What are the classification of analytics?
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In machine learning, classification models play a major role in data analytics. Classification models will try to draw conclusions using observed values. For example, given a set of data, the model can try to predict whether something is a fruit or a vegetable. A decision-tree classification algorithm trains a model by producing branching
possibilities and assigning probabilities to them. This algorithm performs best when modelling nonlinear associations between classes. The method is computationally efficient but is subject to overfitting. The most common use case for the decision tree is churn analysis. A random-forest classification algorithm trains a model using the results of an ensemble of randomly generated decision trees. This algorithm performs best when modelling nonlinear associations between classes. The ensemble method helps avoid problems of overfitting and underfitting but is computationally expensive. The most common use cases are for a direct-marketing-campaign response, customer contract renewal, sales
lead scoring, loan default risk, and product/alternative choice XGBoost classification is an ensemble method that builds many decision trees to model the association between features and
a target. Due to this algorithm’s boosting capability—a method by which decision trees improve each other—it is less susceptible to overfitting and underfitting. It is useful where you use many different features to train the classifier. The common use cases are customer contract renewal, sales lead scoring, loan default risk, product/alternative choice. Learn about the different types of classification and how to effectively classify your data in Data Protection 101, our series on the fundamentals of data security. A Definition of Data ClassificationData classification is broadly defined as the process of organizing data by relevant categories so that it may be used and protected more efficiently. On a basic level, the classification process makes data easier to locate and retrieve. Data classification is of particular importance when it comes to risk management, compliance, and data security. Data classification involves tagging data to make it easily searchable and trackable. It also eliminates multiple duplications of data, which can reduce storage and backup costs while speeding up the search process. Though the classification process may sound highly technical, it is a topic that should be understood by your organization’s leadership. Reasons for Data ClassificationData classification has improved significantly over time. Today, the technology is used for a variety of purposes, often in support of data security initiatives. But data may be classified for a number of reasons, including ease of access, maintaining regulatory compliance, and to meet various other business or personal objectives. In some cases, data classification is a regulatory requirement, as data must be searchable and retrievable within specified timeframes. For the purposes of data security, data classification is a useful tactic that facilitates proper security responses based on the type of data being retrieved, transmitted, or copied. Types of Data ClassificationData classification often involves a multitude of tags and labels that define the type of data, its confidentiality, and its integrity. Availability may also be taken into consideration in data classification processes. Data’s level of sensitivity is often classified based on varying levels of importance or confidentiality, which then correlates to the security measures put in place to protect each classification level. There are three main types of data classification that are considered industry standards:
Content-, context-, and user-based approaches can be both right or wrong depending on the business need and data type. Determining Data RiskIn addition to the types of classification, it’s wise for an organization to determine the relative risk associated with the types of data, how that data is handled and where it is stored/sent (endpoints). A common practice is to separate data and systems into three levels of risk
Note: Some also use a more granular scale, adding “severe” risk or other categories to help further differentiate data. Using a Data Classification MatrixCreating and labeling data may be easy for some organizations. If there aren’t a large number of data types or perhaps your business has fewer transactions, determining the risk of data and your systems is likely less difficult. That said, many organizations dealing with high volume or multiple types of data are likely to need a comprehensive way of determining their risk. For this, many use a “data classification matrix.” Creating a matrix rating data and/or systems from how likely they are to be compromised and how sensitive that data is will help you quickly determine how to better classify and protect all things sensitive. An Example of Data ClassificationAn organization may classify data as Restricted, Private or Public. In this instance, public data represents the least-sensitive data with the lowest security requirements, while restricted data is in the highest security classification and represents the most sensitive data. This type of data classification is often the starting point for many enterprises, followed by additional identification and tagging procedures that label data based on its relevance to the enterprise, quality, and other classifications. The most successful data classification processes employ follow-up processes and frameworks to keep sensitive data where it belongs. The Data Classification ProcessData classification can be a complex and cumbersome process. Automated systems can help streamline the process, but an enterprise must determine the categories and criteria that will be used to classify data, understand and define its objectives, outline the roles and responsibilities of employees in maintaining proper data classification protocols, and implement security standards that correspond with data categories and tags. When done correctly, this process will provide employees and third parties involved in the storage, transmission, or retrieval of data with an operational framework. The video clip below gives techniques for classifying sensitive data and is from our webinar, How Classification Defines Your Data Security Strategy, which is presented by Garrett Bekker, Senior Analyst, Information Security at 451 Research. You can watch the full webinar here. Policies and procedures should be well-defined, considerate of the security requirements and confidentiality of data types, and straightforward enough that they are easy for employees promoting compliance to interpret. For instance, each category should include information about the types of data included in the classification, security considerations with rules for retrieving, transmitting, and storing data, and potential risks associated with a breach of security policies. GDPR Data ClassificationWith the General Data Protection Regulation (GDPR) in effect, data classification is more imperative than ever for companies that store, transfer, or process data pertaining to EU citizens. It is crucial for these companies to classify data so that anything covered by the GDPR is easily identifiable and the appropriate security precautions can be taken. Additionally, GDPR provides elevated protection for certain categories of personal data. For instance, GDPR explicitly prohibits the processing of data related to racial or ethnic origin, political opinions, and religious or philosophical beliefs. Classifying such data accordingly can significantly reduce the risk of compliance issues. Steps for Effective Data Classification
There are more benefits to data classification than simply making data easier to find. Data classification is necessary to enable modern enterprises to make sense of the vast amounts of data available at any given moment. Data classification provides a clear picture of all data within an organization’s control and an understanding of where data is stored, how to easily access it, and the best way to protect it from potential security risks. Once implemented, data classification provides an organized framework that facilitates more adequate data protection measures and promotes employee compliance with security policies. Additional Data Classification Resources
What are the classification of data analytics?There are four types of data analytics:
Predictive (forecasting) Descriptive (business intelligence and data mining) Prescriptive (optimization and simulation) Diagnostic analytics.
What are different types of classification?The three types of classification are Artificial classification, Natural classification, and Phylogenetic classification.
What are the 3 main types of data classification?Data classification generally includes three categories: Confidential, Internal, and Public data.
What are the 5 data analytics?5 Types of analytics: Prescriptive, Predictive, Diagnostic, Descriptive and Cognitive Analytics - WeirdGeek | Data analytics, Data analysis tools, Data science.
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