Automated data discovery utilizes machine learning, statistical techniques, and data mining tools to identify patterns, relationships, and insights within large datasets. This process aims to make discovering meaningful insights in data more efficient, faster, and more accurate than manual methods.
Automated data discovery can be applied to a wide range of data types and use cases, such as identifying patterns in customer behavior, Banking, and finance, detecting fraud, forecasting sales, and Retail, or optimizing supply chain operations. It can be used to find previously unknown information in structured and unstructured data.
The process typically consists of several stages, including data preparation, feature selection, model selection, model evaluation, and model deployment. Automated data discovery can be done either on-premises or in the cloud, and it often involves using big data technologies like Apache Hadoop and Apache Spark to handle large and complex data sets.
Some of the algorithms that are commonly used in automated data discovery include:
- Clustering: Clustering algorithms group similar data points together. Some popular clustering algorithms include K-Means, hierarchical clustering, and density-based clustering.
- Association Rule Mining: Association rule mining algorithms look for relationships between items in a dataset. These algorithms are used to find frequent item sets and generate association rules. Apriori algorithm and Eclat algorithm are examples of association rule mining algorithms.
- Anomaly Detection: Anomaly detection algorithms are used to identify data points that are unusual or different from the rest of the data. Some popular anomaly detection algorithms include Mahalanobis Distance and Local Outlier Factor (LOF).
- Time Series Analysis: Time series analysis algorithms are used to analyze data that changes over time. Some popular time series algorithms include Exponential Smoothing and ARIMA.
- Neural Networks: Neural networks are a type of machine learning algorithm that are used to identify patterns and relationships in data. Some popular neural networks include Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNN).
- Random Forest, Decision Trees : These algorithms are used to classify and predict outcomes based on a given set of input features
- Principal Component Analysis (PCA) and Singular Value Decomposition (SVD): for dimensionality reduction and feature extraction.
These are just a few examples of the many algorithms that can be used in automated data discovery.
Some of the challenges of Automated Data Discovery include:
- Data Quality: Automated Data Discovery heavily relies on the quality of data. Poor-quality data can lead to unreliable or inaccurate results. Data quality needs to be tackled before this activity. Although Platforms like SCIKIQ can manage this real-time basis as well in certain use cases.
- Data Privacy: With the increase in data collection, there is also an increase in concerns about data privacy and activity should comply with regulations such as GDPR.
- Scalability: Automated Data Discovery needs to be able to handle large and complex datasets, and be able to scale to meet the large volume and velocity of data.
Some of the current and future scope of Automated Data Discovery include:
- Real-time and Streaming Data: Automated Data Discovery need to be able to process real-time and streaming data in order to provide insights in near real-time. This means data integration and analytics on a real time basis.
- Hybrid Approaches: Combining different techniques and algorithms, such as rule-based and machine-learning methods, to increase the accuracy and maintain the data quality of the entire exercise.
- Multi-modal Data: Automated Data Discovery is going be used to handle multiple types of data, such as text, images, and audio and be able to interpret data.
Automation can simplify the complexity of IT infrastructure and offer fast return on investment. Automated tools can also extend classification coverage across a variety of data sources, including those that originate outside of user control. With the growing volume of data being created on a daily basis, organizations should consider implementing automated data identification and classification tools that can work in a scalable and accurate manner to enable safe storage, sharing, and analytics.
By combining people, process, and technology, organizations can meet all key data protection and control requirements, not only in terms of understanding and managing data but also delivering the breadth of security coverage required on a local and remote basis.