Value realisation can be described as the extracted value from project or from underlying processes. To extract a value, innovation is the key in every project.

1. Introduction
Value realization can be described as the extracted value from project or
from underlying processes. To extract a value, innovation is the key in
every project.
To apply innovation in process engineering or product engineering or
service engineering, we have to have a well established methodology to
tap the end results. End results should be measurable, if we take the
results on graph, the Euclidian distance between old process point (x1,y1)
and new process point (x2,y2) should be able to display the deviation,
which we can call it improvement by applying innovation model. If we are
able to put these measures in each and every phase of process or SDLC
life cycle, any of the fat process could be converted into a lean process.
Due to large heterogeneous components and the scale of the complexity of
interdependence of the components, timelines and business need, delay is
induced in a process by cross development team, cross service
engineering team, vertical specialized CoE’s, or Horizontal specialized
CoE’s.
Model of IT department
IT departments work in different model in a way the respective company is
structured. Normally a company is structured to mainly focus on the

2. improvement of bottom line i.e. profits. Revenue generating departments
are the factories of profits. The innovation model needs to be applied on all
the revenue generating factories first and could be extended to other
departments and processes. The figure below represents the most simple
and commonly found IT department working model
Ideally all SDLC phases are part of typical migration project.
Major tasks in process of migration
Tasks of the migration
 Requirement gathering
 Data collection
 Data segregation
 Functional Specification
 Technical specification
 Details design – High Level Design
 Details Design – Low Level Design
 Migration document for support
 Build, System Test, UAT, Production
 Test Plan
 Test cases
 QTP (Automated test tools)
 Manual testing
 Status Reporting

3. A peep into value realization
Value realization can be described as the extracted value from project or
from underlying processes. The dimensional elements should be applied at
each and every phase of project or processes to extract value.
Let me introduce some of the dimensional elements which could invoke
value as below.
 Innovation
 Optimization
 Foresight
 Increasing efficiency
 Re-invent
Applying the dimension at each and every phase or process to analyze and
realize the value. This process called as a model.
Value metrics analysis
The project estimation is normally done on the basis of the estimation
models. If we can compare the estimated efforts post applying value
realization model and do the analysis, the difference could be the value
which is extracted.

4. Task transformation post value realization model
Analysis: The phase identifies the suitable streams, interfaces or
components for the migration in one agile iteration process; this can be
automated.
Coding: coding on demand, to fill the gaps to fit the new software version.
This also could be automated to the highest level where possible.
Testing: The test process could be automated and identify the manual test
cases which needs to be executed, so that the regression testing can be
easily achieved in less time.
Data: One year data can be collected for testing, if we have an existing
system, best practice is to collect sample of all possible data formats of
input data. The phase can be diminished, or data collection and
segregation can be automated.
*50 components per quarter
*8hrs. Per day

5. Other variables affecting value
The Architecture task:
For Components, subjected for a change in the scope of migration, there
will be no existing data for testing.
Human Capability collaboration
 Like the challenge of resource retaining issues
 KT and the process from stage to stage
 Known issue database centralization
What next
The challenges include not just the obvious issues of scale, but also
heterogeneity, lack of structure, error-handling, timeliness, and
provenance, at all phases and stages of the analysis. These technical
challenges are common, however these are cost-effective to address in the
context of one domain alone. Furthermore, these challenges will require
trans-formative solutions, and can be addressed naturally by the next
versions of products. Addressing, these technical challenges to achieve
cost effective solutions can be done.