Data Management: Best practices for effective data management
Posted: Wed Jan 22, 2025 8:43 am
In an increasingly competitive data economy, discover the golden rules and best practices for effective data management.
Bringing data to life is a new mantra from Informatica, so businesses can achieve transformative results. There is no longer any doubt about the value of information and the role it plays in business today. In fact, the true potential for business growth lies in data and its endless opportunities. Let's look at some methods and best practices for effective data management.
The first best practice in data management is to overseas chinese in australia data the company's current needs in this area. Although good practices can always be applied, there are cases in which there is still a lot of work to be done, but the first thing to do is to find out the starting point.
Importance of data in digital transformation
HubSpot BLOG
Data is the oil of the 21st century and analytics is the combustion engine
Source: Forbes
Data management, self-assessment
One of the most effective methods to ensure the efficiency of the data management plan is to make a prior effort to self-assess. To do this, there is nothing more practical than placing the company itself in one of the following evolutionary stages towards maturity in data management and governance :
1. Initial stage
It lacks standards.
Reactive approach.
You do not have a master data management plan.
Non-existent strategy.
2. First contact
Setting standards.
Definition and communication of the strategy.
Identifying the master data management plan.
3. Positioning
Key management indicators defined and providing metrics.
Operational business glossary.
Documented compilation of rules applicable to data.
Master data management plan executed.
Implementation of the supporting technology framework.
Deviation detection and monitoring system implemented.
4. Proactivity
Information silos fully integrated with master data systems.
Proactive approach to data management.
Feedback aimed at continuous improvement in operation.
Causality analysis integrated into the feedback process.
Automation of performance measurements.
5. Advanced stage
Global alignment of the entire company with the strategy.
Complete and efficient application of feedback and automation processes.
Processes, people and technology working in harmony.
Needless to say, the work that remains to be done is inversely proportional to the degree of maturity achieved. Companies in an advanced stage are capable of enjoying effective data management and will only have to worry about maintaining and monitoring it based on indicators; while those who are in earlier stages still have to build the foundations of their data management plan.
Bringing data to life is a new mantra from Informatica, so businesses can achieve transformative results. There is no longer any doubt about the value of information and the role it plays in business today. In fact, the true potential for business growth lies in data and its endless opportunities. Let's look at some methods and best practices for effective data management.
The first best practice in data management is to overseas chinese in australia data the company's current needs in this area. Although good practices can always be applied, there are cases in which there is still a lot of work to be done, but the first thing to do is to find out the starting point.
Importance of data in digital transformation
HubSpot BLOG
Data is the oil of the 21st century and analytics is the combustion engine
Source: Forbes
Data management, self-assessment
One of the most effective methods to ensure the efficiency of the data management plan is to make a prior effort to self-assess. To do this, there is nothing more practical than placing the company itself in one of the following evolutionary stages towards maturity in data management and governance :
1. Initial stage
It lacks standards.
Reactive approach.
You do not have a master data management plan.
Non-existent strategy.
2. First contact
Setting standards.
Definition and communication of the strategy.
Identifying the master data management plan.
3. Positioning
Key management indicators defined and providing metrics.
Operational business glossary.
Documented compilation of rules applicable to data.
Master data management plan executed.
Implementation of the supporting technology framework.
Deviation detection and monitoring system implemented.
4. Proactivity
Information silos fully integrated with master data systems.
Proactive approach to data management.
Feedback aimed at continuous improvement in operation.
Causality analysis integrated into the feedback process.
Automation of performance measurements.
5. Advanced stage
Global alignment of the entire company with the strategy.
Complete and efficient application of feedback and automation processes.
Processes, people and technology working in harmony.
Needless to say, the work that remains to be done is inversely proportional to the degree of maturity achieved. Companies in an advanced stage are capable of enjoying effective data management and will only have to worry about maintaining and monitoring it based on indicators; while those who are in earlier stages still have to build the foundations of their data management plan.