Difference Between Artificial Intelligence vs Business Intelligence
Business Intelligence is a technology that is used to gather, store, access and analyzes data to help business users in making better decisions, on the other hand, Artificial Intelligence is a way to make a computer, a computer-controlled robot, or a software that think intelligently like humans. Artificial Intelligence is based on the study that how human thinks, learn, decide and work in order to resolve an issue and then using the outcome of this study as a basis of developing intelligent software and systems.
Head to Head Comparison Between Artificial Intelligence and Business Intelligence (Infographics)
Below is the Top 6 Comparision Between Artificial Intelligence and Business Intelligence:
Artificial Intelligence and Business Intelligence Comparison Table
Following is the comparison table between Artificial Intelligence and Business Intelligence.
|Basis of Comparison||Artificial Intelligence||Business Intelligence|
|Philosophy||AI is started with the intention of creating similar intelligence in machines that we find in humans||It helps in analyzing business performance through data-driven insight i.e understand the past and predict the future|
|Goals||To create expert systems and implement human intelligence in machines||It should provide information that can enable efficient and effective business decisions at all levels of the business.|
|Areas that contribute||Artificial Intelligence is a combination of science and technology based on computer science, maths, Biology, Psychology||It combines business analysis tools which include ad-hoc analytics, enterprise
reporting, OLAP(online analytical processing)
|Applications||Artificial Intelligence is used in various fields such as Gaming, Natural language processing, Expert systems, Vision systems, Speech recognition, Handwriting recognition, Intelligent Robots.||It is used in Spreadsheets, querying and reporting software, Digital dashboards, Data mining, Data warehouse, Business activity monitoring.|
|Research Areas||Research areas for Artificial Intelligence are Expert systems, Neural networks Natural language processing, Fuzzy logic, Robotics.||Research areas for Business Intelligence include Data mining in social networks, process analytics, Bigdata, OLAP|
|Issues||Artificial Intelligence faces three issues. They are Threat to Privacy, Threat to Human dignity, Threat to safety.||Business Intelligence issues are classified into two types. They are Organization and People and Technology and data|
Algorithms in Artificial Intelligence vs Business Intelligence
The Algorithms in Artificial Intelligence and Business Intelligence are as explained below:
|Artificial Intelligence Algorithms||Business Intelligence Algorithms|
|Breadth-first search algorithm
It starts from root node and explores neighbor nodes first and moves to the next level neighbor nodes.It provides the shortest path to the solution and can be implemented using FIFO
|Decision Tree Algorithm
This extract the predictive information in the form of human-understandable rules and these rules can be if-then-else which leads to the predictive information
|Depth First Search Algorithm
This algorithm is implemented using LIFO(Last in first out)data structure.It creates nodes same as breadth-first search but it differs in only order.In each iteration, it stores the nodes from root to leaf and also it cannot check duplicate nodes.
It makes predictions by using Bayes algorithm, which derives probability prediction from the underlying evidence, as observed in data.
|Uniform Cost Search Algorithm
In this algorithm, sorting is done in increasing cost of the path to a node.It always expands the least cost node.This search is identical to the Breadth-first search if each transition has the same cost.It explores the path in the increasing order of cost.
|Generalized Linear models
It implements logistic regression for classification of binary targets and linear regression for continuous targets.It supports confidence bounds for prediction probabilities and also supports confidence bounds for prediction.
|Iterative Deepening Depth-first Search
It performs the depth-first search at level-1 and starts over, then executes a complete depth-first search to level 2, and continues till it gets the solution.
|Minimum Description Length
It is an information theoretic model selection principle.It assumes that most simple, compact representation of data is the best way to explain the data
|Pure Heuristic Search
It expands nodes in the order of their Heuristic values.It creates two lists, a closed list for the already expanded nodes and an open list for the created but unexpanded nodes.In this, the shorter paths are saved and longer paths are disposed of.
It is a distance-based clustering algorithm that partitions the data into a pre-determined number of clusters.Each cluster has a centroid
|Travelling Salesman Problem
In this algorithm, the main aim is to find a low-cost tour that starts from a city, visits all cities en-route exactly once and ends at the same city starting.
It performs market-based analysis by discovering co-occurring items within a set.This algorithm finds rules with support greater than a specified minimum support and confidence greater than a specified minimum confidence.
It is an iterative algorithm that starts with an arbitrary solution to a problem and attempts to find a better solution by changing a single element of the solution incrementally.If that change produces a better solution, an incremental change is taken as a new solution.This process is repeated until there is no further improvements.
|Support Vector Machine
Distinct versions of SVM use different kernel functions to handle different types of data sets.Linear and Gaussian(non-linear) kernels are supported.SVM classification attempts to separate the target classes with the widest possible margin.SVM regression tries to find a continuous function such that the maximum number of data points lie within an epsilon-wide tube around it.
|There are other algorithms like Simulated annealing, Local beam search, A* Search, Bidirectional search.||BI supports/uses Non-negative Matrix Factorization, One class Support vector machine, Orthogonal Partitioning clustering, Maximum Entropy.|
Integration of Artificial Intelligence vs Business intelligence
Artificial Intelligence and Business Intelligence are a perfect match. Artificial Intelligence and Business Intelligence is witnessed through AI-powered alerts, from basic threshold-alerts to advanced neural network alerts and helps a business stay in full control of key success factors by alarming them as soon as something takes place. When combined with innovative business dashboards these AI advances will continue to revolutionalize the business intelligence landscape. All of this businesses to step away from the time-intensive process of digging through data to unearth trends and reacting to costly issues.
Artificial Intelligence is at the center of a new enterprise to build a computational model of intelligence. The main assumption is that intelligence of human can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer.Business Intelligence makes it possible for groups within an organization to gain actionable insight from business data, and to leverage these insights to meet criteria. Business Intelligence solutions offer business focussed analysis at a scale, complexity, and speed i.e not achievable with basic operational systems reporting or spreadsheet analysis, thereby delivering significant value.
This has been a guide to Artificial Intelligence vs Business intelligence. Here we have discussed Artificial Intelligence vs Business intelligence head to head comparison, key difference along with infographics and comparison table. You may also look at the following articles to learn more –
- Business Intelligence VS Data Mining – Which One Is More Useful
- 12 Important Business Intelligence Tools (Benefits)
- 5 Best Thing You Must Know About Business Intelligence vs Data Warehouse