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Data Capability Gap Analysis Level 2 to 3
Assessment Block
People and Culture
Process
Data Activities
Gap Description
Fixing problems is more important than improving data
management.
Creating any kind of data management capability is not seen
as important.
Data issues are all IT issues.
Nobody outside of IT takes any interest in the value of our
data.
Disruption causes issues due to lack of resource to deal with
it.
Even where basic processes exist, they fail when key staff
are unavailable.
Business change tends to happen with little consideration
for data assets.
Processes around data don't fit into wider business
processes.
Data Capabilities still fall well short of appropriate data
management.
Very large efforts are required just to create the minimum
outputs.
No standard definitions exist to allow sharing/consolidation
of data.
Problem resolution tends to be fix and forget, lessons not
learned.
Data Quality aspirations are not consistent or maintained.
Gap Mitigation
Find a senior sponsor with an interest in data and educate on value of
data management.
Use Data Landscape to show why increasing capability is important in
terms of cost and effort of known output.
Use Data landscape to show who should be accountable for data
outside of IT and look for advocates.
Link key datasets (e.g. Student Record) to organisational initiatives.
Create simple business processes for known obligations and prioritise
these.
Ensure business processes are well understood and up to date.
Integrate with wider processes.
Speak with project management/business change staff to ensure they
understand impact on data must be assessed early.
Work with business analysts/process owners to workshop through data
implications.
Show value of better data management to senior sponsors/HR. Gain
commitment for training and investment.
Agree what key outputs are. Create a team to develop lean processes.
Investment case for more staff if necessary.
If Data architecture group exists, work with this team, otherwise
consider external capability. Focus on core datasets.
Implement a root cause analysis approach and embed with wider data
community.
Work with information asset owners to agree data quality in line with
importance of data sets.
Assessment Block
Technology
Gap Description
Data - even for core data assets - is not reconciled.
Physical and Logical data modelling is patchy leading to poor
development.
Technology only supports at best our data activities.
Tools are being developed all over the organisation.
There is not investment case for data management tools.
Gap Mitigation
Show value of 'single version of the truth'. Requires senior sponsor to
drive initiative in Bus.
If data architecture group exists, work with this team, otherwise
consider external capability. Focus on core datasets.
Understand most important requirements and integrate with roadmap
for other technology solutions.
Workshop what is required and look to build capability around a single
initiative.
Investment cases must show direct line between wider business
benefits or increased data capability.
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