DataOps Management is the core philosophy of Agile Data Engine. It guides us in fulfilling our purpose: to unlock the potential of data to create business value now and in the future - in a sustainable way.
introducing dataops management
DataOps Management is about implementing, operating and managing analytical data products and the data platform, so that productivity is maximized, operation costs are minimized and the investments on data continue to pay back longer.
DataOps Management honors the battle-tested and proven fundamentals in data management, and combines them with new and evolved thinking, methods and technologies.
Read about the practice of DataOps Management that bring the principles to life:
'Data as a Product' thinking
DataOps
the challenge
New data is being created at an exponential rate. Artificial Intelligence will be used everywhere and in every way. To stay competitive in the future, almost all businesses must utilize data and analytics effectively in their operations.
At the same time, as the demand is surging, the resources (e.g. skilled people, energy) are increasingly scarce and expensive. The pressure is growing higher towards companies: they need to be able to manage and harvest their data assets in a productive and sustainable way.
The winning companies will be the ones who can best manage the development investments and operations on data. This will define the success of companies in the future.
ask yourself this:
Do you have the data you need to run and manage your business?
Still today, most line-of-business leaders say that they do not have the data they need. Data teams are not able to deliver what they need, and with a speed that is in line with the demand of business.
One can wonder how this is possible, after all the advancements in technologies and methodologies. The benefits of exercising agile development practices are not realized as promised. The pace how new technologies and tools are being born has gotten out of hands. The modern data stack has become a postmodern nightmare. The tail is wagging the dog.
the fundamental flaws
Data platforms are designed and implemented with a short-sighted mindset. Throwaway culture prevails as technologies and people come and go.
Data platforms are procured and managed like technology projects, instead of business driven products - as they should be.
The focus is not on team productivity, but on hero individuals or on short term costs. There is not enough standardisation of work and reusability of deliverables.
The output and work are not measured. What you don’t measure, you cannot manage. Without measurement and management, continuous improvement does not happen.
