t cht lk
t cht lk
However , there are basic principles that apply to any company . First , companies must thoroughly evaluate the nature of the data they are planning to fetch . Second , more data doesn ’ t equal better data – deciding which data brings added value for the business and omitting data that is excessive or unnecessary is the first step towards better compliance and fewer data management risks .
Rytis Ulys , Analytics Team Lead at Oxylabs
How can businesses foster a culture of data-driven decision-making throughout their organisations ?
Could you discuss the importance of data privacy and security in the era of advanced analytics , and how businesses can ensure compliance while leveraging data effectively ?
Data privacy and security were no less important before the era of advanced analytics . However , the increased scale and complexity of data collection and processing activities also increased the risks related to data mismanagement and sensitive data leaks . Today , the importance of proper data governance cannot be understated : mistakes can lead to financial penalties , legal liability , reputational damage and
consumer distrust .
In some cases , companies deliberately ‘ cut corners ’ in order to cut costs or gain other business benefits , resulting in data mismanagement . In many cases , however , improper data conduct is unintentional .
Let ’ s take an example of Gen AI developers who need massive amounts of multifaceted data to train and test ML models . When collecting data at such a scale , it is easy for a company to miss that parts of these datasets contain personal data or copyrighted material that the company wasn ’ t authorised to collect and process . Even worse , getting consent from thousands of Internet users who might be technically regarded as ‘ copyright ’ owners is virtually impossible .
So , how can businesses ensure compliance ? Again , it depends on the context , such as the company ’ s country of origin . US , UK and EU data regimes are quite different , with the EU having the most stringent one . The newly released EU AI Act will definitely have an additional effect on data governance as it tackles both developers and deployers of AI systems within the EU . Although generative models fall in the low-risk zone , in certain cases , they might still be subject to transparency requirements , obliging developers to reveal the sources of data the AI systems have been trained on as well as data management procedures .
The first step is , of course , laying down the data foundation – building the Customer Data Platform ( CDP ), which integrates structured and cleaned data from various sources the company uses . To be successful , such a platform must include no-code access to data for non-technical stakeholders , and this isn ’ t an easy task to achieve .
No-code access means that the chosen platform ( or ‘ solution ’) must hold both an SQL interface for experienced data users and some sort of ‘ drag
Deciding what data you don ’ t need is no less important than deciding what data you need .
and drop ’ function for beginners . At Oxylabs , we chose Apache Superset to advance our self-service analytics . However , there is no solution that would fit any company and would only have pros and no cons . Moreover , these solutions require well-documented data modeling .
When you have the necessary applications in place , the second big challenge is building data literacy and confidence of non-technical users . It requires proper training to ensure that employees handle data , interpret it and draw insights correctly . Why is this a challenge ? Because it is a slow process , and it will take time away from the data teams .
Fostering a data-driven culture isn ’ t a one-off project – to turn data into action , you will need a culture shift inside the organisation , as well as constant monitoring and refinement efforts to ensure that non-technical employees feel confident about deploying data in everyday decisions . Management support and well-established co-operation between teams are key to making self-service analytics ( or data democratisation , as it is often called ) work for your company . p
www . intelligentcio . com INTELLIGENTCIO EUROPE 71