DATA OPERATIONS

SUPPLY YOUR
DATA AND INCREASE
AVAILABILITY

With the hudge data volumes of today organizations have to think about how they can make it available for use. Distributing the data through systems and technology to where it creates value can however be cumbersome.

Because the data volumes are so large nowadays and expected to keep growing exponentially, having a good strategy to deal with this is of the utmost importance. As it involves many people accross the organization, touches numerous technologies and systems and plays a part in many processes you really need to think it through. Thinking of how to structure the approach to this in Data Operation is an important step.

DATA OPERATIONS

METHODOLOGY

Data Operations is a methodology focused on bringing data to where it is used in the organization and where it brings value to the busines. In Data Operations people, tools and processes are brought together to make data available in such a way that it can be used for the purpose of, for instance, analytics.

PURPOSE

The purposes of Data Operations is to bring data from source systems through different layers of technology to its inteded target, one of which can be a datawarehouse.  This is where the data is stored in a way that is optimally structured for analytics and reporting.

REALITY

The reality is that regardless of the effort that is put into Data Operations, it can still be very cumbersome. Due to the complexity bringing data from  source systems to the datawarehouse, it is mostly still complex, lenghty, costly and involves a lot of manual work. This is where Datawarehouse Automation comes in.

DATAWAREHOUSE

As said, an important element in your data operations is the datawarehouse where things come together. But do you recognize any of these questions?

  • The business is hampered by the long delivery times to make data available for new reports or analytics.

  • Required or desired changes to the DWH are slow or impossible.

  • Creating business value from the DWH is too costly and time consuming.

DATA WAREHOUSE AUTOMATION

The most efficient way to address these issues is by automating the entire DWH-process – from delivering raw data to exposing reliable information – covering all aspects of the data to information creation process. The purpose of Datawarehouse Automation is to standardise and automate all the individual steps in the development cycle of a datawarehouse. With Datawarehouse Automation the development cycle can be accelerated while assuring the quality and consistency of the data. By automating the steps in the warehouse lifecycle all repetitive steps like design, development, deployment and any other operational tasks are eliminated. The benefits of automation are huge. It’s faster, more flexible, reduces errors and easier to manage.