Data architecture: the basis of a differentiating strategy

Data architecture

Data architecture

It integrates the models, policies and rules that govern what data is to be collected; how they will be stored, classified and exploited using the available technological infrastructure.

In the same way that data architecture is critical for the good management of a company, so is its corporate strategy, so it is necessary to take care of its design and implementation. Both are related, since if something goes wrong in the design of the corporate strategy, there can be many failures in the management of the data and, consequently, in the organization of the company. A migration process, for example, can become a real headache if the database design presents problems. The 7 Vs of “big data” technology – Learn More of This

An example of a well-defined data architecture are those companies that, after learning about the changes that were going to be applied to the GDPR, adapted their databases before the regulations came into force.

Big data and the origin of data architecture

To understand what data architecture consists of, you have to know what big data is : ‘ large volumes of data of all kinds that cannot be analyzed using conventional computer tools ‘. Thus, the objective of big data tools is none other than to analyze data and information in an intelligent way, in order to help in decision-making. Recruitment 2.0: innovation in human resources

For its part, the objective of data architecture is to define the origin and types of data necessary for the development of the business. The system designed to achieve this must be simple enough to be understood by stakeholders, as well as consistent and stable. Therefore, data architecture does not seek to define a universal design methodology, but rather to develop techniques to help display and produce information spaces .

Data architecture planning and design

In general, the data architecture is designed and developed during the planning stage of a new system to establish how the data will be processed, stored, and used, and how it will be accessible. Thus, to design an efficient system, control the flow of data and guarantee its protection, it is important to know the relationship and the type of management necessary for each type of data from the beginning. CDO: the strategic vision that data management needs

11 necessary functions in data management

With regard to data management, the DAMA International organization defines eleven necessary functions:

  1. Data governance : planning, supervision, and control in the management and use of data.
  2. Data architecture : establishment of models, policies and rules to manage data.
  3. Data modeling & design : database design, implementation management and technical support.
  4. Data storage : definition of the storage location, and the amount and type of data to be stored.
  5. Data security : protection of privacy and confidentiality.
  6. Data integration & interoperability : transport and consolidation of data.
  7. Documents & contents : establishment of the rules to be applied to the data outside the databases.
  8. Reference & master data : shared data management to reduce the amount of redundant information, improve data quality and obtain a global view of the information.
  9. Data warehousing & BI : management of the analytical data process and access to data that will support decision-making.
  10. Meta-data : indexing of the information contained in a database.
  11. Data quality : definition, control and improvement of data quality according to the needs of the project.

Data architecture in data model development

The data architecture of a company has to be one of the pillars on which the development of the business data model is supported . To define it, the following aspects must be taken into account: Tips for implementing an effective customer scorecard

  • The database configuration.
  • The form of data storage.
  • The metadata architecture.
  • The data integration model or models.

The guidelines chosen in the definition and planning of a data architecture should contemplate the link with other business models and offer some flexibility so that the organization can develop the data when necessary and without impediments. For example, they must take into account that the data collected and stored may be exploited at another time by various business units, and not just for the one for which it was collected in the first place.

In many cases, in order to carry out this development it will be necessary for the company to adapt to the circumstances of the market, and also to its demands . For example, when new legislation on data protection emerges, as has happened in Europe with the RGPD, it will be necessary to adapt the data architecture to the new reality that it poses both in terms of the new regulations and the that clients demand in relation to the protection of their information.

By establishing the foundations of a data architecture, the informational skeleton of a company is structured. In this process, there are several factors that cannot be ignored, for example, the present and future information needs of the company, and the quality of the data models . To do this, it is advisable to define the corporate information management strategy around three points: Why is the brand ambassador the most powerful branding resource?

  • Development of standards applicable to all perspectives of the data model.
  • Review of the quality of the data model.
  • Version management and data model integration processes .

It must be taken into account that the data architecture designs that are created within an organization can be reused to generate other different systems; for example, to generate the data architecture designs for new subsidiaries that are opened by the company that made the original design. In this way, it is possible to reduce costs and improve the quality of the databases, especially if the architectures from which the designs are reused have been successful.

The data architecture development cycle

The development of a data architecture, which always precedes the development of the system, is divided into four stages:

  • Requirements. This phase focuses on capturing, documenting, and prioritizing requirements that influence data architecture. It is necessary to place special emphasis on the quality of the data, since they play a crucial role within these requirements. For example, if the data obtained is redundant, incomplete or not related to the information to be obtained according to what is established in the data architecture, this data will be good, but cannot be considered of quality, since it does not conform to the requested requirements.
  • Design . It is the most complex stage of data architecture, since it is the moment in which the structures that compose it are defined. Patterns and design tactics are used to create it . At this point you also have to choose the technologies that will be used for data management, storage and processing.
  • Documentation. After the creation of the architecture design, it must be possible to communicate it to the rest of the actors involved in its development , and to do so successfully it is necessary to document the architecture design in detail.
  • Evaluation . After the documentation stage, it is important to evaluate the design to identify potential problems. This is advantageous if done early, before coding begins, as the cost of correcting identified defects is less than after the system is built.

If the database architecture design is perfectly defined and adjusted, and meets all legal requirements regarding storage and management, later problems will be avoided. In this sense, analysis is the fundamental instrument to obtain continuous improvement.

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