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Deploying analytics data to the
cloud is top of mind for many organizations. But making the
transition away from an on-premises model is about more than
transferring data. More often, factors like current data management
processes, IT skills, and access to critical business logic will
determine whether your move to the cloud will be easy or more
There are three common challenges that organizations often face
when deploying analytics data to the cloud that can impede success.
Let’s look at these challenges and examine what an organization
can do to overcome them.
The reality is that most companies still have some level of
on-premises data, and some have very high volumes of data residing
behind their private networks. Moving this data over a limited
bandwidth internet connection can present a significant
Generally, organizations maintain a highly secure environment.
This means that getting in to access the data can require setting
up point-to-site VPNs or other complex networking systems.
Organizations not only need access to this data stored in their
private networks, they also need to extract analytics data and move
it into the cloud fairly quickly. Since it’s both inexpensive and
infinitely scalable, Azure Data Lake provides an excellent storage
destination for analytics data in the cloud. However,
adapting to data lake concepts like operationalizing the file
structures and delta loads are complex, time consuming and require
continued use of expensive skills.
So how do you address these challenges and get your data into a
data lake quickly and reliably?
One way is by leveraging the power of automation to simplify the
process and addresses common issues. Technically, here’s how it
Managing analytics data in the cloud requires organizations to
accept that they must adapt to rapidly changing technology. We all
know that both the volume and velocity of data pouring into an
organization is accelerating, and companies are struggling to keep
up unless they have the data architecture in place to do so. For
some businesses, this means that platforms used to manage this data
are forced to evolve with it in a reactive fashion.
Organizations with legacy data warehouses are being forced to
move to cloud solutions that can scale just to keep up with the
growing volume of analytics data. And even these cloud solutions
are evolving. Keeping up with these changes presents a massive
challenge for organizations. Organizations often find that data
professionals need to update their skills regularly and IT teams
need to rebuild analytics infrastructure every few years.
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