Category: 2. Data Organization

Classification of data brings order to raw data. We can classify a bulk of data based on their need or purpose.  The different types of data, based on which they are organised are given below:

• Chronological data
• Spatial data
• Qualitative data
• Quantitative data

Chronological data are grouped or classified according to the time, such as days, weeks, months, years. For example, growth of population with time in years.

Spatial data are classified based on geographical locations or areas such as cities, states, countries, etc.

Qualitative data are categorized under different attributes like nationality, gender, religion, marital status, etc. Such data cannot be measured but can be classified based on their presence and absence of qualitative characteristics. For example, categorising the population of males and females in a city.

Quantitive data is the type of data when the above attributes (in case of qualitative classification) are further categorised into number based data such as height, age, marks of students, salary, etc.

To understand more about data organization, data handling methods and other concepts please download BYJU’S -The Learning App.

As noted in earlier chapters, information about organizations is not protected by the Privacy Act of 1974 (P.L. 93–579). If a statistical agency that has identifiable information about organizations is not governed by agency-specific confidentiality legislation, it must rely primarily on exemption 4 of the Freedom of Information Act (P.L. 89–487), which covers “trade secrets and commercial or financial information obtained from a person [interpreted to include legal persons—such as business organizations] and privileged or confidential,” to deny public requests for access

to identifiable records for organizations. The Freedom of Information Act, however, does not provide authority to deny requests from other parts of the government.

Most of the major federal statistical agencies have some form of legislation that allows them to protect the confidentiality of data on organizations that they collect and process. One exception is the Bureau of Labor Statistics, which, as explained in Chapter 5, has relied on a combination of regulations and lower court decisions to protect data that it obtains from businesses, either directly or through state employment security agencies.

For most of the economic censuses and surveys conducted by the Census Bureau, the confidentiality provisions in Section 9 of Title 13 of the U.S. Code apply. Those provisions protect the confidentiality of respondents’ file copies of census and survey report forms, as well as the originals submitted to the Census Bureau. They do not apply to reports collected from state and local governments, because those reports, by their nature, contain only data available, at least in theory, to anyone. They also do not apply to the data compiled from official import and export documents in the Census Bureau’s foreign trade statistics program. Section 301(a) of Title 13 makes the export data confidential unless the secretary of commerce determines that it is in the national interest to disclose them. The import data are not covered by Title 13 because they are collected by the Customs Service and only compiled by the Census Bureau. As a matter of policy, the import and export data are published in extensive detail by commodity and other variables, but without explicit identification of importers and exporters. The majority of data cells in the most detailed tabulations are based on fewer than five transactions.

Some additional aspects of confidentiality legislation and its effects on the ability of federal statistical agencies to protect data on organizations and to share or release their data for statistical and research purposes are discussed in connection with four case studies presented below.

Statistics on organizations (considered legal persons) cover all data subjects (units of analysis) other than natural persons or groups of natural persons, such as families or households. Occasionally, data for persons and organizations may be part of the same data set. For example, some surveys link information about the business

activities of sole proprietorships with demographic information about the personal characteristics of the proprietors. Other surveys link information about such organizations as hospitals or schools with data on persons served by or working in those organizations.

There are many kinds of organizations, and the differences among them often determine the level of confidentiality accorded to their data. In the commercial sector there are three legal forms of ownership: sole proprietorship, partnership, and corporation. Nonprofit corporations, which are exempt from income taxation, are a special group. Among the for-profit corporations, those whose shares are publicly traded are subject to special reporting requirements, and the contents of their reports (e.g., to the Securities and Exchange Commission) are generally available to the public. A subset of for-profit corporations, especially utility companies, are granted exclusive rights to particular markets; in return, their financial and other data may be subject to even greater public scrutiny.

Many companies consist of several individual establishments, at different physical locations. Although data for the company as a whole may be readily available to anyone, the same is not necessarily true for employment, payroll, production, and other data for each establishment controlled by the company.

In the public sector, the general expectation is that most information about the activities of federal, state, and local agencies and units of government will be available to all. Public access to such data is facilitated by freedom of information and sunshine laws at the federal level and in many states.

Establish consistent and clear naming practices. Name your files in a descriptive and clear way. If you need to rename multiple files, you can use a file renaming application to do it automatically. 

Keep file titles short. Avoid symbols. If you use dates, keep a consistent format.

Use consistent file version management. This means that you create a new file using an updated name, instead of saving over the old file. This is also known as “file versioning.”

Create and use a data dictionary to standardize categories and provide a definition around the role of each. This will allow all your company’s stakeholders to get the most out of the datasets you’ve collected.

Your data is probably stored as one of the most common structure types. Tabular data are flat, rectangular files. This represents data that is currently stored in a spreadsheet. Most research data is stored in this structure.

Hierarchical files are typically xml files that are able to save data and metadata in the same file. This structure is used to avoid redundancies. Relational databases organize data in multiple tables, which can hold great quantities of data and handle complex queries.

In any good data organization strategy, understanding your data’s structure is key to unlocking its value. Data can be stories in two ways: structured or unstructured. 80 to 90 percent of the world’s data is unstructured — and that number is growing many times faster than its structured counterpart.

Data that is formatted, tagged, and organized in databases is referred to as structured. It can be easily accessed, processed, and analyzed.

Good data organization strategies are important because your data contains the keys to managing your company’s most valuable assets. Getting insights out of this data could help you obtain better business intelligence and play a major role in your company’s success.

Data organization is the practice of categorizing and classifying data to make it more usable. Similar to a file folder, where we keep important documents, you’ll need to arrange your data in the most logical and orderly fashion, so you — and anyone else who accesses it — can easily find what they’re looking for.