Summary: The article discusses the tension between data scientists’ desire for large volumes of data and the need for data privacy and security. It emphasizes the importance of finding a middle ground in data retention and usage, while also highlighting the complexities of managing data in organizations and the impact of data security regulations.
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How Much Data Do We Need? Balancing Machine Learning with Security Considerations
For a data scientist, there’s no such thing as too much data. But when we take a broader look at the organizational context, we have to balance our goals with other considerations.
Data Science vs Security/IT: A Battle for the Ages
Acquiring and keeping data is the focus of a huge amount of our mental energy as data scientists. If you ask a data scientist “Can we solve this problem?” the first question most of us will ask is “Do you have data?” followed by “How much data do you have?” We want to collect data because it is the prerequisite for most of the kinds of work we want to do, in order to produce valuable models and beneficial results.
Institutional Considerations
It’s so easy for a company to become a data hoarder. You begin with a shortage of data, and you’re flying blind, collecting data about transactions, business activities, etc. as you go to help inform decisions and strategy.
An aside about regulation
The growth in data security regulations in recent years has increased the challenges of the scenario I describe for businesses. In some ways, it was our own doing — numerous data breaches, lax security, and opaque consent policies by assorted companies over recent years have led to public demand for better, and government filled the gap.
AI Solutions for Your Company
If you want to evolve your company with AI, stay competitive, and use it to your advantage, consider the following practical solutions:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter.
Spotlight on a Practical AI Solution
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- How Much Data Do We Need? Balancing Machine Learning with Security Considerations
- Towards Data Science – Medium
- Twitter – @itinaicom
