The need for data-driven decision-making to guide strategic business decisions throughout an organization has never been greater. Today, companies collect enormous amounts of data, coming in from their operational, ERP, CRM, marketing, and social media systems. They realize that their data is extremely valuable. In some ways, it’s the lifeblood of their company. Utilizing it to the fullest is one of the most important jobs of a company executive. But how do executives utilize their company’s data to best align their strategic goals? Many are finding Business intelligence (BI) tools have their place; the addition of artificial intelligence (AI) into these BI tools makes them considerably more effective in both understanding the business on a deep, granular level as well as finding new profitable avenues to explore.
BI tools generally employ descriptive analytics to answer questions like "What happened?" and "Why did it happen?" They aim to summarize and understand historical patterns in data that can reveal business trends. AI tools, on the other hand, focus on predictive and prescriptive analytics, using advanced algorithms and complex models to make predictions, recommend actions, as well as optimize outcomes. Going beyond historical analysis, AI tools provide answers to questions like "What will happen?" and "What should we do about a particular business event?"
Business Intelligence Tools Overview
BI tools allow businesses to collect, secure, organize, administer, and analyze their data, ultimately providing visualization capabilities that surface current and historical data into easy-to-understand dashboards. Users gain insights into the performance of the business, which helps increase data-driven decision-making throughout the organization.
Using predefined queries, aggregations, calculations and analytics, business users can explore, visualize, and report on company-wide information. BI tools typically employ techniques such as data mining, data cleansing, Extract, Transform, and Load (ETL) processes, and data visualization to enable users to monitor key performance indicators (KPIs), identify corporate trends, as well as understand the business in a highly granular way. BI tools first organize the data, then analyze and surface it into a user interface via a data visualization reporting tool, which provides users with quick access to the company’s information.
BI Use Cases
BI tools allow businesses to understand their data in a diagnostic and descriptive analytics way, i.e., explaining what has happened and why it might have happened. For example, BI tools can help marketers track campaign metrics, in some cases in real-time. They can measure a campaign's overall success and drilldown to reveal how effective an individual marketing campaign was. This can provide performance metrics that can help with future advertising campaigns. Sales managers can monitor revenue targets, their reps performance, and the success of particular sales incentives. They can keep track of important sales KPIs as well. The operations department can access supply chain data to ensure the system runs optimally. Powerful in their own right, BI tools become much more valuable when adding predictive analytics, prescriptive analytics, or AI component.
Artificial Intelligence Tools Overview
According to the analytics pioneer, SAS, “Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.” It is a branch of computer science dealing with the simulation of intelligent behavior in machines. The backbone of technologies like robotics, machine learning, deep learning, natural language processing, and Large Language Models (LLMs), AI is more profound than “electricity or fire,” claims Sundar Pichai, the CEO of Google. He believes AI will be one of the most important technologies ever devised by man.
AI tools can process and analyze vast amounts of data, then find patterns in the data which normal BI tools just can’t decipher. AI tools can learn from experience, working with the company’s most recently captured data to make predictions as well as optimize system processes. AI can utilize complex modeling systems such as natural language processing (NLP), speech recognition, machine translation, computer vision, and predictive analytics, which enables businesses to automate their processes, enhance their customer experience, improve their operations, optimize their budgets, and uncover new opportunities that might drive future company profits. AI adds substantial value above and beyond normal analytics.
AI Use Cases
Because of AI’s ability to learn from experience, it can adapt through progressive learning algorithms that let the data do the programming. AI finds structure and regularities in data so that algorithms can acquire skills. Just as an algorithm can teach itself to play a game like Go or chess, AI can teach itself which product to recommend to a potential customer the next time he or she is seen online. Since AI models continuously adapt to new information, AI systems can constantly adjust to a changing marketing landscape, recognizing what is working and what isn’t. It can send out the right offer at the right price at the right time to make the chance of a sale as likely as possible.
With its ability to utilize modeling in both a structured and unstructured way, AI allows companies to build complex algorithms to solve all kinds of business problems. Supervised learning regression models allow marketers to understand how their advertisements are performing and what might need to be tweaked to increase sales. These models can reveal the true worth of a customer when looked at holistically. They also provide manufacturers with predictive maintenance capabilities so parts can be replaced before they fail, something incredibly important in the transportation industry.
