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How to get started with Data Management

When you have identified the need for Data Management, NNIT has a fast and proven way of getting you started on utilizing your data. This article gives you insights into our approach.​

By using NNIT for your next Data Management, you will get:

  • Plan and operation of your new way of working with data. We have a structured approach for assessing your current state of Data Management and we help you realize the future state.

  • Higher data quality and improved transparency, thereby creating the foundation for digitalization and digital transformation using data assets.

  • The possibility to break down data silos, get control of your data assets, and achieve agility by introducing a controlled self-service.

  • An architecture that can expand across data domains, organizational boundaries, technologies, and cloud vendors.

The NNIT Data Management Framework

Overall approach

At NNIT, Data Management is not merely a technology, but rather an extensively organizationbased discipline where people, processes, data, and technology interact. NNIT’s project approach for the implementation of Data Management appreciates this basis and has always focused on the interaction between the business, its strategies, people,
processes, architecture, and technology.

Furthermore, our implementation approach is based on a combination of sequential and iterative execution. Basically, we recommend that initial scoping and baselining be carried out sequentially, whereas design and implementation should take place iteratively, governed by the maximum business value. We believe that preparing various Proof of Concepts is important for a project to be successful and for value to be generated subsequently.

Our approach is technology-neutral, but NNIT has a strategic partnership with a world-leading Data Management technology supplier – Informatica.


The first part of our approach to Data Management ensures the establishment of the pain/gain narrative. In this phase, we take a look at “what we want to achieve and the identification of the various stakeholders.” The results of this phase define the initial scope and adapt the methodical approach to the rest of the project.

Data management analysis

In this phase, the customer’s maturity for Data Management is investigated and described. This initial mapping forms the basis for the further work toward greater maturity. NNIT’s maturity model is built on five levels, going
from initial maturity where there is no vision but an awareness of a problem to optimization where controlled Data Management is the de facto way to do things, data is considered an asset, and we continue learning more and improving our use of data. The purpose is to establish a basis for determining the goal of the intended maturity level forming the basis for the subsequent solution design phase.

Solution design

In this phase, we address the goal vision(s) established in the analysis, creating a number of scenarios of how to achieve that goal. Each scenario is evaluated against ongoing initiatives and a related business case. Generally, a scenario will include a number of scenario preconditions, an overall goal architecture, a solution architecture proposal as well as one or more proposals for Proof of Concepts suitable for testing hypotheses. After an evaluation, a solution scenario is selected
and broken down into feature requirements and to-be designs. Finally, a road map is established together with a plan for releases and iterations as well as the related goals, among other things.


The next step is to execute and implement the initial product backlog. NNIT recommends a staged and iterative approach with agile but controlled configuration management and risk control. The procedures and processes put on top of configuration management depend on the data domain and its GxP classification. The iterations are driven from the
initial backlog that has a priority based on establishing organizational capabilities, architecture support, specific data domains, or a combination of these, for example. Our recommendation is to take on one data domain at a time.


After the initial staged implementation, you will start realizing the value and solving the goals set up for the project; HOWEVER, getting in control of Data Management is not primarily a matter of technology implementation but rather an ongoing journey toward higher maturity. Some of the achievements and deliverables could be
guidelines for the onboarding of new GxP and none- GxP data domains, target architecture, and a road map that can be used for onboarding and decommissioning systems, data governance processes, and trained people, but the major achievement is the ability to utilize data in an agile, cost-efficient, and controlled manner. You will be on the way to using data in your digital transformation while maintaining a high level of compliance.


Article written: January 2019




NNIT Life Sciences+45 7024 ​​ Life Sciences



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