4 best practices for maintaining data integrity

Keep the data integrity is an elementary component to maximizing the power of your customer and prospect database.

It's quite possible that your data will never be perfect. But it is possible to approach perfection by making data integrity be a priority. To do this, the organization should focus on some best practices to instill confidence in the data. On the contrary, without a proven track record of reliable data, you will never have full confidence in the business value of your data.

It's quite possible that your data will never be perfect. But it is possible to approach perfection by making data integrity be a priority. To do this, the organization should focus on some best practices to instill confidence in the data. On the contrary, without a proven track record of reliable data, you will never have full confidence in the business value of your data.

Data integrity must be a requirement at all stages of every business process. The organization must ensure that its data is useful to all stakeholders, all time.

A first step in knowing if you're on the right track to ensuring data integrity is to start by answering some basic questions.:

• When problems are discovered in your organization data qualityAre you able to react quickly and appropriately?

• Does the organization have systems, processes, tools, training, adequate controls and monitoring to anticipate problems and correct them?

• Do you have the resources to address the problem??

• Have these mechanisms been communicated to the rest of the company??

Who owns the data?

There are several people within the organization on whom data integrity is based to meet its goals.. They are usually the following:

In larger organizations, but nevertheless, there are additional people even more intimately connected to data integrity:

• Data Controller– Control the information, including the right to manage and delete data. You are responsible for the data in the source system or system of record. Has an abstract view of the data, focusing on relationships between tables, roles or structures.

• Responsible for the application– Ensures that the application meets the specified objectives and user requirements. It is also actively involved in streamlining efforts and appropriate security safeguards.. You are also well positioned to communicate with other stakeholders. IT tends to communicate with technical language and often imposes technical requirements. The company, Conversely, communicates through business terminology without fully explaining its processes. The application administrator must be fluent in both languages and understand the particular needs of each computer.

Maintain data quality across the enterprise

Data quality and integrity issues arise when data is shared, Replicated, Archived, transfer to a data warehouse for reporting or are sent to another system. The needs of business users differ, leading to conflicts due to inconsistent data models between systems. As data quality issues arise, new rules will need to be applied.

And this is where a third important role appears.:

• Data managerThis assumes responsibility for data definitions, the context, resolving inconsistencies between business functions and appropriate business patterns. They also oversee secure custody, data transport and storage and the implementation of those rules. It's your job to explain official standards and descriptions to various managers and stakeholders throughout the lifecycle..

4 best practices for maintaining data integrity

Delegate

Assign a business manager and an IT manager for each system of record or origin system.

• Shape those responsible for acting in case of problem.

• Set tracking and alerts service level agreement (Sla) for source systems, logging and middleware.

Educate

Designate a data steward to inform all stakeholders about definitions, data standards and rules.

• Subsequent Business Terms in a search glossary, accessible with simple desktop tools.

• Create a data relationship repository to make the movement of data between systems transparent.

Monitor

Create data quality dashboards for individual key elements.

• Develop impact analysis so you can forecast problems before they arise and find solutions before it's too late.

• Have change management procedures in place Proper global settings so you can recover quickly if any changes cause problems.

proceedings

Establishes a regulated system for the file, data retention and destruction.

• Protects even outdated data by implementing security measures.

• Automate processes to eliminate arbitrary data decisions and the possibility of inconsistent and unreliable data.

Poor data quality can lead to many other negative outcomes: higher costs, failures in business processes, poor business decisions, poor customer service.

Following the steps above, if you plan to achieve the 100% data integrity, you can be sure you have a 100% of trust in your organization.