Classification models help identify key patterns in purchasing which could help with market basket analysis. These models can also increase customer retention. Unsupervised learning models help with customer segmentation, target marketing, and recommender systems. For example, as part of a company’s marketing department, AI tools can identify buying patterns and make predictions about a customer’s potential purchase, then send out offers the AI deems most likely be used. This could, ultimately, increase the likelihood of a sale. AI tools continuously refine their models, improving predictions, and adapting to changing market conditions with limited or no human intervention, something BI systems can’t do.
BI and AI Tools Key Differences
Many of today’s BI tools have built-in AI, machine learning, and deep learning capabilities, but specialized AI platforms like TensorFlow, PyTorch, Keras, Scikit-learn, Microsoft Azure Machine Learning, IBM Watson Studio, and Amazon SageMaker provide frameworks that allow advanced AI algorithms. These can simulate human intelligence and automate tasks far more complex than what standard BI tools can do. These AI tools automate tasks, predict outcomes, as well as make intelligent decisions based on the data they are trained on.
Although BI and AI tools are distinct, they build upon each other. Without BI, AI wouldn’t be possible. Collecting, securing, organizing, and cleansing data is a necessary part of the AI process. While BI tools help users analyze data and visualize reports, human input and interpretation is required. They have few automation capabilities beyond capturing and cleansing data, then delivering reports.
AI tools, however, can automate complex tasks, gain insight from data patterns, and make intelligent decisions without explicit human intervention. The deluge of data coming in from highly unstructured data sources, like social media and big data, have proven too complex for most BI tools to handle. This is really where AI truly shines. BI can easily handle structured and semi-structured data, but AI is required when unstructured or big data is involved.
BI and AI Tools Synergies
In 2018, Gartner added “Analytics” to the name of its report on BI tools; the “Magic Quadrant for Business Intelligence” became the “Magic Quadrant for Analytics and Business Intelligence Platforms,” which reflected the growing importance of advanced analytics, AI, machine learning, and other advanced analytical capabilities to all BI platforms. Before 2018, “Gartner's Magic Quadrant for BI” evaluated BI platforms on their ability to deliver traditional reporting, querying, and data visualization capabilities, but afterwards analytics and AI capabilities became an important element of the rankings.
Qlik’s AutoML feature is an example of how BI tools are integrating AI into their platforms today. AutoML is a machine learning tool that adds the power of predictive analytics and explainable AI to its Qlik’s award-winning software. According to Qlik, AutoML lets users easily generate models, make predictions, and test what-if scenarios using a simple, code-free experience. All of the other BI vendors are adding similar features to their tools. No one wants to be left behind.
Utilizing AI, today’s BI tools go beyond simple analytics. They can handle the most difficult yet most valuable type of analytics, predictive and prescriptive analytics. These types of analytics use algorithms to optimize labor management as well as provide pricing data on such highly perishable items as airline seats, hotel rooms, amusement park entrance tickets, and even casino table game seats. These optimization models take into account both the ticket supply and inherent demand of the items, while also factoring in seasonal and even weather changes that might affect demand. These models can help businesses sell their items at the highest possible price available, thereby increasing profits substantially. Businesses are finding that these profits can easily cover the initial investments made in the AI and BI tools.
Final Thoughts on BI and AI
A few years ago, the BI software industry recognized users were demanding more sophisticated analytical capabilities, so it shifted away from simply leveraging data-driven insights and added high-level analytics and AI capabilities to their solution. These provided insights into the ever-increasing amount of data flowing into a company’s data warehouse and it wasn’t a moment too soon as the big data revolution was transitioning to a world of both big and fast data.
Whereas BI tools allow businesses to understand their data in a diagnostic and descriptive analytics way, AI tools provide predictive and prescriptive analysis on a company’s data. These processes can explain both why something happened as well as reveal how a business can ensure they will continuously happen, thereby potentially increasing profitability.
In 2018, Gartner recognized analytics was becoming a big part of the BI world and renamed its report on BI tools and the “Magic Quadrant for Business Intelligence” accordingly. Today, the markets for AI and BI software are converging. The software vendors are in a race to make their tools as sophisticated as possible, and they are aware of the threat from open-source tools like R and Python presents. High-level analytics incorporated within a BI tool has become a necessity rather than a nice-to-have. This is great news for the consumer because these vendors are producing highly sophisticated, powerful, and easily scalable software that is also easy-to-use. A win-win for all.
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