In an ultra-connected world, data has become one of the world’s most valuable resources. Today, when we talk about AI, ChatGPT or Midjourney, we also talk about data. Indeed, it is data that lies behind these products.
As we all know, data is used to help organizations make decisions. Examples include:
- Improve, customize and adapt products and services
- Identify trends and patterns
- Feeding prediction models, machine learning and artificial intelligence into scientific research
The culture of data
Building a data culture means creating an environment where everyone is involved in data-driven decision-making, innovation and growth. To ensure that the culture is properly implemented, it is important to ensure that the data is accessible to all and that it makes sense for everyone.
There are usually several stages of data maturity: ad hoc, awareness, formal data governance with established policies, established data management, data-driven decision making. Data maturity is not just about technology but how an organization understands the value of data, sets up governance, develops data culture among employees and turns knowledge into actions that drive business success.
Data is also a company asset with real value. Today, everything has a price, especially data. For example, on the internet, everything can be sold as long as there are buyers. This is what can explain the rise of ransomware: get money to pay attackers who develop 0-day exploits to break into businesses and public services to get more money and/ or destabilize society.
Personal data can also be sold. Some attackers are also interested in the data, not for its financial value but for its commercial value. Here we mean intellectual property, trade secrets, strategic information, etc. To protect the value of an organization, it is essential to protect the company’s data in proportion to its value by improving how it is managed to generate value.
Data protection is demanded and expected by customers, regulators and partners alike. No one has unlimited means and resources, it is mandatory to prioritize your data and those you manage and protect them appropriately.
Consider a data minimisation strategy that balances the capability to achieve your business objectives while reducing your exposure to risks and reducing your costs of storing data that are no more relevant to achieve your business objectives.
Enhance your protection capabilities by focusing in your most important assets: enforce the protection of them and around them, enable monitoring capabilities on their use and misuse, control their access and quickly fix their vulnerabilities once identified. The best strategy to secure data is the one that makes the trade-off between collecting all the data to try to take advantage of it and targeting the relevant data to address the exposure risk.
To conclude, the power of data and its importance in the value creation process should never be underestimated. The “engine” of all IT projects should only be data. Many people talk about the benefits of AI for a company, but they seem to forget one detail: the influence of data on the success of AI projects. In this case, the data is even the key element. You have to take care of your data and give it the necessary confidence by controlling its security.