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What is data classification?Data classification is the process of organizing data into categories that make it easy to retrieve, sort and store for future use. A well-planned data classification system makes essential data easy to find and retrieve. This can be of particular importance for risk management, legal discovery and regulatory compliance. Written procedures and guidelines for data classification policies should define what categories and criteria the organization will use to classify data. They also specify the roles and responsibilities of employees within the organization regarding data stewardship. Once a data classification scheme is created, security standards should be identified that specify appropriate handling practices for each category. Storage standards that define the data's lifecycle requirements must be addressed, as well. What is the purpose of data classification?Systematic classification of data helps organizations manipulate, track and analyze individual pieces of data. Data professionals often have a specific goal when categorizing data. The goal affects the approach they take and classification levels they use. Some common business goals for these projects include the following:
Why data classification is importantData classification is an important part of data lifecycle management that specifies which standard category or grouping a data object belongs in. Once sorted, data classification can help ensure an organization adheres to its own data handling guidelines and to local, state and federal compliance regulations, such as the Health Insurance Portability and Accountability Act, or HIPAA. Companies in highly regulated industries often implement data classification processes or workflows to aid in compliance audit and data discovery processes. Data classification is used to categorize structured data, but it is especially important for getting the most out of unstructured data. Data categorization also helps identify duplicate copies of data. Eliminating redundant data contributes to efficient use of storage and maximizes data security measures.
Common data classification stepsNot all data needs to be classified. In some cases, destroying data is the prudent course of action. Understanding why data needs to be classified is an important part of the process. Steps involved in developing a comprehensive set of policies to govern data include the following:
Types of data classificationStandard data classification categories include the following:
In computer programming, file parsing is a method of splitting data packets into smaller subpackets that are easier to move, manipulate, categorize and sort. Different parsing styles determine how a system incorporates information. For instance, dates are split up by day, month or year, and words may be separated by spaces. Some standard approaches to data classification using parsing include the following:
Tools used for data classificationVarious tools are used in data classification, including databases, business intelligence (BI) software and standard data management systems. Some examples of BI software used for data classification include Databox, Google Data Studio, SAP Lumira and Vise. More generally, a regular expression is an equation used to quickly pull data that fits a certain category, making it easier to categorize all of the information that falls within those particular parameters. Benefits of data classificationUsing data classification helps organizations maintain the confidentiality, ease of access and integrity of their data. For unstructured data in particular, data classification lowers the vulnerability of sensitive information. For example, merchants and other businesses that accept major credit cards are expected to comply with the data classification and other standards of the Payment Card Industry's Data Security Standards. PCI DSS is a set of 12 security requirements aimed at safeguarding customer financial information. Classification also saves companies from paying steep data storage costs. Storing massive amounts of unorganized data is expensive and could be a liability. General Data Protection RegulationThe European Union's General Data Protection Regulation (GDPR) is a set of international guidelines created to help companies and institutions handle confidential and sensitive data carefully and respectfully. It is made up of seven guiding principles: fairness, limited scope, minimized data, accuracy, storage limitations, rights and integrity. There are steep penalties for not complying with these standards in some countries. Implementing methodical data classification is a necessity to comply with the many parts of GDPR. It requires organizations to assign specific security control levels to data to prevent unauthorized disclosure. Classifying data helps data security teams identify data that requires anonymization or encryption. Another aspect of GDPR that requires effective data classification is that it gives individuals the right to access, change and delete their personal data. Data classification lets companies quickly retrieve such data and fulfill a person's specific request. Examples of data classificationA number of different category lists can be applied to the information in a system. These lists of qualifications are also known as data classification schemes. For example, one way to classify sensitivity categories might include classes such as secret, confidential, business use only and public. An organization might also use a system that classifies information based on the type of qualities it drills down into. It might look at the type of content information that goes into files, looking for certain characteristics. For example, context-based classification examines applications, users, geographic location and creator info. User classification is based on what an end user chooses to create, edit and review. Data reclassificationAs part of maintaining a process to keep data classification systems as efficient as possible, it is important for an organization to continuously update the classification systems it uses. It must reassign the values, ranges and outputs of these systems to more effectively meet the organization's classification goals. Data regression vs. data classification algorithmsBoth regression and classification algorithms are standard data management styles. When it comes to organizing data, the biggest differences between regression and classification algorithms is the type of expected output. Systems that produce a single set of potential results within a finite range often find classification algorithms are ideal. When the results of an algorithm are continuous, such as an output of time or length, using a regression algorithm or linear regression algorithm is more efficient. See how a regression algorithm works.Find out more about data governance and how it ensures data is consistent, trustworthy and not misused. In which type of sampling design is the final choice of respondents left up to the interviewer?In probability sampling, the units are selected randomly while in quota sampling a non-random method is used—it is usually left up to the interviewer to decide who is sampled.
What are the two basic types of sampling utilized by marketing researchers?Random, or probability sampling, gives each member of the target population a known and equal probability of selection. Systematic sampling is a modification of random sampling. To arrive at a systematic sample we simply calculate the desired sampling fraction and take every nth case.
What type of research is Justin's team collecting?What type of research is Justin's team collecting? Justin is part of a group of students who are working with a bakery to collect marketing research. The bakery wants to determine whether a new type of pastry would be successful.
Which of the following is an example of secondary data analysis?Popular examples of secondary data include: Tax records and social security data. Census data (the U.S. Census Bureau is oft-referenced, as well as our favorite, the U.S. Bureau of Labor Statistics) Electoral statistics.
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