Data governance ensures that your data is of high quality, in accordance with data standards and also fit for purpose.
Next to compliance, data governance leads to major internal business benefits such as better data analytics and increased business efficiency (by not having to reformat or rework data), therefore shorter time to market/patient.
Data governance is control and oversight over your organization’s data management and datasets, including roles, responsibilities, and processes for ensuring accountability and ownership. A solid data governance framework enables an organization to manage its data as a strategic asset.

With XEVMPD, and now with IDMP, DADI, CTR and MDR, the demand for structured product data for regulatory submissions continues to increase. Managing all this data, spanning multiple functions, as well as keeping up to speed with ever-evolving data standards requires strong data governance.

In order to evolve to a data-drive organization, you will need accurate, reliable and consistent data across the organization. Iperion is eager to support your evolution and ensuring you get more value from your data.

Our Data Governance support consists of:

  • Identify how to get more value out of data in the most efficient way
  • Define and agree on who owns data, performs data maintenance and consumes data
  • Set up a Data Governance strategy & organizational structure
  • Identify where data should be sourced from
  • Increase data quality by implementing a Data Quality Framework and data quality rules
  • Improve an already established Data Governance organization

Our Data Governance framework consists of the following components:

  • Data Strategy
    Define and implement a clear strategy with concrete goals defined, this includes a governance charter, an operating framework, data scope and an implementation roadmap.
  • Organizational Structure
    An organizational chart, including an interaction model, is defined together with all required Data Governance functions and governing bodies.
  • Roles & Responsibilities
    Interrelated role descriptions, linking to the organizational structure with clearly defined roles & responsibilities for the different Data Governance roles, to facilitate ownership and accountability.
  • Policy & Standards                                                                                                                  
    Data Governance Policy that covers all key principles/aspects of data management used in operations of Data Governance. Sources of origin are identified, data standards & data definitions are established, and relationships/interfaces are mapped.
  • Processes
    Data management processes (describing how data needs to be managed) are defined covering all principles/aspects of the Data Governance policy, to be operational in all affected business functions. Data Governance Processes are defined to ensure that data is managed. Communication processes are established as part of the Data Governance framework and will facilitate communication of updates to policy, processes, standards and reporting.
  • Change Management & Training
    Identify, engage and train a broad range of stakeholders to instill a new data culture in the organization, including regular communication of Data Governance processes; Designed with specific users in mind and targeting awareness for the importance of Data Governance.
  • Data quality
    Establish Data Quality framework and define Data Quality rules. Ensure Data Quality is actively monitored, improved and that Data Quality issues are effectively remediated.

This allows companies to establish the correct data ownership, with associated business rules, ensuring data quality and enabling interoperability of data between systems.


  1. Assess
    We perform an assessment of the current state of data governance related organization, processes, technology and information including identified use cases.
    We define a high-level desired future state tailored to your specific needs, and a Data Governance Roadmap (high level)
  2. Design 
    We deliver an in-depth and specific design of the future Data Governance and identified use cases. a Detailed roadmap and implementation plan is created.
    Important is to create awareness, engagement and buy-in from relevant stakeholders together.
  3. Implement
    Within our approach we ensure that all elements are fully incorporated and seamlessly integrated.
    These principles are then further cemented within the organization by the quality procedures and business documents.
    During the implementation we make sure changes are properly communicated and implemented.
  4. Maintain
    Our experts can support in translating experience gained during the maintenance of your Data Governance. A periodic review on improvements on how to optimize your Data Governance. We also support in Data Governance maintenance activities.  Learn more about our managed services.

We can assist you in determining your current Data Governance maturity level and determining how to achieve the desired level using our best practice approach. We will assess your organization’s current state and desired future state for Data Governance and use this to create a practical implementation roadmap for getting there.


Typical challenges of Data Governance

Data Governance has not yet received the attention it deserves in the pharmaceutical industry. This is primarily due to the organic and historical growth of functions and departments. We have noticed that many pharmaceutical companies have either very limited Data Governance or no Data Governance at all. This results in the following:

  • Data entry in different departments is done with inconsistent definitions, leading to different business rules. As a consequence, data needs manual modification between systems and thus duplicate data entry or increasing complexity in data transfer between systems.
  • No Data lineage information resulting in limited transparency of dataflows. It is key to have full transparency of dataflows and know who changes data, how data is modified and in which systems. This to ensure data quality and compliance with data standards.
  • Roles and responsibilities for data are not effectively set, ownership of data is not clearly established within the organization, leading to unclear accountabilities for data sets.
  • Data quality issues are resolved reactive and manually, without investigating the root cause for a data quality issue. Hereby are preventive actions for data quality issues not established.
  • Lack of buy-in from all stakeholders and low awareness of benefits of Data Governance, either before initiating or during execution of a Data Governance project.
  • A lack of cooperation and harmonization, due to different needs of different stakeholders and no common understanding of Data Governance concept.
  • There is no ongoing monitoring of whether data is fit for purpose for your organization or whether the state of data is improving over time.

Iperion – a Deloitte Business has the experience and expertise to tackle these challenges, ensure that a Data Governance program is successfully executed and embraced by the organization.

What are the benefits of effective Data Governance

A well implemented and embedded Data Governance framework will result in proper management of the availability, usability, integrity, quality and security of data within your organization. Data Governance will also deliver the following benefits for your organization:

  • Improve Regulatory compliance
    The ability to evaluate, adapt and respond effectively to internal as well as external changes (e.g. Data standard and regulatory requirements)
  • Full clarity on responsibilities for data sets
    Accountability and traceability are important aspects that are managed by Data Governance. Defined accountability and responsibility for information in your system(s), enables clear escalation routes in case of requests of additional information or data quality issues.
  • Consistent data definitions
    Drive a common and shared understanding of data definitions – leading to consistent usage and less misinterpretations and rework. This paves the way for data integration and efficient exchange of data between systems, also cross-functional.
  • Solve data analytics and reporting issues
    Proper Data Governance eliminates any unclarity about the meaning of data and reporting. This allows for correct analysis of data and this in turn facilitates quick(er) decision making.
  • Reduce costs
    Solid information is extremely valuable to your business. Cost savings can be seen by reducing errors/duplicates and thus saving time in data correction efforts. Furthermore, Data Governance may increase revenue because more timely data may result in faster regulatory applications, which may result in earlier product registration / launch.
  • Improve Data Lifecyle management
    Improvements to address issues and opportunities in data management across the organization

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