Data mining involves using statistical techniques and algorithms to discover patterns in large datasets.

Rise of Big Data: With the advent of the internet, social media, and other digital technologies, the volume of data generated by businesses skyrocketed. This led to the emergence of big data, which requires new tools and technologies for processing and analyzing large datasets.

Adoption of Cloud Computing: Cloud computing has become increasingly popular in recent years, providing businesses with scalable and cost-effective solutions for managing and analyzing data.

Evolution of Artificial Intelligence and Machine Learning: Advancements in AI and machine learning have enabled businesses to automate many aspects of their BI operations, including data processing, analysis, and reporting.

Focus on Self-Service BI: In recent years, there has been a growing focus on self-service BI, which enables non-technical users to create and analyze reports without relying on IT departments or data analysts.

Overall, BI operations have evolved significantly over the years, with businesses adopting new technologies and practices to manage and analyze data more effectively. These developments have helped businesses make more informed decisions and gain a competitive edge in their industries.

A Brief History of Business Intelligence

Business intelligence first appeared on the scene in 1865 with Richard Miller Devens’s work Cyclopaedia of Commercial and Business Anecdotes. The author detailed how a banker used data and analysis to make strategic initiatives for competitive advantage. This, and the applications that followed, were the stepping stones to the scientific approach that would help make informed business decisions.

BI 1.0—From the 1950s to the 1980s, business intelligence underwent a digital revolution led by companies like IBM and Microsoft. The approach became more regularly sought by entrepreneurs, and the introduction of computers increased the use of data to make analyses and more informed decisions.

BI 2.0—The introduction of data warehousing did the biggest service to business intelligence operations. With data warehousing, different data sources could be kept in one location for analysis. Tools like Online Analytical Processing (OLAP) and Extract Transform Load (ETL) helped provide fast business intelligence solutions. 

From the 2000s, local data warehouses became globally available, followed by a change in the data warehousing approach—a single source of truth. 

This data became the “big data” now known to everyone. It was a great step forward in the advancement of data analytics and the decision-making process.

BI 3.0—The next generation of BI came with the use of data collection and search algorithms, followed by the introduction and inclusion of unstructured data. Now with machine learning and artificial intelligence integration into the field, modern business intelligence is as efficient as it can be.

Impact of Massive Data Volume (Big Data) and Cloud on Business Intelligence Operations

Big data constitutes a high volume of structured, semi-structured, or unstructured data that can be accessed from a single reference point. Cloud computing is the on-demand availability of data storage services and computing resources with high computing power to process and analyze big data

When data mining and data visualization tools like OLAP were established, they became widely popular in business processes for making informed decisions using big data. The aggregation of unstructured data, like a person’s location, helped decision-makers revisit their business strategies and include new information to widen the scope of their decision-making processes for further competitive advantage.

Cloud came into the picture when data management in local machines became impossible with the exponential increase in the amount of data. Some of the impacts of big data and cloud computing on BI operations are discussed below:

  • Business analysts now use business intelligence tools and scalable data storage at reduced expenditure.

  • Business data is now easily accessible and usable across a company’s hierarchy, from executives to junior analysts.

  • With cloud computing, companies now integrate artificial intelligence (AI) with their business processes to understand their customers better and aim for targeted marketing. 

  • Every search you perform is stored in a data lake and used by a machine learning (ML) algorithm that, i.e., your past shopping data, lets the decision support system (DSS) decide which brand you should be recommended via the social media platform you use. 

This is the extent of the impact big data and cloud computing have brought in today’s business intelligence operations.

Some Modern BI Features

Modern tools for business intelligence offer not only speed but the ease of access for business intelligence operations. Some of the latest BI features are described below:

Embedded analytics brings all the data analysis tools to one place and helps users visualize and analyze data in their natural workflow.
This feature has increased both speed and ease of access, allowing the data analysts to focus less on assembling the resources and more on the business problem at hand.

Cross-Tenant sharing enables businesses to share their data sets with their tenants (customers) via BI platforms.
Now business users can directly connect to and access source data systems, perform their analysis, and share data with external partners or vendors, or consultants.

AI capabilities form part of modern BI tools. Business intelligence tools are now also providing pre-trained ML models available to ease the data preparation steps.
Power BI is currently providing text analytics and vision algorithms allowing users to perform sentiment analysis, image tagging, keyphrase extraction, and language detection.

Low-code or no-code tools are now being introduced by companies like Microsoft to provide users with app development and business intelligence automation. This improves the export, import, and customization process.
Power Automate offers natural language AI functionality that users can easily use to specify their required workflows. The tool can listen to and process the user’s commands. 

Business Intelligence as a Service

The main constituents of any business intelligence solution are data extraction, organization, and insights. For new businesses to get BI solutions, the first step used to be setting up a data warehouse and aggregating data there. It was a costly process for new businesses to implement.

With cloud computing and modern tools powered by AI, businesses can be provided with an end-to-end business intelligence solution. Now setting up a data warehouse for getting started isn’t a headache anymore. The whole business intelligence system can be provided as a service today without the user having to invest separately in data extraction or organization steps. 

