Artificial Intelligence has existed for years; its development, consequences, and application have been sincerely taken care of. While Business Intelligence is in talks right now, it is slowly finding its way out in every field and branch.
In this article, we shall discuss how they differ from each other, their compatibility, and their complementary nature. Artificial intelligence can bridge the gap between the users who wish to take part in analytics but are unaware of the technical expertise.
How are AI and BI different?
The fundamental difference between Artificial Intelligence and Business Intelligence is that the former believes in building software. It is essentially a computer-controlled machine that can think like a human, while Business Intelligence requires humans to be an integral part.
Business Intelligence is the technology & the tools used to store and analyze data to create information from it and help users make better decisions. This is where their goals differ as well and so do their areas of contribution.
What exactly are their Areas of Contribution?
Gaming, Natural language processing, Speech recognition Expert, and Vision systems are some of the AI applications.
- Natural language processing is the method in which machines easily capture human language. It ensures easy translation of the texts into actions.
- The vision system has been an outstanding innovation that has made inspecting moving objects, their identification, and recognition easier.
- Search recognition is a unique development that uses sound vibrations and converts them into digital format for the computer to understand.
At present, AI is being used in multiple fields such as Healthcare, Navigation, e-commerce, Robotics, Human resource, Agriculture.
Spreadsheets, Dashboards, Data Mining, Querying, Data warehouse are Business Intelligence applications as illustrated above.
- Data warehousing, as the name suggests, is a collection of data from multiple sources. It acts as an electronic library of business data from where necessary pieces of raw data can be retrieved at any time for further decision making and analytics.
- Benchmarking is one of the most critical parts of Data Analytics. It helps in critically evaluating the performance with the industry standards. Performance metrics such as quality, cost, and time involved are compared to judge respective business processes.
- Business Process Reengineering is the change in management specific to the immediate business requirements.
- Data Querying enables users for quick recovery of data and extraction of the information that shall be useful in the model.
- Online Analytical Processing (OLAP) is quite like the extraction of data, but it also helps to analyze them from different points of view.
These are some of the present applications of BI and in different fields. BI is further moving forward to incorporate Data Mining in Social networks, working with high-velocity data, OLAP, and more.
AI and BI as a team
Artificial Intelligence has made working with data more effortless, but it is difficult for such a system to integrate itself with the business processes. It might not be able to incorporate the ethics and values of the company and fail to create a report that has a human touch.
This is precisely where AI and BI team up. AI, on the one hand, helps in the quick extraction of data, it’s gathering, and testing, while BI helps users to transform it into real-time reports.
The evolution of Business Intelligence is shown in the flow chart below. From specific purpose tools to Augmented Intelligence, technology stands in its 3rd Generation in 2020.
The ease of use increases significantly when AI is incorporated with the BI tools, as shown in the figure below. AI-based software can efficiently run multiple calculations and models at the same time and instantly suggest the optimal way of working with the same.
Source: IBM Research Sponsored Publication
Additionally, it can be seen as a conversing tool with the data. AI is capable enough of determining the KPIs and explaining them to the business users. It will be a learning platform with multiple insights from different attributes at the same time.
The functions, tools, and quality of reports have seen a notable change in the discussed field, from descriptive reports to visualizations and more. The amount of manual work is decreasing and with recommended actions and insights, the quality and detail of reports are excellent today.
Source: IBM Research Sponsored Publication
AI fits in the BI landscape through automation. The algorithms run in the background and perform automatic cleansing of data, removal of duplicates, or suggestion of appropriate visualization. For instance, tableau comes with the ASK feature that enables the user to do the same.
Auto charting, Auto dashboard creation, Chatbot integration, Root-cause analysis, Time series detection are some of the features that have come up with the integration of AI into the BI world.
When AI-enabled BI?
Instances where such a platform comes handy:
- Voluminous data bursting from diverse sources in the BI tools: AI can help in quick and easy tailoring of the same.
- Irregular growth of Big Data: AI shall help check this growth and keep on breaking it into pieces from time to time for correct insights.
- If a decision is to be made promptly using voluminous data: AI shall perform the task much faster than humans.
- Lack of data analytics in a firm: This would admittedly, require the recruitment of a specialist who has knowledge about the same, but AI-enabled BI shall help save time and solve the problem.
Therefore, we can argue AI can fill in the gaps that arise from human error or human attributes such as processing time, tiredness, unavailability of human capital, or the lack of knowledge about technical expertise.
Additionally, suppose the essential part of data analytics such as data extraction, its cleaning and arranging can done using AI!! In that case, it shall free up the employees from the mundane work & allow space for more creative/strategic work.