But data management is complex. Businesses must start with reliable sources, then collect data efficiently, then analyze it, and draw meaningful conclusions. They must find the right tools to help them get to those conclusions that will be the most impactful. At the same time, companies must take responsibility for the power that data holds. Doing so requires forethought about matters like privacy, governance, and ethics.
Because data management is both so important and complex, it’s critical to plan ahead, either internally or with a trusted partner. Here we present some key areas to consider in looking forward to how you will manage data in 2025.
Privacy
Privacy was already an issue prior phone number list to 2020, but the COVID-19 pandemic forced individuals, companies, and governments to consider it even more. For example, in their effort to curb outbreaks, some companies have been collecting sensitive health data about individuals, such as their symptoms and whether or not they have been vaccinated. What are they doing with this data?
Businesses must consider domestic and international laws (such as the California Consumer Privacy Act or CCPA and the European General Data Protection Regulation or GDPR) when making decisions about data retention and use. A recent National Law Review article states, “Noncompliance with privacy and security requirements can result in harsh monetary and legal penalties, including steep fines and potential civil liability, and can result in a loss of consumer trust potentially impacting a brand into the post-pandemic landscape.”
Ethics
According to a recent Datanami article, “Algorithmic define your mission and differentiator bias is a real threat to the goal of achieving fair and equal treatment at the hands of AI models.” This bias can take the form, for example, of treating people of different races differently in the selection of job candidates.
For companies, this situation requires the adoption of new practices to create “explainable AI” — systems that can explain why a decision based on a specific set of data was made — to maintain the trust of employees, customers, and the public at large.
Data anonymization — the process of removing anything from a data set that links it to the owner of that data — is one part of reducing algorithmic bias, with some countries passing legislation to ensure “responsible AI.” Microsoft offers the following responsible AI principles:
- Fairness – AI systems should treat all people fairly.
- Reliability & Safety – AI systems should perform reliably and safely.
- Privacy & Security – AI systems should be secure and respect privacy.
- Inclusiveness – AI systems should empower everyone and engage all people.
- Transparency – AI systems should be understandable.
- Accountability – People should be accountable for AI systems.
Governance
The Data Governance Institute defines mobile list data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”