REGIONAL REVIEW
data-driven decisions , unless they operate in a very confined local market and are of limited size . To drive competitiveness , an increasing number of businesses are collecting not only consumer data they can get from their owned channels but also publicly available information from the web for price intelligence , market research , competitor analysis , cybersecurity and other purposes .
Up to a point , businesses might try to get away without using data-backed decisions ; however , when the pace of growth increases , companies that rely on gut feeling only unavoidably start lagging behind . Unfortunately , there are no universal approaches to harnessing data effectively that would suit all companies . Any business has to start from the basics : first , define the business problem ; second , answer , very specifically , what kind of data might help to solve it . Over 75 % of data businesses collect ends up as ‘ dark data ’. Thus , deciding what data you don ’ t need is no less important than deciding what data you need .
In what ways do you envision data visualisation evolving in the context of business intelligence and analytics ?
Most data visualisation solutions today have AI-powered functionalities that provide users with a more dynamic view and enhanced accuracy . Further , AI-driven automation also allows businesses to analyse patterns and generate insights from larger and more complex datasets while freeing analysts from mundane visualisation tasks . I believe data visualisation solutions will have to evolve towards more democratic and noob-friendly alternatives , bringing data insights beyond data teams and into sales , marketing , product and client support departments . It is hard to tell , unfortunately , when we could expect such tools to arrive . Up until now , the focus of the industry hasn ’ t been on finding the single best visualisation solution . There are many different tools available on the market , and they all have their advantages and disadvantages .
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 . 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 .
How can businesses foster a culture of data-driven decision-making throughout their organisations ?
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 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 nontechnical 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 . �
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