Managing Cloud Data Warehouse and Analytics: a guide for Chief Data Officers (CDOs)
What Do CDOs Need to Consider?
This blog was developed from a Thought Leadership whitepaper written by Maria Villar. Managing Data in the Cloud is a guide to help Chief Data Officers (CDOs) manage data sources, quality, processes, analytics, and more. This is Part Two in a three-part series.
A data lake or a data warehouse as a service (DWaaS) are often created to perform analytics or collect and store diverse information from numerous sources in a data warehouse.
With data warehousing in the cloud, an individual department can acquire nearly unlimited computing power and data storage with just a few clicks. Total cost of ownership (TCO) is also key. The DWaaS pricing model is set up to charge only for resources used. This means no forecasting or paying for long-term needs or up-front costs.
What about flexibility and scalability? A cloud data warehouse can scale up or down dynamically, as needed.
Now more than ever, security and disaster recovery are on a CDO’s mind. In many cases, cloud data warehouse providers offer stronger data security and encryption than that available in many in-house, on-premise data warehouses. You may also configure your cloud system to ensure data is automatically duplicated and backed up to minimize the risk of lost data. Remember to verify that your service-level agreements cover these requirements.
Cloud data warehouse providers often include new technologies, such as machine learning, artificial intelligence, and tools for data analysis which can easily be integrated into your solution.
Trusted Data Sources
The cloud warehouse integrates and persists many sources of data, including internal company data and external third-party data. With multiple possible data sources for the same data type and field, a CDO must provide guidance on the authoritative trusted source for each of the critical fields in the cloud repository.
Data Quality Management
There are no inherent data quality benefits with a cloud data warehouse. Data quality must still be proactively managed by the CDO team. Many of the same master data quality practices and on-premise warehouse data quality practices apply.
For the cloud data warehouse, the usual data quality parameters are still relevant:
A few tasks for cloud warehouse data quality management:
- Developing an automated, proactive archive-and-delete calendar and rules for “end of purpose” checks.
- Managing high data volumes and real-time data makes automation and real-time testing.
- Profiling data prior to acquisition to ensure the quality level fits the purpose.
Analytics in the cloud offer several benefits, such as speed of innovation and operation, greater cost savings, secure access to strategic insights, real-time analysis of data regardless of on premise or in the cloud, increased mobility, and the potential for more accurate and timely forecasting. These make cloud analytics a high-impact driver of innovation and value.
Cloud data management platforms that manage all the data tasks in a DWaaS are becoming more available, including those from traditional data management software vendors. These platforms are comprehensive and include integrated services, such as data discovery and cataloging, data ingestion and integration, data quality, and data governance.
In our next edition, we will discuss managing third-party data providers in the cloud.
For more information
- Read all three of the blog posts in the Managing Data in the Cloud series on the SAP Community (by searching on “Managing Data in the Cloud Series”)
- Take the outcome-driven data strategy master class. Start the video course and get the workbook.
- Read the full whitepaper, Managing Data in the Cloud, discussing each topic in more detail.
- Visit http://www.sap.com/datastrategy for tools to help you on your journey to creating an outcome-driven enterprise data strategy.
- Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion