Data Management for Business Resilience

 

Data has become one of the crucial components for many businesses, without which businesses may not be able to function appropriately. Data is generated at a large scale and is used for many purposes, especially in decision-making activities. To ensure that data is used strategically and maintained perfectly, data management solutions are leveraged by organizations. In this article, you will get to know the significance of data management from a business perspective.

What is data management?

It is a platform wherein procedures, practices, policies and architectures are developed so that the data lifecycle can be managed accordingly. Data management is the process of keeping, collecting and using data in a secure, cost-effective and efficient manner. Data management helps connected things, organizations and people optimize data usage to yield maximum benefit by making better informed decisions.

 

Data management principles:

l  Data is regularly accessed, created and updated across diverse data tiers

l  Data is stored both across multiple clouds and on-premises

l  Both rapid disaster discovery and high availability are provided

l  Data is used in an increased number of applications, analytics and algorithms

l  Ensuring effective data security and data privacy

l  Data is archived and destroyed in compliance with established compliance guidelines and retention schedules.

 

Following are the three key components of data management strategy:

1. Data governance: Developing best practices and processes regarding the usability, security, integrity and availability of the organization’s data

 

2. Data delivery: An accurate and consistent set of insights and conclusions or data drawn from data analysis available to customers both outside and within the organization and stakeholders

 

3. Data operations: Agile methods are implemented to deploy, design and manage applications on a distributed architecture. The barriers between development and the operations teams is removed, so that the entire data lifecycle can be improved.

 

Data management best practices:

1. A data science environment needs to be developed so that the data can be repurposed more efficiently: A significant amount of activities are automated by data science environments. Those tools are bought into practice that removes the need for manual data transformation, which, in turn, helps it easier to conduct testing.

 

2. The data is identified by developing a discovery layer: When a discovery layer is put above the organization’s data tiers then it enables analysts and data scientists to browse and search for useful datasets

 

3. Discovery is used to stay ahead of compliance requirements: There is an increase in compliance demands and hence it would be a wise step to use new data discovery tools so that data can be reviewed, which includes tracking, detecting and monitoring data.

 

4. Multiple data storage platforms are managed and integrated with a common query layer: When a standard query layer is employed, that spans across varied kinds of data storage, the data can be accessed centrally, no matter what format it is in or where it resides.

 

5. Autonomous technology is used to maintain performance levels across growing mindsets: AI and ML platforms can optimize indexes and continuously monitor database queries when those queries change. Rapid performance is maintained by this practice and the need to perform time-consuming manual tasks is eliminated.

 

Following are a few important data management programs available:

1. Tableau: Big data is scrutinized and then quickly translated into actionable insights. It is ideal for visualization and analytics

 

2. Segment: Data is collected from mobile and web apps and thus the information is readily available to the teams

 

3. Panoply: It is a cloud data management tool that combines, sorts, collects, stores and optimizes data without the need for modeling or data coding.  

 

Conclusion: If you are looking forward to implementing data management framework for your specific project, then do get connected with a well-established software testing services company that will provide you with a viable and methodical roadmap that are in line with your project specific requirements.