These days, intelligent data discovery tools allow organization leaders to accelerate analysis, benefit from AI-powered suggestions, detect what is most important in a timely manner, and perform the necessary actions or corrections.
For their part, data anonymization tools are necessary for the protection of the information explored, analyzed and visualized during the data discovery process.
Let's start by defining what data discovery is, and then delve into the tools and legal considerations.
What is data discovery?
Data discovery is a process in which different techniques are used to collect and evaluate data from various isolated sources and to detect and extract patterns and trends. The purpose of this process is to understand the data and extract useful information.
Data discovery gathers isolated and disparate data to analyze, detect relationships, cleanse, prepare and share with all areas of the organization.
It is not uncommon to relate the definition of data discovery to business intelligence. However, in reality, the former enhances the latter, offering a broader and more detailed visualization of the data.
The goals and challenges of data discovery
The goals of the data discovery process are to provide a panoramic view of data sources, to significantly improve decision-making and, finally, to optimize critical business areas on a large scale.
The main challenges that must be taken into account in data discovery are:
Ensuring the security and privacy of information throughout its life cycle.
The risk of re-identification, in the case of anonymized sensitive information.
Implementation and maintenance of safeguards to ensure data governance.
The respect of contractual obligations by third parties in relation to the security and confidentiality of information in the event that they share data.
Compliance with current regulations, such as the GDPR and other international data protection regulations.
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Two types of data discovery
1. Manual data discovery
Manual data discovery methods usually take longer because the human specialists in advanced analysis techniques are in charge of configuring the data sources and preparing the rules on which the process will be based.
In addition, they also conceptualize all the information into maps for understanding business data.
2. Intelligent data discovery
In this case, all the processes of data preparation, integration, hidden data detection, correlation, conceptualization, and presentation are carried out using artificial intelligence and its subfields.
These are highly efficient data discovery methods because they employ systems that combine AI, machine learning and natural language processing (NLP) models, which will not only analyze the data, but also learn continuously during each process.
Data discovery tools
In this first step of data discovery, the tools are used to perform preliminary work prior to business analysis. Essentially, these tools examine the data from each source, combining, cleansing, transforming and ensuring the quality.
In the analysis, tools are used to identify patterns and trends; for example, predictive analysis is used through machine learning algorithms or statistical models.
During this step of data discovery, the tools transform the data into graphical representations (maps, tables, scatter plots, etc.) that facilitate the overall visualization of the information and communicate the relationships between data.
Modeling is another data discovery step. In this case, the tools evaluate the processing and storage of data in detail. To this end, they analyze and identify the requirements in each business process and evaluate how the models used satisfy these requirements. An efficient database design can be achieved using this technique.
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The advantages of data discovery
The intelligent data discovery process offers the following advantages:
Transforming data into valuable and accurate information.
Converting data patterns and KPIs into understandable information.
Saving analysts time and effort.
The ease of interpreting data in different ways, allowing users to create specific and unique analyses.
Continuous capture and preparation of new data for future accessibility.
Uses of data discovery in an organization
Data discovery has enabled companies to leverage information in a variety of innovative ways in order to consider new variables and manage a wide range of areas.
Examples of data discovery tools include the following uses:
Analyzing customers: they help companies gain a 360-degree view of customers by analyzing their behavior, transactions, and emotions. This allows them to:
Detect early signs of customer dissatisfaction.
Understand how customers perceive the company.
Determine which customers are candidates for certain products.
Detecting fraud: an example of intelligent data discovery is the detection of atypical patterns in transactions that could signify fraudulent actions.
Optimizing health services: they improve or simplify the evaluation of patient outcomes.
Making the most of the supply chain: they help detect critical areas for improvement, as well as to evaluate suppliers and inventories.
The GDPR and data discovery: legal considerations
The definition of data discovery focuses on how customer data collected by companies is explored, stored, processed and used. Based on this in-depth analysis, organizations can increase their market value and optimize their business decisions.
However, the processing of this information must comply with the rules set out in the GDPR and employ tools that allow the de-identification of personal data.
The main legal considerations in data discovery include:
Having the consent of the data subject.
Protecting data through effective security controls and the application of data anonymization or encryption techniques.
How data discovery with AI can save your business
AI data discovery methods minimize human intervention in critical business operations, such as decision-making, information management, business management, product optimization, etc., meaning they significantly improve performance in each of these processes.
Data discovery with AI amplifies the ability to exploit data and optimizes the quality of analysis and reporting because it enables the analysis of structured and unstructured data, and there are no limits to the diversity of sources.
For the best results, you should always have the most suitable, flexible, fast and easy-to-use AI data discovery tools, which also have attractive and useful graphical functionalities. And, of course, never forget anonymization tools.
At Pangeanic, we have developed our own powerful data anonymization solutions based on AI.
Contact us, and we will help you protect your company and your customers' data.