Considerations for an Effective Data Strategy
Posted: Thu Feb 13, 2025 3:48 am
Data integration activities such as preprocessing and normalizing the data become complicated activities when the data sources generate data in different formats. This activity can incur huge additional costs and is time-consuming as well. In the case of edge analytics, data integration activities will be performed at the edge and cambodia whatsapp number data the data is typically not expected to be in different formats.
Cloud analytics solutions provide a complete perspective of the overall state of the business as they have access to all data sources. Therefore, for organizations to analyze key performance indicators, they rely on cloud analytics solutions. Edge analytics provides metrics associated with a particular deployment or location and does not represent or provide the performance of the entire organization.
The primary purpose of a data strategy is to identify mechanisms for measuring key metrics that are part of the overall business strategy. Therefore, a data strategy needs to consider all data sources, identify appropriate preprocessing and modeling algorithms, and eventually feed the processed data into an analytics solution for detailed insights and actions.
In the case of organizations deploying interconnected edge solutions, massive amounts of data are expected to be generated in each of the edge sites. Therefore, an effective data strategy needs to consider cost implications while processing these massive amounts of data. Here are some key considerations for building an effective data strategy.
Cloud analytics solutions provide a complete perspective of the overall state of the business as they have access to all data sources. Therefore, for organizations to analyze key performance indicators, they rely on cloud analytics solutions. Edge analytics provides metrics associated with a particular deployment or location and does not represent or provide the performance of the entire organization.
The primary purpose of a data strategy is to identify mechanisms for measuring key metrics that are part of the overall business strategy. Therefore, a data strategy needs to consider all data sources, identify appropriate preprocessing and modeling algorithms, and eventually feed the processed data into an analytics solution for detailed insights and actions.
In the case of organizations deploying interconnected edge solutions, massive amounts of data are expected to be generated in each of the edge sites. Therefore, an effective data strategy needs to consider cost implications while processing these massive amounts of data. Here are some key considerations for building an effective data strategy.