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This chapter is from the book

This chapter is from the book

Data Management: Determine and Maintain Ownership

Data management is not easy and has only become more complex over the last ten to fifteen years. Years ago, people only had to be concerned with paper documents and control might have only meant locking a file cabinet. Today, electronic data might be found on thumb drives, SAN storage arrays, laptop hard drives, mobile devices, or might even be stored in a public cloud.

Data Governance Policy

Generally you can think of policies as high-level documents developed by management to transmit the guiding strategy and philosophy of management to employees. A data governance policy is a documented set of specifications for the guarantee of approved management and control of an organization’s digital assets and information. Data governance programs generally address the following types of data:

  • Sets of master data

  • Metadata

  • Sensitive data

  • Acquired data

Such specifications can involve directives for business process management (BPM) and enterprise risk planning (ERP), as well as security, data quality, and privacy. The goal of data governance is:

  • To establish appropriate responsibility for the management of data

  • To improve ease of access to data

  • To ensure that once data are located, users have enough information about the data to interpret them correctly and consistently

  • To improve the security of data, including confidentiality, integrity, and availability

Issues to consider include:

  • Cost—This can include the cost of providing access to the data as well as the cost to protect it.

  • Ownership—This includes concerns as to who owns the data or who might be a custodian. As an example, you may be the custodian of fifty copies of Microsoft Windows Server 2012 yet the code is owned by Microsoft. This is why users pay for a software license and not the ownership of the software itself, and typically have only the compiled “.exe” file and not the source code itself.

  • Liability—This refers to the financial and legal costs an organization would bear should data be lost, stolen, or hacked.

  • Sensitivity—This includes issues related to the sensitivity of data that should be protected against unwarranted disclosure. As an example, social security numbers, data of birth, medical history, etc.

  • Ensuring Law/Legal Compliance—This includes items related to legal compliance. As examples, you must retain tax records for a minimum number of years, while you may only retain customers’ for only the time it takes to process a single transaction.

  • Process—This includes methods and tools used to transmit or modify the data.

Roles and Responsibility

Data security requires responsibility. There must be a clear division of roles and responsibility. This will be a tremendous help when dealing with any security issues. Everyone should be subject to the organization’s security policy, including employees, management, consultants, and vendors. The following list describes some general areas of responsibility. Specific roles have unique requirements. Some key players and their responsibilities are as follows:

  • Data Owner—Because senior management is ultimately responsible for data and can be held liable if it is compromised, the data owner is usually a member of senior management, or head of that department. The data owner is responsible for setting the data’s security classification. The data owner can delegate some day-to-day responsibility.

  • Data Custodian—Usually a member of the IT department. The data custodian does not decide what controls are needed, but does implement controls on behalf of the data owner. Other responsibilities include the day-to-day management of data, controlling access, adding and removing privileges for individual users, and ensuring that the proper controls have been implemented.

  • IS Security Steering Committee—These are individuals from various levels of management that represent the various departments of the organization. They meet to discuss and make recommendations on security issues.

  • Senior Management—These individuals are ultimately responsible for the security practices of the organization. Senior management might delegate day-to-day responsibility to another party or someone else, but cannot delegate overall responsibility for the security of the organization’s data.

  • Security Advisory Group—These individuals are responsible for reviewing security issues with the chief security officer and they are also responsible for reviewing security plans and procedures.

  • Chief Security Officer—The individual responsible for the day-to-day security of the organization and its critical assets.

  • Users—This is a role that most of us are familiar with because this is the end user in an organization. Users do have responsibilities; they must comply with the requirements laid out in policies and procedures.

  • Developers—These individuals develop code and applications for the organization. They are responsible for implementing the proper security controls within the programs they develop.

  • Auditor—This individual is responsible for examining the organization’s security procedures and mechanisms. The auditor’s job is to provide an independent objective as to the effectiveness of the organization’s security controls. How often this process is performed depends on the industry and its related regulations. As an example, the health care industry in the United States is governed by the Health Insurance Portability and Accountability Act (HIPAA) regulations and requires yearly reviews.

Data Ownership

All data objects within an organization must have an owner. Objects without a data owner will be left unprotected. The process of assigning a data owner and set of controls to information is known as information lifecycle management (ILM). ILM is the science of creating and using policies for effective information management. ILM includes every phase of a data object from its creation to its end. This applies to any and all information assets.

ILM is focused on fixed content or static data. While data may not stay in a fixed format throughout its lifecycle there will be times when it is static. As an example consider this book; after it has been published it will stay in a fixed format until the next version is released.

