Effective Data Warehouse Management Practices for Enterprises

Irrespective of the industry, size, or type of the business, enterprises need to design and implement the best data management practices that bolster their DataOps teams to gather, store and process information in their data warehouse.

CDOs should consider modernizing their data warehouse management approach based on the industry, client, and employee needs. DataOps teams need to align the data tech stack with short and long-term business goals to improve their business performance. The best strategic approach to managing data generated throughout all the channels will enable the business to accomplish its business goals with efficiency. Here are a few effective data warehouse management practices that CDOs can consider to transform data into valuable business insights:

Design and implement efficient Master Data Management (MDM) practices

Effective MDM practices can be designed and implemented to monitor work process that creates and establishes accurate, consistent, and verified master data as a system record. One significant challenge of this approach is that evaluating the reliability and accuracy of master data getting stored in the data warehouse, could be difficult.

DataOps teams need to ensure that they have effective data warehouse management policies in place that ensures the data quality is consistent throughout all the data channels.

Moreover, they also need to set work and information flows that do not lose out on information during the migration and identify the siloes in real-time. Enterprises that are able to successfully implement master data management practices will streamline their data warehouse management.

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Standardize data

Enterprises have different applications of data throughout their different business units. CDOs should consider gathering and structuring data before integrating the data sets into the data warehouse. The DevOps teams should customize the different data management tools to ensure they follow a consistent data structure throughout their organization. Developing a consistent data format throughout the organization helps DataOps teams to reduce siloes in data structures, schema, and formats. This approach is one of the most efficient ways to ensure that the data analyzed is reliable and structured throughout the organization.

Enforce real-time change data capture (CDC) strategies

CDOs can design and enforce stringent CDC policies that enable them to monitor all the changes made in the data sets and apply all the changes to the data warehouse in real-time. The DataOps can monitor, capture and save the changes in relative tables named change tables. This data warehouse management strategy enables businesses with a holistic view of all the data that has been changed in the past. Moreover, this approach is one of the most effective ways to minimize the impact on the channel while integrating the latest data sets into the data warehouse. The change data capture approach enables businesses to populate data analytics dashboards in real time and improve the data migration processes.

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Implement reliable extract, load, and transform (ELT) pipelines instead of extract, transform and load (ETL)

Designing and implementing ELT/ETL pipelines will streamline the data management practices. ETL is a traditional approach to managing data by gathering large volumes of transactional or event data in the staging environment and cleaning and structuring the information before loading it into the warehouse. ELT is a modern approach that strengthens the capabilities of cloud-based data warehouses. CDOs, instead of implementing an ETL approach, can go for ELT because it enables them to transform the data in a single place once migrated to the targeted data repository. Extract, load, and transform (ELT) pipelines will enhance the organization’s capabilities to save datasets of every type, such as unstructured data. This data warehouse management approach will help businesses to offer real-time access to all the authorized users and helps data analysts to save time while processing new information.