Assessment of your key data pillars
good adoption of data culture starts with a study phase with maturity
level assessment and the building of an elevator pitch. The maturity
level assessment on the key data pillars (data governance, data quality,
data management, data privacy, data integration, data preparation,
analytics), based on your current operational challenges and business
pain points, will enable you to identify the priority data topics to be
put on your roadmap.
During the elaboration of the roadmap, it is very important to define tangible objectives, deliverables and solutions
that will speak to the business and will ring the bell in people's mind
with concrete operational benefits. From these priority topics, and
from the overall corporate strategy, we will elaborate an elevator pitch
that will be used for explaining the objectives and the high-level
roadmap to the C-level first, then to heads of functions, and finally to
operational teams. No sponsorship, no credibility!
Evangelize a data-driven culture
There is a mandatory step of evangelization through the top-down levels of the organization pyramid to explain:
- WHY do we implement data governance in companies? – "It
is crucial that you define a data governance that answers strategic
objectives and operational pain points, and that all employees in the
organization understand WHY such a transformation"
- WHAT are the different components of the approach with their benefits?
- HOW are we going to implement (i.e. high level roadmap)?
This step is very important to get the concerns and the feedback
from the various levels and stakeholders. Along the evangelization
journey, the pitch and the presentation can be adjusted in regards with
the remarks of the business stakeholders.
Operational pain points and creating Planning document for implementation
a data governance framework based on the best fit for the main
objectives, business priorities, organization structure, and overall
culture and maturity level. Define mission and vision of the data
governance program. Establish and define goals, governance metrics and
success measures, and funding strategies. It is important to understand
that these planning activities may be performed on a recurring basis.
phase prioritizes on ensuring sustainability of data service
implementation and operation and identifying transformational
opportunities with efficient & cost-effective data services and
optimized data quality management.
the challenges of this phase might include the potential loss of
agility and flexibility for uniformed enterprise-scope governance
method. It is extremely important to balance the centralized control vs.
distributed management to account for unique local and departmental
Most data projects are done
following an agile or scale methodology. The data owners and operational
teams must be fully involved in the project execution through regular
reviews, the prioritization of the product backlog and through regular
intermediate solution development demos.
Final GO for Deployment
is also a very important step for supporting the adoption before the
business go-live. Before conducting training and testing, I always
suggest doing a final round with the business sponsors and the head of
functions in order to answer their last questions and to get their “blessing” before the deployment.
this phase, you will present them the final solution and the
organizations or processes changes that will be deployed. From there,
you can organize the User Acceptance Testing (UAT) and the training
phase ensuring that everyone feels comfortable with the future solution.
governance is one of the most important components of Enterprise
Information Management. It is interrelated with all other disciplines of
data management functions. The level of dependency might vary from
function to function. Master Data Management, as an example, would not
succeed without effective data governance. Styles and different focuses
of data governance require unique data governance activities to meet
these specific requirements. However, as the term “Governance”
indicates, the foundation of any governance involves certain levels of
control. Practicing data governance is about finding the right amount
and right level of control. Taking an iterative approach will mitigate
implementation risks and help an organization focus on the right level
of control to be effective and successful in managing their most
important asset – data.