The Doppler Quarterly Special Edition 2019 | Page 48
Six Key Enablers
Enabler #1 - High Value Data Collection
It is essential that businesses understand their application
estate. We strongly believe in “High Value Data Collection,”
which focuses on defining upfront: the context, the data to
be gathered, how it will be consumed and the final
outcome.
Taking on a migration initiative requires a deep dive into the
enterprise’s portfolio. Gear your discovery and analysis
effort toward addressing the main business drivers and
specific goals. Based on those goals, target an analysis
exercise at the estate, application and infrastructure levels,
as well as specific to an individual component. We recom-
mend that every organization perform an analysis at each
of these levels based on various business drivers.
The following are the typical use cases:
• Defining general strategy for transformation across
the landscape, understanding common patterns and
identifying first movers (Estate Level Analysis)
• Addressing challenges for a specific application port-
folio for a line of business (Application Level
Analysis)
• Addressing specific pain points, such as middleware
or database transformation (Component Level
Analysis)
• Addressing more specific business drivers, such as
“exit a data center,” which may require an infrastruc-
ture-centric analysis
46 | THE DOPPLER | SPECIAL EDITION 2019
The type of data required for each of these use cases varies
from general application information to asset details,
detailed architecture and dependency information.
Even though creating a data model to define exactly what is
needed for each of these analyses should be a no-brainer,
many organizations struggle with this. Issues range from
not finding required information to being overwhelmed
with the amount of data, as well as the time and effort
needed to gather it.
We recommend that organizations spend up-front time and
effort creating a data model that defines the use cases with
the following requirements:
• Asset information
• Additional analysis attributes
• Data gathering mechanisms
The data gathering mechanism can range from self-service
questionnaires to discovery/monitoring tools, to CMDB
sources. Many discovery tools have additional capabilities
for analysis, including cost analysis, architecture recom-
mendations and platform recommendations.
In summary, organizations need to enable high value data
collection through the proper definition of use cases, asset
details and other functional data that is key to the analysis.
They also need a robust discovery mechanism that can
gather all the required information and maintain it in a
repository to use in further stages of analysis and eventual
migration, if required.