We already made entries of #DataOps (data operations), but to refresh the memory we say: it is the combination of people, processes and technology that allow us to handle data that is useful for #developers, #datascientist, #operations, applications and tools (eg #artificial #intelligence) , allowing to channel the data, keep them safe during their life cycle and configure a #governance over them.
The faster we manipulate and deliver the data, the faster the #growth for the business will be due to the use of the information, therefore, its objective is to promote data management practices and procedures that improve the speed and accuracy of the analysis.
The idea of this post is to make a short-list with 5 basic problems that are solved with the implementation of DataOps in an organization.
Let’s see what DataOps solves:
#Bug fixes: In addition to improving the agility of development processes, DataOps has the power to boost time to respond to errors and defects by significantly reducing times.
#Efficiency: in DataOps, data specialists and developers work together and, therefore, the flow of information is horizontal. Instead of comparing information in weekly or monthly meetings, the exchange occurs regularly, which significantly improves communication efficiency and the final results.
#Objectives: DataOps provides developers and specialists in real-time data on the performance of their systems.
#DataSilos: DataOps faces the data silos that are generated in different departments or management of a company, many groups see their operations as inviolable “fifths” in which each silo is a barrier to success to implement better management strategies of data. The implementation of a correct governance is crucial for obtaining all the data sources that the organization requires to meet its business objectives.
#Skills: It is a fact that data professionals do not abound. The lack of availability of the right people to manage #BigData & #BI projects means that the projects are not executed in a timely manner, or worse, that they fail. It is a mistake to put more data on a computer that does not have the knowledge and resources to handle it.
We invite you to join our Linkedin Group of “DataOps in Spanish”