More and more companies collect huge amounts of data and want to turn them into money . But where are the obstacles to achieving that ?
When it comes to data monetization , most companies only scratch the surface . This is because the use of data can only be imposed to a limited extent . Instead , companies need to get as many people as possible excited about using data . Analytical competence is often confined to the IT or BI department , employees are not trained in working with data or do not have the right tools to use data easily and sensibly in their daily work . The fact is , the more employees and managers are used to working with data , making decisions based on facts and playfully drawing new insights from data analysis , the more likely it is that companies will develop ideas on how to make money with data - be it in the form of services or even entirely new business models .
In other words , it is necessary to build a data-driven corporate culture . But what does the path to this end look like ?
A culture of analysis begins with empowerment . Employees must be empowered to explore data themselves and answer their own questions . This also includes a certain degree of trust managers must have in their teams when dealing with data . Modern BI tools also help to curate and purposefully manage data . This way , everyone gets access to the data they need without jeopardizing sensitive data and governance regulations .
What role do technologies like AI play here ?
If companies want more employees to work with data , they need to make access as easy as possible . Technologies such as Natural Language Processing ( NLP ) and AI can help . NLP - the ability of computers to understand human language - lowers the entry barrier for professional analytics . With our Ask Data feature , users can formulate questions about
Henrik Jorgensen Country Manager DACH at Tableau Software .
their data in natural language . When people can interact with a data visualization like a personal assistant , it allows more people across all disciplines to ask deeper questions about their data , thus increasing the overall acceptance of data analysis . With the use of AI-based data analysis , we are now going one step further and making statistical knowledge more accessible . For example , the new Explain Data feature can be used to explain statistical outliers . This allows users without specialist knowledge to quickly expose the ' why ' behind their data by simply clicking on the data point in a visualization . Explain Data evaluates hundreds of patterns and explanations within seconds , taking all available data into account . Innovations such as these foster a culture of curiosity and strongly promote a data-driven corporate culture .
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