Publication Date
Enhancing your data for artificial intelligence
The key for companies to use artificial intelligence successfully is data that’s mature and properly prepared. This requires investments in technology, capacity building and strategic planning, but the benefits are worth the effort.
Raw data analysis
Any initiative seeking to take full advantage of artificial intelligence must begin by recognizing and assessing the maturity of the existing data. Omitting this will result in cumulative negative effects over the course of your usage.
The first step consists of analyzing your raw corporate data, extracted from various sources. This includes information like transactions, customer interactions, production data, etc. Due to its non-structured format, raw data is often difficult to analyze and leverage directly.
This data requires deep cleaning to eliminate doubles and errors, and to ensure its overall quality. When the data is extensive and complex, managing and analyzing it will be increasingly difficult and requires significant resources for storage and processing as well as analytical capabilities.
Cleaning and preparing the data
Once the raw data is analyzed and controlled, the next step is to clean and prepare it. This operation requires processing any missing values, standardizing the data, deleting unnecessary data, and structuring it to ensure it’s relevant and complete for analysis.
To facilitate the task, automation tools are available to prepare your data and guarantee its quality and consistency for later use. With Microsoft, these tools can be embedded with other cloud services, facilitating the automation and integration of these processes. Consider using Azure Databricks, Microsoft Fabric, SQL Server Integration Services (SSIS), or Power BI, among other options.
Descriptive and generative analysis
Once the data is clean, your company can begin to note certain trends and draw conclusions from it. Use descriptive analysis to understand past trends and historical performances. In other words, analyze what’s happened before to obtain these results.
You can then work to understand why you’ve arrived at these outcomes. This is when you turn to generative analysis, which uses historical data to look to the future. It can be used to create new data and simulate different scenarios.
For example, by analyzing past sales data, the company can identify the most popular products and use generative models to simulate the impact of different pricing strategies or find a pivot point to maximize the benefits.
Predictive and prescriptive models
Predictive and prescriptive artificial intelligence enables you to process large quantities of data quickly and precisely. They allow you to analyze massive data sets to extract trends and models and correct them thanks to their automatic and deep learning abilities. Their goal is to determine the best scenario to produce and the actions necessary to take full advantage.
Predictive algorithms are designed to detect complex and non-linear relationships in the data that are often invisible to traditional methods of analysis. They can integrate a wide range of variables—such as historical sales data, seasonal trends, current promotions, changes in consumer behaviour, and economic indicators—to anticipate future demand with precision. This allows companies to make more informed and proactive decisions by optimizing their processes based on the most likely scenarios.
At the same time, prescriptive artificial intelligence recommends specific actions to achieve defined goals. For example, it can recommend the best time to purchase merchandise based on price fluctuation, inflation and even the weather.
Predictive and prescriptive models allow you to anticipate future trends and make informed decisions. Artificial intelligence can generate precise predictions by leveraging historical data and taking into account multiple internal and external variables. AI also has the advantage of dynamically adjusting the predictions whenever a parameter changes.
Metaphor: Traditional statistical models vs. predictive machine learning (ML) models
A traditional statistical model is like a chef who follows a precise recipe to prepare a dish. There’s a specific list of ingredients and a series of steps to follow. If the recipe says to “add 100 grams of sugar,” it will follow this instruction to the letter. The result is predictable and always follows the same formula, with little flexibility to adapt to new circumstances or unforeseen ingredients.
In contrast, a predictive ML model is like an experienced chef who improvises based on the ingredients at hand and the taste of their guests. Instead of following a set recipe, the chef continually tastes as they go, adjusting the seasoning, testing different combinations, and learning from each preparation. They can detect subtle flavours, recognizing hidden trends in their customers’ preferences, and quickly adapt to changing or unexpected ingredients to create the best possible dish, even if they’ve never cooked this exact meal before.
Securing and protecting the data
During the entire process, it’s important to ensure the data’s security and confidentiality. This includes implementing robust security measures like data encryption, secured access to sensitive information, and compliance with data privacy and protection regulations.
We recommend using intrusion detection systems and antivirus software to prevent malware attacks and protect your data from threats. Educating and training your employees about good security practices also plays a crucial role in preventing data breaches.
Remember
By achieving a level of data maturity that’s high enough to exploit the full potential of artificial intelligence, your company can enjoy several significant benefits:
