AI and BI: A Perfect Match

The latest analytical tools, including AI, machine learning, working with natural language, are being used now across all industries. BI helps professionals and ordinary employees working with information to automate many aspects of data research, as well as the development and use of data models. Let’s discover how AI is revolutionizing BI with machine learning algorithms and natural language processing.

AI and BI Symbiosis 

Today, there are practically no separate solutions to adjust the analysis of cross-cutting indicators that evaluate the work of several departments and their relationship (including implicit) of an enterprise. AI does not need formulas and algorithms - this is its serious advantage over classical systems. It can fast enough, based only on statistics, perform monitoring of indicators determining implicit relationships and dependencies that affect the work of the entire company. 

Business intelligence is a designation of computer methods and tools for organizations that provide the translation of transactional business information in a human-readable form suitable for business analysis, as well as the means for mass work with such processed information. The purpose of BI is to interpret a large amount of data, focusing only on key performance factors, simulating the outcome of various options for action, tracking the results of decision-making. AI can act as an application and addition to BI-systems, and work independently, integrating into any corporate information system at the output level. It can take on not only the function of analysis but also forecasting future problems and even reorganizing the business.

Below are several examples of how AI can help automate specific tasks. It is possible due to improved actions related to decision-making processes.

1. Detects. AI is able to identify problems with the quality of data, suggest data sources, track those that are used more often and more efficiently.

2. Describes. AI helps identify those metrics and KPIs that are better suited to describe the data you need. AI is also able to offer more appropriate visualization methods.

3. Diagnoses. AI gives direction to research aimed at finding causes, that is, it helps to find the answer to the question “why?”.

4. Predicts. AI can help automate tasks that often require a lot of time for data researchers because they are performed manually. Among them: data collection, deployment of a predictive model, model management. AI can tell which algorithms suite the best in each case.

5. Prescribes. One of the most promising applications of AI. People usually cannot determine patterns, find all the dependencies in huge amounts of data, or evaluate all the results for the best effect. AI is well suited for such tasks.

6. Decides. When alternative solutions become available, the AI can evaluate the constraints and relate them to the goals. AI can also be used to develop rules that will help automate the decision-making process related to operational activities.

7. Effective. AI can be used to automatically execute individual processes, such as creating content, distributing offers, or making financial transactions.

Machine-Learning Algorithms for Big Data Processing

Big data means a combination of special technologies that are used to process a significantly larger amount of data. There are a large number of techniques and methods for analyzing and processing data. Among the main ones, the following can be distinguished:

• Class Methods or In-Depth Analysis (Data Mining). These methods are based on the use of special mathematical tools in conjunction with achievements from the field of information technology.

• Crowdsourcing. This technique allows you to receive data simultaneously from an almost unlimited number of sources.

• A/B Testing. From the total amount of data, the final set of elements is selected, which is alternately compared with other similar sets, where one of the elements was changed, which helps to determine which changes in which parameter have the greatest impact on the set.

• Predictive Analytics. This method is aimed at predicting and planning how the controlled object will behave in order to make the most favorable decision in this situation.

• Machine Learning (AI). The method is based on an empirical analysis of information and the subsequent construction of self-learning algorithms for systems.

• Network Analysis. After receiving the statistical data, the nodes created in the grid are analyzed, that is, the interactions between individual users and their communities.

Machine learning is a type of AI that uses algorithms to study data. Instead of acting according to the program, these algorithms build a model on the basis of the obtained data and, utilizing the intermediate data of their analysis, offer solutions or forecasts. 

As noted in the list above, machine learning is one of the big data processing methods. It eliminates the need for a programmer to explain in detail to a computer how to resolve a problem. Instead, the computer is trained to independently find a solution. The algorithm receives the necessary data and then utilizes it to process requests.  Machines learn to see images and classify them. They can recognize text, numbers, people, and terrain in these images. Computers not only identify distinctive features for sorting but also take into account the context of their use.

Natural Language Processing

Thanks to NLP and machine learning, modern technology can interpret data received in natural language. Let’s take chatbots. If you look at simpler chatbots, any answer will be grammatically perfect as it uses a set of ready-made offers in the database. And therefore, it will not be able to process information if a person made grammatical errors or wrote a sentence that does not match the given templates. Newer and smarter chatbots are “trained” to recognize the natural language and respond accordingly to the situation. Unfortunately, the ability to naturally respond requires a huge amount of training time, and a large amount of data to study a wide variety of possible queries. 

Below is a list of tasks that AI natural language processing should solve. Many of them can be associated with the recognition of both text and speech or even pictures.

• Referencing. The challenge is to create an abstract or summary of a large text. 

• Open and closed questions. Modern chatbots are expected to be ready to answer questions, whether they are open or closed. 

• Mapping. The bot must match objects with words, and understand when different words refer to the same object. 

• Ambiguity. The ambiguity that is often found in natural language phenomena is still a serious problem for bots. Homonymy alone requires that the correct conception be chosen, depending on the context. Some languages are more ambiguous, it is more difficult to analyze expressions in them.

• Morphology. Each language has a special morphology. And the chatbot should be able to divide words into morphemes.

• Semantics. This is the task of determining the meaning of sentences or words in a natural language and the generation of sentences in a natural language. It guarantees the bot the ability to translate a natural language, analyze questions on it, or create an answer.

• Text structure. Associated with text structure and punctuation. 

These elements of natural language processing together provide the development of intelligent bots.

Final Word

Data analysis software developers have always sought to provide the capabilities of their technology to a wider audience of ordinary business users and all those working with information. Without the widespread adoption of novel technologies, business success in the digital age is impossible. The digital transformation trends dictate the rules and you need to act right now to put the latest analytics innovations at the service of your company.