Traditional digital tools for preparing business analysis are handy. They work quickly, efficiently and are perfectly capable of performing their tasks. There is only one problem – they perform their functions only when programmed by humans. That’s when humans provide them with the right data and choose the right process to analyze the information given to the program and draw conclusions from it. For this, business analysts usually spend most of their time preparing the data for analysis. Will the use of artificial intelligence help change this situation?
Will artificial intelligence replace business analysts? – table of contents:
- Will AI replace business analysts? Introduction
- Types of analysis supported by AI
- Will AI replace business analysts? – Summary
Will AI replace business analysts? Introduction
Artificial intelligence-supported business data analysis (BDA, Business Data Analysis) is today an essential part of Business Intelligence system designed to come up with as well as make knowledge-based decisions. Business Intelligence consists of technologies that enable businesses to analyze and manage data to take steps that improve business performance.
Still, is artificial intelligence capable of replacing the work of analysts today? To try to answer this question we’ll need to take a closer look at what AI’s role is in data analysis.
Types of analysis supported by AI
We will describe them one by one, indicating how AI improves the performance of each.
Descriptive analysis, also known as descriptive analysis, is the simplest form of analytics. It involves collecting and organizing historical data, that is, about what has already happened in the company. It usually does not need to use artificial intelligence. AI is used only when analyzing immense bulks of data, or when analysts expect artificial intelligence to uncover new patterns that have not been studied before.
An example of the use of AI-supported descriptive analytics could be the processing of large amounts of customer data using an e-commerce platform to identify moments of purchase abandonment.
Augmented analytics is a tool that supports analysts in tasks such as preparing data for analysis or visualizing results through various charts, tables and presentations. Based on the AI-prepared data, an analyst can more easily draw conclusions from the collected material without the help of a team to input and classify information.
An interesting example of augmented analytics concerns its application in the agricultural industry. Artificial intelligence can collect and classify data from various sources and measurement tools, such as those on water and fertilizer consumption as well as temperature and plant growth. It will then present them in a human-accessible form, making it easier to draw conclusions from its methods and make business decisions.
Predictive analytics, focuses on finding patterns in existing data so that more accurate decisions can be made based on it and potential risks can be identified. Artificial intelligence features statistical modeling, machine learning (ML, Machine Learning), and Data Mining techniques to effectively predict future events.
Among other applications, it features in enterprise resource planning (ERP). For example, it allows reducing the need to stockpile raw materials and spare parts. It also makes it possible to create an optimal calendar for maintenance work. Moreover, it helps determine staffing needs and market demand for products over a given period of time.
Pescriptive analysis, otherwise known as prescriptive, like all the above collects data on past situations. However, its purpose is the most complex, and its operation is the most dependent on artificial intelligence. This is because it is about indicating the best behavior in a given business situation.
Although the results of the prescriptive analysis are very valuable and promising, getting it right is very difficult. Firstly, it requires the collection of a huge amount of data. Therefore, it is only performed by larger companies.
When performing prescriptive analysis, artificial intelligence usually draws in data obtained through descriptive and predictive analysis, which we wrote about above. It draws conclusions from the collected information using Machine Learning (ML). This allows AI to suggest, for example, a strategy for publishing content, or to plan an effective advertising campaign.
Will AI replace business analysts? – Summary
Business analysts working in small and medium-sized enterprises can still sleep soundly. That is, of course, if they learn on the fly to work with AI tools to support their work, increasing the accuracy of their analysis and the effectiveness of their conclusions.
Artificial intelligence can significantly speed up and facilitate the processes of data collection, classification, and visualization. However, giving suggestions about the future based on a small set of information is still in the hands of experienced analysts.
AI in business:
- Artificial intelligence in business - Introduction
- Threats and opportunities of AI in business (part 1)
- Threats and opportunities of AI in business (part 2)
- AI applications in business - overview
- What is NLP, or natural language processing in business
- Automatic document processing
- AI and social media – what do they say about us?
- Automatic translator. Intelligent localization of digital products
- AI-assisted text chatbots
- The operation and business applications of voicebots
- Virtual assistant technology, or how to talk to AI?
- Business NLP today and tomorrow
- How can artificial intelligence help with BPM?
- Will artificial intelligence replace business analysts?
- The role of AI in business decision-making
- What is Business Intelligence?
- Scheduling social media posts. How can AI help?
- Automated social media posts