There is a delicate balance between the needs of data scientists and the requirements of data security and privacy.
Data scientists often need large volumes of data to build robust models and derive valuable insights. However, the accumulation of data increases the risk of data breaches, which is a concern for security teams.
This hunger for data and the need for suitable control over sensitive data creates a tension between the data scientists seeking more data and the security teams implementing measures to protect data from inappropriate use and abuse.
A strategic approach to data management is needed, one that satisfies the need for data-driven insights while also mitigating security risks.
There needs to be an emphasis on understanding the depth of the data, rather than just hoarding it indiscriminately.
Towards Data Science article Author, Stephanie Kirmer reflects on her experience as a senior machine learning engineer and discusses the challenges organizations face as they transition from data scarcity to data abundance.
Kirmer highlights the importance of making decisions about data retention and striking a balance between accumulating enough data for effective machine learning and avoiding the pitfalls of data hoarding.
Kirmer also touches on the impact of data security regulations, which add a layer of complexity to the issue. Despite the challenges, Kirmer advocates for a nuanced approach that balances the interests of consumers, security professionals, and data scientists.
Kirmer also stresses the importance of establishing principles for data retention and usage to guide organizations through the decisions surrounding data storage.
Paul Gillin, Technology Journalist at Computerworld raised this topic back in 2021. in his piece Data hoarding: The consequences go far beyond compliance risk, Gillin discusses the implications of data hoarding, which extends beyond just compliance risks. It highlights how the decline in storage costs has led to a tendency to retain information rather than discard it.
Pijus Jauniškis a writer in Internet Security at Surfshark describes how the practice can lead to significant risks, especially with regulations like the General Data Protection Act in Europe and similar legislation in other parts of the world.
There is however a landscape where data is both a valuable asset and a potential liability, a balanced and strategic approach to data management is crucial to ensure that the needs of both groups are met.
The data community has a significant responsibility in recognizing both.
Data management responsibilities extend beyond the individual who created or collected the data. Various parties are involved in the research process and play a role in ensuring quality data stewardship.
To generate valuable data insights, people need to become fluent in data. Data communities can help individuals immerse themselves in the language of data, encouraging data literacy.
A governing body organizationally, is often responsible for the strategic guidance of a data governance program, prioritization for the data governance projects and initiatives, approval of organization-wide data policies and standards and if there isn’t one, one should be established.
Accountability includes the responsible handling of classified and controlled information, upholding data use agreements made with data providers, minimizing data collection, informing individuals and organizations of the potential uses of their data.
In the world of data management, there is a collective duty to prioritize and respond to the ethical, legal, social, and privacy-related challenges that come from using data in new and different ways in advocacy and social change.
A balanced and strategic approach to data management is crucial to ensure that the needs of all stakeholders are met. We collectively need to find the right balance between leveraging data for insights and innovation, while also respecting privacy, security, and ethical considerations.
