Reliable and consistent data is a key ingredient to assess performance and make critical management decisions.
Data governance is a set of processes, controls and standards that ensure that your data is formally managed throughout the organization. 

Data governance is a set of processes, controls and standards that ensure that your data is formally managed throughout the organization. A data governance program defines:

  • what data is managed
  • what data is business critical
  • where data comes from
  • who owns data and who consumes that data

Iperion can assist you in defining and implementing a holistic data governance framework for life sciences industry.


Our framework consists of the following aspects:

  • Data management
    Establish data ownership, data definitions, identifying sources of origin and relationship/interfaces
  • Data quality framework
    Create Quality plan, establish KPI’s, reporting, establish data quality rules
  • Governance
    Define governing bodies and their interaction, roles and responsibilities
  • Procedures
    Write procedures to operationalize the data governance

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

Our approach to Data Governance implementation

  1. Assessment
    Iperion performs an assessment of the current state of data governance related organization, processes, technology and information.
    We define a high-level future state in a general roadmap.
  2. Design
    We deliver an in-depth and specific design of the future data governance solution, including a detailed roadmap.
    Important is to create awareness, engagement and buy-in from relevant stakeholders together.
  3. Implementation
    Within our approach we ensure that data management, data quality and governance principles 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. Maintenance
    Our experts can support in translating experience gained during the maintenance of your Data Governance by executing periodic review. In addition, we can provide training on the current process and workshops to establish new procedures. Learn more about our managed services.

Typical challenges of Data Governance

Within the pharmaceutical industry Data Governance hasn’t yet received the required attention. This is mainly due to historic and organic growth of functions and departments.

  • Lack of alignment between silos which resulted in duplicate data storage, different definitions and use, duplication of systems and disjointed procedures leading to increased inefficiencies and risk of incompliance.
  • Lack of buy-in from all stakeholders either before initiating or during execution of a data governance project.
  • Due to different needs of different stakeholders and no common understanding of Data Governance concept, a lack of cooperation and harmonization can occur.

What are the benefits of Data Governance

A well implemented Data Governance will result in proper management of the availability, usability, integrity, quality and security of data within the organization:

  • Data Governance saves money
    Solid information is extremely valuable for your organization. By reducing errors/duplicates and therefore saving time in data correction efforts, cost reduction can be seen. In addition to this, Data Governance could increase revenue as more timely data may lead to faster regulatory applications which may result in an earlier registration / launch of products.
  • Data governance solves 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.
  • Data governance provides clarity on responsibilities
    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.

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