Business intelligence as a service is not like software as a service. Tools provided by Software as a Service (SaaS) companies, like GoodData or Birst, do offer user interface (UI) and analytics tools with capabilities for a business intelligence solution, but they do not provide data extraction or preparation services, only the analysis. Now, business intelligence is available as a complete service and covers everything a new business requires for BI.

Business Intelligence vs. Data Analytics–Which One is Better?

Business intelligence and data analytics are both interconnected but focus on different problems. Business intelligence provides business leaders with actionable insights to make data-driven decisions. Data analytics, on the other hand, goes deep into the data and finds trends and patterns that could tell about the past and help make predictions for the future.

To understand the difference between business intelligence and data analytics, we need to look closely at the problems they focus on.

What vs. WhyBusiness intelligence answers the “what” questions, while data analytics focuses on the “why” questions. 

What is the best-selling product? What were the sales figures during the last quarter? What is the key target area to focus on for the future? All these questions get answered by BI. BI dashboards are very helpful in providing these insights to decision makers.

Why is product A getting more sales? Why is product B not selling well? Why are the customers switching to X service? All such questions come in data analytics and analysis.

Past vs. FutureBusiness intelligence works with historical data to focus on past trends and patterns to draw conclusions. In contrast, data analytics works on extrapolating data trends to provide predictive analytics.

Using Insights vs. Creating Insights—BI is more focused on results and definite values, whereas data analytics provides insights into the business.

Non-technical Users vs. Technical Users—BI is used by industry leaders and key decision makers, while data scientists and analysts practice data analytics.

Structured vs. Unstructured Data—BI uses structured data stored in databases, while data analytics uses both structured and unstructured data to get more insights.

It is not a question of which is better, as both arguably carry equal importance and must be used together for better decisions. While business intelligence is a more sure way of looking at things, data analytics informs the decision-makers in a way that can help them be more prepared for changes. Data analytics, in fact, is a tool used by BI.

The Rapid Evolution of BI Security

With the data on cloud-based servers easily made accessible to BI tools, the efficiency in the field did increase, but it created many more security concerns. Any unauthorized access to the BI platform could end in a big catastrophe for the business. Some of such security scenarios are:

BI platforms, now after the introduction of cross-tenant data sharing features, are at even greater security risks as not only their data and BI systems are at stake but also their customers and the customer’s clients’ information security as well. 

BI platforms can now provide service to a chain of clients, and the longer this chain gets, the more security risk it becomes. This was not the case when data warehouses or data servers were just locally maintained. Now BI security is becoming a vital service itself.

The presence of sensitive data on these platforms also adds more cause for caution. Unstructured data can contain a person’s sensitive data or highly-confidential business secrets. A breach of the data means a breach of the trust that consumers have, which has a damaging impact on the business.

Data security, therefore, is now more important than ever. Organizations are now making information security mandatory to preemptively root out any chance of data breach. For this, organizations are going for methods like ethical hacking as well.

The important measures for BI security can be:

  • Data encryption with SSL/TLS encryption
  • Data masking using predefined data rules to mask sensitive data
  • Cloud security
  • Data regulation certifications

With smart strategies and better infrastructure designs, BI security can be assured. Good ETL vendors now provide help with building such secure pipelines.

Some Key BI Trends in 2022 & What The Future Holds for BI?

Covid-19 brought a significant change in how businesses looked at data. Data was still significant at that time, but the scale shifted entirely to a new level, with global quarantine pushing leaders toward prioritization of online selling, online learning, and remote workflow. Business intelligence is now shaping and driving business in 2022. 

As indicated by recent BI summits and surveys, the key BI trends are:

Data Literacy—Organizations are now implementing data analysis and analytics across their hierarchy to help data inform every step of the project. With the introduction of self-service BI, employees need to be delegated BI tasks for which relevant understanding is fundamental. Data-driven organizations are, therefore, putting stress on data literacy.

Cloud Adoption—Covid-19 and global quarantine initially brought a significant setback to business and workforce culture. Leaders had no choice but to adapt to cloud-based approaches to survive and move forward. After realizing how effective this change in approach has been in terms of profit and loss margins, companies are now reallocating their budgets to adopting cloud-based integration.

Data Visualization and Storytelling—Organizations are now using data visualization and storytelling even more to engage clients via key insights. BI dashboards are even more so in trend with AI-ML integration and more options for users to utilize.

Autonomous BI—The advancements in modern BI tools have made business intelligence very autonomous. A user doesn’t have to wait for the IT team’s insights and can do ad-hoc reporting using the current self-service BI tools.

Use of NLP—Natural Language Processing (NLP) is a bridge between humans and computers as it transforms speech into information that systems can process. Its application has brought in the use of voice-activated assistants. Users can now create their BI workflow by just voicing their commands to the AI-powered BI tools.

Data Quality ManagementBig data is driving businesses today as it helps in providing actionable insights to decision-makers. It can also destroy a business if the data is inaccurate. Therefore data quality is being stressed, now more than ever, by corporations. In fact, it has been ranked as the most important trend in the last five years.

Recent BI trends indicate that the impact and influence of BI in businesses will remain dominant in the future, with more enterprises adopting it to their management systems for competitive advantage or just to survive in the competition.

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