Data Management
Data management is an administrative process that includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users. Organizations and enterprises are making use of Big Data more than ever before to inform business decisions and gain deep insights into customer behavior, trends, and opportunities for creating extraordinary customer experiences.
According to the recent UN report, 90% of the world’s data has been created in the last two years and is increasing by 40% each year. Companies certainly notice this new trend in their business and try to adapt to it as much as possible by creating special data science organizational units, investing in market and product research, etc. Rapid and intensive data exchange changes the relationship between manufacturers and consumers, as is the case with companies like Apple, Netflix and Google, but also smaller ones. The potential of using data science to improve business is not yet fully known, but it is known with certainty that it is huge and opens up entirely new opportunities for companies.
The transition of a company to a business based on data science is a certain form of change, but data science can also significantly contribute to change management policy. The wealth of every company is in the large amount of data at its disposal, both on work processes and on clients and employees. However, if this data is not used, it is practically worthless.
A concrete example of the use of data science in change management are computer programs that can, after data collection and processing, provide more accurate estimates of the outcome of a particular change. Unlike people who do not have the ability to rationalize one hundred percent, computer programs exclude subjective factors and offer optimal solutions. They are also faster in calculations than humans and can spot and correct mistakes more easily. In this way, mistakes arising from inadequate change management such as employee dissatisfaction and lack of enthusiasm, monetary damage, and loss of credibility of those in leadership positions can be avoided. Finally, data science greatly facilitates the work of coordinators, leaders, and managers and leaves room for resource management.
According to the recent UN report, 90% of the world’s data has been created in the last two years and is increasing by 40% each year. Companies certainly notice this new trend in their business and try to adapt to it as much as possible by creating special data science organizational units, investing in market and product research, etc. Rapid and intensive data exchange changes the relationship between manufacturers and consumers, as is the case with companies like Apple, Netflix and Google, but also smaller ones. The potential of using data science to improve business is not yet fully known, but it is known with certainty that it is huge and opens up entirely new opportunities for companies.
The transition of a company to a business based on data science is a certain form of change, but data science can also significantly contribute to change management policy. The wealth of every company is in the large amount of data at its disposal, both on work processes and on clients and employees. However, if this data is not used, it is practically worthless.
A concrete example of the use of data science in change management are computer programs that can, after data collection and processing, provide more accurate estimates of the outcome of a particular change. Unlike people who do not have the ability to rationalize one hundred percent, computer programs exclude subjective factors and offer optimal solutions. They are also faster in calculations than humans and can spot and correct mistakes more easily. In this way, mistakes arising from inadequate change management such as employee dissatisfaction and lack of enthusiasm, monetary damage, and loss of credibility of those in leadership positions can be avoided. Finally, data science greatly facilitates the work of coordinators, leaders, and managers and leaves room for resource management.
While some companies are good at collecting data, they are not managing it well enough to make sense of it. Simply collecting data is not enough; enterprises and organizations need to understand from the start that data management and data analytics only will be successful when they first put some thought into how they will gain value from their raw data. They can then move beyond raw data collection with efficient systems for processing, storing, and validating data, as well as effective analysis strategies.
“Data really powers everything that we do.”