Zifo
Data Centrality – a key consideration in Data Strategy
Introduction
Let’s talk about "Data Centrality". At Zifo, we have coined this term to capture the notion that not all data holds equal significance and value in the grand scheme of scientific business processes and strategies. Much like the central pieces in a puzzle, certain data types and domains serve as the backbone of operations and business intelligence in your organisation, driving decisions that govern your business’s growth and performance. The concept of "data centrality" urges businesses to identify, categorise, and prioritise data types and associated datasets to streamline operations and formulate more informed strategies (around data modelling, architecture, governance and so on). Essential you are scoring your data types based on their importance within a given process or workflow
When it comes to both strategic and operational decision-making, an analysis of data centrality could help guide you in navigating complex data landscapes and related investment decisions. In a world that's increasingly data-driven, the ability to discern the varying impacts of different data types can be a game-changer. By focusing on data centrality, businesses can craft strategies that are not only data-driven but also data smart (as against merely data busy), optimizing resources and efforts towards the data that matters the most
A (very straightforward) example is within the realm of pharmaceutical clinical trials. Here, a vast array of data types converge, ranging from clinical trial results to various omics datasets, patient feedback, regulation-relevant information, and more. When implemented, a data centrality model helps in recognizing that some data types that link to clinical trials are of higher value to the strategy than others, as they encompass critical insights into drug efficacy and safety requirements mandated by regulation. Consequently, this data demands a higher level of scrutiny, management and analysis
The challenge for leaders is to cultivate an organisational environment that not only acknowledges the value of data but is adept at distinguishing the centrality of various data domains. Leaders must steer teams towards adopting methodologies that assign value scores to different data sets, facilitating a more structured and impactful approach to data-driven decision-making. This also involves fostering a culture of continuous learning, where teams are equipped with the necessary skills to navigate and leverage the insights drawn from the "central" data domains.
Some types of investments that relate to the centrality of data types and datasets include technological infrastructure enhancements that facilitate smooth integration and analysis, creation of central master data repositories (to note: master data types, by nature, will have a higher centrality score than others), training and development programs to up skill employees in data management and analysis, and consulting partnerships to devise and implement data centrality strategies effectively. These investments must be aligned with the business’s broader goals and strategies, ensuring a cohesive approach to leveraging data centrality for organisational success.
We recommend the following best practices for embedding the concept of data centrality into your science and business strategies:
- Develop a data centrality matrix: A tool that visually represents the value and impact of different data types, assisting in informed decision-making.
- Continuous monitoring and adaptation: Establish mechanisms to constantly review and adjust the data centrality metrics based on evolving innovation and business landscapes.
- Promote a data-centric culture: Encourage a workplace culture where data is regarded as a critical business asset, and employees are trained to work with data-centrality concepts.
- Invest in technology and expertise: Equip your organisation with the necessary tools and expertise to analyse and leverage data effectively and in a prioritised manner.
e.g., here is a summary version of the data centrality matrix illustrated with the example of pharmaceutical R&D. (A more sophisticated, weighted matrix will be available to our customers).
| Data type | Centrality score | Impact on business processes | Strategic importance | Integration density | Impact on systems | Use frequency |
|---|---|---|---|---|---|---|
| Clinical trial data | 10 | Direct impact on drug development processes, safety evaluations | Central to development of effective and safe drugs | High | High | High |
| Patient feedback | 7 | Influences adjustments to drug formulation and delivery | Important for tailoring, repurposing to meet user needs | Medium | Medium | Medium |
| Regulations/ Policies | 8 | Critical for compliance and market approval | Necessary for legal compliance and market access | Medium | Medium | Medium |
| Market research data | 6 | Guides product strategy | Key for understanding therapeutic needs and consumer preferences | Medium | Low | Medium |
| Molecular and genetic data | 9 | Facilitates in-depth research into drug mechanisms and potential therapies | Pivotal in discovery and personalisation of therapies | High | High | High |
| Research supply chain data | 5 | Influences production distribution, inventory management | Regular ingredients availability for Research | Low | Medium | High |
| IP records | 6 | Affects patent filings and protection of research outputs | Vital for protecting research investments and fostering innovation | Medium | Medium | Low |
In the above example, the pecking order of data type importance is roughly this: clinical trial data holds the top position, due to its direct influence on drug development, safety evaluations and in turn, marketability of products. Therefore, critical decisions around resource allocation, research analytics, and technology optimisations should be applied to this data type first because of its higher data centrality compared to other data types like market research or patient feedback. Recognising and acting upon this hierarchy can potentially streamline the development process and enhance the success rate of new pharmaceutical products.
We at Zifo are committed to solving the intricacies involved in data management and strategy formulation in scientific domains. Through our approach of data centrality, we endeavor to assist science-led businesses not only in navigating the data deluge but also transforming data into a strategic asset (valuable, rare, inimitable and non-repudiable). By focusing on the most central data elements that drive business success – essentially a “more bang for the buck” approach, applying typical organisational trade-offs - Zifo can help to transform the way organisations perceive and utilise data, supporting a future of informed, agile, and successful decision-making.
Conclusion
Please reach out to us info@zifornd.com to explore how data centrality can support your science and business strategies.
