Why you need an integrated data lifecycle solution
Nearly every company that has gone through a digital transformation has struggled with how to best utilize the vast amounts of data being collected. Indeed, we estimate that for most companies, 85%-95% of data is never fully utilized, and therefore wasted.
There are many stages in a data lifecycle, including acquisition of the data, creation of data engineering/data sets to impart meaning to the raw data, bulk storage of the data for further use/analysis, database creation for exploring the data, and finally capability to use advanced analytics and/or Machine Learning to extract insights from the data not available through mere reporting, all while maintaining data security and full regulatory compliance. The challenge for many organizations is how best to put together such a system, while keeping costs reasonable and minimizing time to deployment/operation, as well as the challenge of presenting the data in a meaningful way so that people can actually gain insights from it.
What’s needed is a way to deal with the entire lifecycle of data from acquisition to analysis for insights, while also maintaining the advantages of open source and an ability to utilize on-prem and/or hybrid or cloud native computing. Data warehouses have been available for some time and can handle the storage and delivery, but they don’t provide a complete solution. Many organizations have implemented data clouds, whether through pure open source (e.g., Apache Hadoop) or as commercial products (e.g., Talend, Informatica, Amazon Redshift, IBM, SAP, Oracle, etc.), but this does not solve the entire data lifecycle challenge, and often forces the use of many add-on disparate products that may not be easily integrated.
Nearly every company that has gone through a digital transformation has struggled with how to best utilize the vast amounts of data being collected. Indeed, we estimate that for most companies, 85%-95% of data is never fully utilized, and therefore wasted.
There are many stages in a data lifecycle, including acquisition of the data, creation of data engineering/data sets to impart meaning to the raw data, bulk storage of the data for further use/analysis, database creation for exploring the data, and finally capability to use advanced analytics and/or Machine Learning to extract insights from the data not available through mere reporting, all while maintaining data security and full regulatory compliance. The challenge for many organizations is how best to put together such a system, while keeping costs reasonable and minimizing time to deployment/operation, as well as the challenge of presenting the data in a meaningful way so that people can actually gain insights from it.
What’s needed is a way to deal with the entire lifecycle of data from acquisition to analysis for insights, while also maintaining the advantages of open source and an ability to utilize on-prem and/or hybrid or cloud native computing. Data warehouses have been available for some time and can handle the storage and delivery, but they don’t provide a complete solution. Many organizations have implemented data clouds, whether through pure open source (e.g., Apache Hadoop) or as commercial products (e.g., Talend, Informatica, Amazon Redshift, IBM, SAP, Oracle, etc.), but this does not solve the entire data lifecycle challenge, and often forces the use of many add-on disparate products that may not be easily integrated.
While open source software/systems seem to be very attractive, especially from a cost perspective, the “roll your own” approach to implementation of a functional solution is often fraught with challenges, and “free” is not really “free”. Time to full operation is substantially reduced by choosing a complete solution, as is the complexity of ongoing operations and support. This approach can save enterprise deployments tens of millions of dollars over the long term.
