A few years ago, decisions were mainly based on opinions, assumptions, and a complex combination of notes and historical records that reflected past outcomes, and not on data, which may or may not have been relevant for the future.
Nowadays, organizations turn to data and technology to understand past results and anticipate future business needs.
This allows them to manage aspects as diverse as suppliers, inventory, expansion to new locations, products and services, as well as hiring, training, and investments. However, an excess of data can also create complications.
If data is not collected, managed, and analyzed efficiently, it can become overwhelming and hinder decision-making.
According to some estimates, incorrect data costs global businesses more than $5 trillion annually.
The data vision takes information to the next level by offering comprehensive analysis and quality assurance features that allow analysts and users to quickly identify errors, improve data quality, and increase productivity.
Companies can leverage the power of statistics and machine learning to uncover key insights that drive more effective decisions and improve overall data quality.
By integrating machine learning, natural language processing, and automation within advanced analytics solutions, companies can improve outcomes and assist their teams with augmented analytics, designed as self-service solutions for business users.
This allows the team to gather and analyze information with the confidence that data quality is ensured, and the results will be clear and accurate.
When an analytical solution is built on this foundation, with advanced tools and techniques to support users, the company can ensure the adoption of the tool and achieve positive results.
Users don’t need to learn complex systems or depend on data scientists or analysts to get answers.
Tools that support the data vision include various data quality management techniques.
These tools allow users to interact with datasets in a specific way, providing clear and useful information to make decisions and develop strategies. Some of the key features to look for include:
- Overview: Displays the data quality index as a percentage, reflecting the dataset’s quality level. It indicates the data quality and the number of columns, including information on missing values, duplicates, and measurement and dimension columns.
- Observations: Highlights all inconsistencies and anomalies found in the dataset, along with the names of the affected columns.
By clicking on a column name, users can access detailed information about the issue and recommendations for fixing it.
- Column Analysis: Provides details about each column in the dataset, categorizing them by type and displaying sample values, missing values, most and least frequent values, unique values, and the quality index of each column.
- Column Associations: Shows relationships between columns, helping to understand how they are connected. The higher the association index, the stronger the relationship between columns.
- Feature Importance: Automatically identifies the target variable and its main predictors in the dataset, also showing the impact of each predictor on the target variable. This simplifies the selection of the most important predictors, making it easier to build effective predictive models.
- Missing Value Analysis: Provides an overview of missing values across all columns in the dataset.
The graphs show both non-missing and missing values, allowing users to quickly identify columns with incomplete data.
- Column Metadata: Provides information about the dataset’s update, including the last modification and publication date.
It also includes details about the data type, column type, and sample value of each column in the dataset.
- Settings: Allows users to customize the data vision calculation process for datasets, reducing the load and processing time.
Data analysis tools and data quality management techniques provide a comprehensive set of features that allow users to identify errors, improve data quality, and increase productivity.
Users can uncover valuable insights and improve the overall data quality with clear recommendations for quick and effective action.
Do you need data development for your company or business? Write to us at comunicaciones@bpt.com.co
