- Sisense
- Looker
- Datapine
- Zoho Analytics
- Yellowfin

Applications of Expert System
- Design Domain
- Medical Domain
- Knowledge Domain
- Monitoring Systems
- Process Control Systems
- Finance/Commerce

Mostly, in such search techniques, the end objective may be an optimization function where the genetic algorithm may be applied for Fast Search through a huge pool of possible solutions so as to find the most optimal solution fast, without compromising the fitness of purpose too much.

Similarly, problems which are often solved using genetic algorithms include time-table scheduling and job-scheduling problems. Many scheduling software packages use Genetic Algorithms as a predictor model. GAs have also been applied to classic engineering disciplines besides the information technology, communications, telecommunications, electronics and semiconductor industry. Genetic algorithms are also popular for usage as an approach to solve global optimization problems.

Key ideas of Genetic Algorithm:
1 – Genetic Algorithms (GA) Can Optimize Solutions in Minutes
2 – Iteration Is Key—a GA Learns and Improves Over Time

 

OLAP transforms raw data so that it reflects the real dimensionality of the enterprise as understood by the user. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making. 

It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. 

Multidimensional views are inherently representative of an actual business model. Rarely is a business model limited to fewer than three dimensions. Managers typically look at financial data by scenario (for example, actual versus budget), organization, line items, and time; and at sales data by product, geography, channel, and time.

For example, the simple process of addition is an algorithm. We have math laws that tell us how to perform addition and they always work exactly the same. Two plus two is always four.

Now consider facial recognition. If you see a friend at a distance partially turned away from you, 99% of the time you can correctly identify your friend, but sometimes you get it wrong and maybe it’s just someone that looks like your friend. Your brain is using heuristics.

 

Supervised learning learns from a set of labelled data and generate a model which is capable of label unknown dataset based on the generalization of the labelled dataset. Examples of supervised learning include regression and classification. 

2. Unsupervised Learning
Unsupervised Learning extracts knowledge/patterns from data without labelled data.  

Algorithms such as clustering can be applied on unlabelled dataset to cluster different clusters of data based on the attributes of the data.

Applications of ML in BI:
1. Market Basket Analysis - Association Rule Learning is used to know what combination of products are frequently purchased together. Knowing the trend will help with optimal product placement and thus increase sales by reducing purchasing friction.
2. Fraud Analysis - Banks often use ML algorithms to detect potential fraud transactions for customers thus preventing further losses by customer.

3. 24-h realtime customer support - Virtual assistant or chatbot is widely used by companies to handle basic inquiries, orders, and so on from customers from all around the world in this globalised economy. Examples: Facebook Messenger Bot.
(Further Reading: https://www.thoughtspot.com/data-chief/5-ways-machine-learning-can-make-your-bi-better)

 

The use of CASE tools is recommended because they:
(a) Maximize the consistency between the design and the implementation
(b) Minimize human error incurred by translating the design into source code
(c) Shorten the time spent on the development of source code
(d) Facilitate the implementation of change control procedures 

IoT can be linked with business intelligence (BI), providing the possibility to analyze data and get valuable information. 

Example of IoT solutions:

1) Predictive analytics which can add real value to business. IoT will be responsible for collecting large data volumes and tracking things, while BI part will go to processing data and making smart predictions.

2) Prescriptive analytics. The core thing here is to make right and on-time recommendations (so-called prescriptions), thus allowing to improve decision-making, save costs, and avoid failures.

Advantages of connecting IoT and BI

1) Improve anomaly detection. The tool will notify about any kinds of changes and suspicious activities that take place during every 24 hours. If there are some anomalies and other things that are hard to detect, the tool can also use an integrated Machine Learning module, that will easily catch them up. 

2) Provide businesses with comprehensive monitoring statistics, real-time data calculates user-defined KPIs (Key Performance Indicators), always keeps you updated on your strengths and weaknesses.

3) Generate accurate individually visualized reports, that can schedule according to the company needs.