For the purposes of business records, there are five phases identified as being part of the lifecycle process. These include the following:

  • Creation and Receipt

  • Distribution

  • Use

  • Maintenance

  • Disposition

Data owners typically have legal rights over the data. The data owner typically is responsible for understanding the intellectual property rights and copyright of their data. Intellectual property is agreed on and enforced worldwide by various organizations, including the United Nations Commission on International Trade Law (UNCITRAL), the European Union (EU), and the World Trade Organization (WTO). International property laws protect trade secrets, trademarks, patents, and copyrights:

  • Trade secret—A trade secret is a confidential design, practice, or method that must be proprietary or business related. For a trade secret to remain valid, the owner must take precautions to ensure the data remains secure. Examples include encryption, document marking, and physical security.

  • Trademark—A trademark is a symbol, word, name, sound, or thing that identifies the origin of a product or service in a particular trade. The ISC2 logo is an example of a trademarked logo. The term service mark is sometimes used to distinguish a trademark that applies to a service rather than to a product.

  • Patent—A patent documents a process or synthesis and grants the owner a legally enforceable right to exclude others from practicing or using the invention’s design for a defined period of time.

  • Copyright—A copyright is a legal device that provides the creator of a work of authorship the right to control how the work is used and protects that person’s expression on a specific subject. This includes the reproduction rights, distribution rights, music, right to create, and right to public display.

Data Custodians

Data custodians are responsible for the safe custody, transport, and storage of data and the implementation of business rules. This can include the practice of due care and the implementation of good practices to protect intellectual assets such as patents or trade secrets. Some common responsibilities for a data custodian include the following:

  • Data owner identification—A data owner must be identified and known for each data set and be formally appointed. Too many times data owners do not know that they are data owners and do not understand the role and its responsibilities. In many organizations the data custodian or IT department by default assumes the role of data owner.

  • Data controls—Access to data is authorized and managed. Adequate controls must be in place to protect the confidentiality, integrity, and availability of the data. This includes administrative, technical, and physical controls.

  • Change control—A change control process must be implemented so that change and access can be audited.

  • End-of-life provisions or disposal—Controls must be in place so that when data is no longer needed or is not accurate it can be destroyed in an approved method.

Data Documentation and Organization

Data that is organized and structured can help ensure that that it is better understood and interpreted by users. Data documentation should detail how data was created, what the context is for the data, the format of the data and its contents, and any changes that have occurred to the data. It’s important to document the following:

  • Data context

  • Methodology of data collection

  • Data structure and organization

  • Validity of data and quality assurance controls

  • Data manipulations through data analysis from raw data

  • Data confidentiality, access, and integrity controls

Data Warehousing

A data warehouse is a database that contains data from many other databases. This allows for trend analysis and marketing decisions through data analytics (discussed below). Data warehousing is used to enable a strategic view. Because of the amount of data stored in one location, data warehouses are tempting targets for attackers who can comb through and discover sensitive information.

Data Mining

Data mining is the process of analyzing data to find and understand patterns and relationships about the data (see Figure 2.2). There are many things that must be in place for data mining to occur. These include multiple data sources, access, and warehousing. Data becomes information, information becomes knowledge, and knowledge becomes intelligence through a process called data analytics, which is simply examination of the data. Metadata is best described as being “data about data”. As an example, the number 212 has no meaning by itself. But, when qualifications are added, such as to state the field is an area code, it is then understood the information represents an area code on Manhattan Island. Organizations treasure data and the relationships that can be deduced between individual elements. The relationships discovered can help companies understand their competitors and the usage patterns of their customers, and can result in targeted marketing. As an example, it might not be obvious why the diapers are at the back of the store by the beer case until you learn from data mining that after 10 p.m., more men than women buy diapers, and that they tend to buy beer at the same time.

Figure 2.2

Figure 2.2 Data mining.

Knowledge Management

Knowledge management seeks to make intelligent use of all the data in an organization by applying wisdom to it. This is called turning data into intelligence through analytics. This skill attempts to tie together databases, document management, business processes, and information systems. The result is a huge store of data that can be mined to extract knowledge using artificial intelligence techniques. These are the three main approaches to knowledge extraction:

  • Classification approach—Used to discover patterns; can be used to reduce large databases to only a few individual records or data marts. Think of data marts as small slices of data from the data warehouse.

  • Probabilistic approach—Used to permit statistical analysis, often in planning and control systems or in applications that involve uncertainty.

  • Statistical approach—A number-crunching approach; rules are constructed that identify generalized patterns in the data.

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