Why would you want to simplify Data Management?
Simplifying Data Management would relieve our daily struggles and give us more time for doing other
things that help the business and ourselves move forward with projects and knowledge.
The bigger question is, How?
We are all aware that every top solution has a way to simplify our environment in some form or
fashion…but are they the right fit for our business needs?
Think about Cloud and Object Storage
If you are looking at saving money on your budget at scale with the ability to promote all tiers of data,
then there are no better options available like adding on-premises object or cloud storage. The main
challenges of integrating cloud is how to determine what data goes where and how to migrate the
correct data to the cloud. There are Object Storage/Cloud solutions available that are able to meet or
exceed your application data requirements using data virtualization, metadata management, and
machine learning to help make this a simple and automated process. Using the right objectives, you will
be able to determine how much performance and resources are needed for each application, while
being able to stay within the IT departments spend to store that data set. In either location (on-premise
or cloud), when data gets cold, depending on the business requirements, you can use automation and
application awareness to move data to different performance levels. These models will also let you
determine what the business timing is for data to be considered cold; 1 month, 6 months, or a year of
inactivity — IT can set parameters, move the data off tier I performance storage to lower tiers, but be
kept highly accessible in case it is needed again.
If your team is thinking cloud, it’s important to make sure data will be able to move seamlessly off-
premises as well as being able to move seamlessly back on-premise. Remembering that local Object
Storage can help if you are forced to rehydrate an entire volume because, if rehydrating in the cloud,
you could end up paying much more than what you bargained for. This is because it is generally
inexpensive to move data to the cloud, but costly to bring it back again. Making sure you can pull back
data at file-level granularity will help you keep costs low while enjoying the flexibility and agility that is
driving rapid cloud adoption in the enterprise.
Once you’ve chosen the platform, gained insight into your data, and given your applications awareness
to your diverse storage resources, the final step is automating. Object storage and cloud systems can
provide these capabilities within a single system or vendor ecosystem, but metadata engine software
can automate management according to IT-defined objectives, even across different vendors.
With machine learning on the rise, it is no surprise that this type of intelligence is also coming to data
management. Smart Object Storage and Cloud Solutions can detect patterns such as; when internal
business data gets hot at certain times, and if IT’s data management objectives allow, once used the
data can move back to performance level of storage so that items such as reports can run at a faster
speed. There is no argument that data growth will continue to be on the rise. Given that most IT
departments’ budgets and staff are not increasing to help teams deal with: data growth, visibility,
integration, cloud adoption, and automation, which are critical to giving IT staff the time needed to
manage strategic projects instead of spending their days working as storage traffic cops. Determining
how to add these capabilities to your enterprise today is essential to scaling to meet the challenges that
face all businesses working to use their data as a tool that helps them lead their industries.