4 types of data analysis. The role of AI in decision-making processes.

How can you harness the power of artificial intelligence to make your business decisions based on detailed data and even more relevant? We’ll take a look at the types of data analysis and how they can be supported by AI, as well as the tools that will revolutionize the way you view data.

Data analysis

4 types of data analysis supported by AI

The most important types of data analysis that artificial intelligence can support are:

Descriptive analysis – also known as descriptive analysis, is the simplest form of analytics. It involves collecting and organizing historical data, i.e. on what has already happened in the company. It usually does not need to use artificial intelligence. AI is used only when very large amounts of data are being analyzed, or when analysts expect artificial intelligence to uncover new patterns that have not been studied before.
Augmented analytics – is a tool that supports analysts in tasks such as compiling data for analysis or visualizing results through various charts, tables and presentations. Based on the AI-prepared data, an analyst can more easily conclude the collected material without the help of a team to input and classify information. One can help here with the free ChatGPT tool, or use freemium options such as Visme or Datawrapper.

Example of data visualization.

Source: academy.datawrapper.de

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 uses statistical modeling, machine learning (ML, Machine Learning), and Data Mining techniques to predict future events.
Prescriptive analytics – otherwise known as prescriptive analytics, 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.

Decision making – human vs. AI

The basis for making accurate decisions of any type is knowledge of the relationship between events and processes. Both humans and artificial intelligence trying to predict the future have some chance of success by collecting and analyzing data on the past.

Statistically, the chances of making a more accurate decision are given by a more closed system, that is a situation that is not subject to external influences. The chances of success are also increased by a more extensive data set describing in various ways similar past relationships.

Artificial intelligence has an advantage over humans because it can analyze much larger amounts of data and see patterns in it that are invisible to the human eye. AI can, for example:

see cyclical changes in demand for the company’s services dependent on location,
more accurately analyze market information consisting of a variety of data,
fish out the candidate’s optimal combination of skills for the company from a visually unattractive resume.

However, a human has the advantage over artificial intelligence that when making decisions he can take into account external factors whose impact on the company’s situation may not be obvious or indirect. A human interpreting data can:

consider the ethical, social and legal aspects of their choices,
question and critically evaluate their assumptions and conclusions,
take into account existing relationships with customers and business partners.

Decision-making methods

To cope with the risks, uncertainties and responsibilities associated with making business decisions, companies are adopting methods to make the process easier and more orderly. These include:

The Eisenhower Matrix – is a simple task prioritization technique based on axes of urgency and importance. It enables you to divide tasks into 4 categories:

Urgent and important – require immediate implementation.
Important but non-urgent – you should plan a deadline for their implementation.
Urgent but unimportant – can be delegated to someone else or skipped altogether.
Neither urgent nor important – unnecessary, time-absorbing.

AI can help business analysts who employ the Eisenhower matrix to automatically categorize analytical tasks by urgency and importance, making prioritization and planning easier.

SPADE (Spanning-tree Progression Analysis of Density-normalized Events) – a multifaceted framework that emphasizes single-person accountability for decisions based on sharing the experience of the entire team. It’s a tool used in business, but also in medical diagnostics. AI can support search by data analysis, simulating options and algorithmically modeling the consequences of each decision.
Agile Inception – creates a framework for the first conceptual and decision-making phase of the agile team’s work. Its main moments are:

Define product vision and business objectives.
Analysis of options and risks, prototyping of solutions.
Selecting the best ideas and determining the MVP.

AI can model risks, simulate options and recommend the best prototypes based on the data.

Integrated Thinking – which is a method that focuses on the exploration of possibilities and rapid prototyping of solutions, where tools such as ChatGPT or Google Bard will work well.

4 decision making areas supported by AI

Artificial intelligence is used for both simple but labor-intensive data analysis decisions and those that require handling large data sets. These include:

Entering documents into databases – even in situations where they are delivered to the company in paper form or contain incomplete or poorly structured data, AI can accurately organize the information and decide which collection the document belongs to,
answering questions asked in natural language – decision-making makes artificial intelligence able to accurately respond to questions asked, and take the initiative by asking follow-up questions,
Business process management – in the case of incomplete data, AI can decide to move on to one of the cliques of alternative next steps included in the process map
Process automation – the action of artificial intelligence enables the automation of workflows between the various programs serving the company.

The best AI tools for business data analysis

Below is the latest generation of tools that can help with the most difficult of data analysis – prescriptive analysis, answering the question of what needs to be done to improve results based on the data. None of them will decide on their own, but their capabilities significantly facilitate an objective and multifaceted approach to data.

ChatGPT Code Interpreter – a tool available to ChatGPT Plus subscribers that features analysis, visualization and interpretation of data of up to 170 MB. Its biggest advantage is that it accurately adapts to the questioner’s commands, while the disadvantage is the need to prepare the data for analysis in another program. However, a Code Interpreter can deal with repeated lines, inaccurate data, and unit inaccuracies, detect outliers, check for errors, clean, preprocess, inspect and visualize data. AI handles structured data exceptionally well. You can upload Excel spreadsheets, CSV files, etc., and have the Code Interpreter describe, process, evaluate, visualize and interpret the data.
Tableau – offers an “Ask Data” function that enters a natural language query and then automatically generates the appropriate data visualizations. It employs AI to understand the user’s query and provide a data-driven response. Tableau also offers other AI-based features, such as “Explain Data,” which automatically interprets data and provides insights into its meaning.
Improvado – an analytics tool to consolidate marketing and sales data from various sources in one place. One of the main advantages of Improvado is that it allows integration with Google Ads, Facebook Ads, or Salesforce. In addition to creating custom reports and dashboards that allow you to analyze data quickly and easily.

Summary

Data analysis supported by artificial intelligence is opening up a new dimension of possibilities for business decision-making. While AI has the potential to analyze much larger data sets and see hidden patterns in them, it will not replace human judgment and intuition. Collaboration between humans and technology, through the best AI tools, is the key to a future in which decisions are more informed, accurate and based on solid data.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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