The term data environment means an environment where data is collected, stored, and analyzed using computer technology. This is quite different to a traditional office environment where people are hired to use their brains, rather than their fingers, to achieve results. In today’s information age, organizations increasingly need to find ways of collecting, storing and analyzing vast amounts of data.
This environment can be created by setting up a specialized room, or space, in your company, such as a data center or an IT department. In addition to this, you should ensure that the appropriate software and hardware are available for the tasks you have been asked to perform. You should also set up policies and procedures so that employees follow the correct processes.
To ensure the data environment is working effectively, you must monitor how it is being used and how it affects the overall performance of your organization. You can do this by reviewing statistics, for example, or by monitoring the productivity of each individual who works in the data environment.
Data Environment can benefit a company
A data environment such as Delphix enables you to collect, store and analyze data. As data volumes grow, so does the need to store and manage it. The ability to analyze data gives you an insight into how your organization is performing. When you know exactly what you are doing wrong and how you can improve, you will be able to fix problems and implement solutions that will help the business.
You can also use data to identify patterns in customer buying behavior. You can then adapt the products or services you offer to meet customer needs and increase sales.
Does the data environment cost a lot of money?
In most cases, setting up a data environment is fairly straightforward. Once you have designed your environment, you can purchase the necessary equipment and hire the people you require.
However, there are many factors that affect the total cost of running a data environment. These include the size of your organization, the volume of data you wish to manage and the complexity of the analysis you are conducting. A good data environment consultant will be able to provide you with a detailed cost estimate.
Types of data environments
There are five main types of data environment: central data environments, decentralized data environments, public data environment, private data environment and hybrid data environment.
- Central data environments: Used when there is a single location where the majority of the data is stored. An example of this is a centralized data center.
- Decentralized data environments: Typically used when the data needs to be accessed from a number of different locations. For example, a business might collect data from employees’ personal computers and store it on a network file server.
- Public Data Environment: Data environment used by a business for public access. These data environments are commonly associated with consumer-facing web sites such as retail sites, search engines, etc.
- Private Data Environment: Data environment used by a business for internal or secure access. The most common example is the enterprise database that stores sensitive business information.
- Hybrid Data Environment: Data environment that allows for both public and private access. An example of this type of data environment is a web application that is accessible to the public via a public URL but has limited functionality that requires authentication.
Role of security in Data Environments
There are two main goals that data environments must serve. These are:
- to protect against loss or damage to data, and
- to provide assurance to business partners that the data is properly secured.
In addition, data environments must be resilient and must be able to support the business’s growth and expansion plans.
To achieve these goals, data environments should meet certain minimum requirements. For instance, they need to be able to store a reasonable amount of data in a reasonable amount of time. Data environments also need to be capable of performing basic functions such as indexing, searching, sorting, and retrieving data. Finally, data environments should be able to provide audit logs and audit trails for historical analysis.
Data environments play an integral role in ensuring the integrity of business data and protecting it from unauthorized users. To this end, the following features are required of data environments.
- Data Security: Ensuring that data is encrypted while in transit and at rest and limiting the exposure of data.
- Data Protection: Ensuring that data is backed up and stored in a safe location.
- Compliance: Maintaining compliance with laws and regulations governing privacy and security.
- Resilience: Ensuring that data can be accessed and used in the event of an outage.
- Backup: Ensuring that backups are performed as frequently as possible.
- Integrity: Ensuring that data is accurate, complete, and reliable.
- Availability: Ensuring that data is available to users at all times.
Types of data security
The different types of data security include:
- Physical Data Security: The process of securing a physical location where data is stored. This includes such things as restricting access to the physical building, restricting access to the physical storage facility, and enforcing access controls.
- Logical Data Security: The process of securing data within the confines of a computer system. Logical security can be implemented using both hardware and software.
Data Security impacts the design of Data Environment
In a data environment, there are two main concerns:
- Security: Ensuring that data is properly secured and protected from unauthorized access.
- Data Privacy: Ensuring that users’ personal data remains private.
Because the two goals are related to each other, it is critical that the data environment address both of these issues. To this end, the data environment needs to have the following components.
- Authentication and Authorization: Ensuring that only authorized users can access data.
- Encryption: Ensuring that data is properly secured while in transit and at rest.
- Auditing: Ensuring that access to data is properly monitored and recorded.
- Backup: Ensuring that data is properly backed up and stored in a safe location.
- Compliance: Ensuring that data is accurate, complete, and reliable.
- Integrity: Ensuring that data is available to users at all times.
- Availability: Ensuring that data is available to users at all times.
- Security-by-Design: Ensuring that security is built in to the data environment.
Common Data Environment
We use ‘common data environment’ (CDE) for referring to a single, common data store across multiple data sources, such as CRM, eCRM, business applications and data warehouses. CDE offers a common data base, and this allows integration between different applications. In addition, it allows multiple applications to share and synchronize data efficiently.
CDE should not be confused with other commonly used terms such as “data warehouse” or “data mart.” The term data warehouse usually refers to a complete data solution that provides the capability to perform complex queries across several databases. Data marts are usually a subset of information that is extracted from large data stores. A data mart is generally used to provide a specific set of data for a particular application. A CDE is designed to support the requirements of various applications within a specific organizational structure.
The main advantage of a CDE is that it provides a centralized database to hold the customer information, which is accessed by many applications. This reduces the requirement to store and maintain multiple copies of the same customer information across different applications. As customers often use multiple channels to interact with a company, having a single point of reference to record interactions helps reduce duplication of effort and data entry.
Types of CDE
A CDE can be a standalone data repository, or a part of the core business applications. It can also be a separate data warehouse or a subset of information contained in the data warehouse.
There are four types of CDEs based on their structure and the underlying technology they are implemented on:
- Common Data Access Layer (CDAL): It provides a common interface and enables the applications to access the data from the data source.
- Common Data Repository (CDR): It is a logical data repository.
- Common Data Store (CDS): It is a physical data store and is a layer of abstraction that hides the differences in the underlying storage media.
- Common Business Application Infrastructure (CBAPI): It is a framework that supports the development of the business applications.
Benefits of using CDE
- Data is managed at the highest level, in a consistent manner.
- Data is easily shared and synchronized among the applications.
- Centralized, updated information is always available.
- Changes to the data in a CDE are propagated to all the applications.
- Data is stored in one location for quick retrieval.
- There is no need to duplicate or replicate data.
- It is easy to expand the CDE by adding new applications, or by adding more data sources.
Disadvantages of using CDE
- CDEs are expensive, requiring specialized skills and tools.
- They may require significant changes to the existing business systems.
- They may be difficult to implement.
- It may be difficult to manage and control data within the CDE.
CDE is a powerful tool, and it has tremendous benefits for any organization. However, implementing a CDE requires a lot of planning and careful planning.
