Advances in artificial intelligence are making it easier for enterprise software developers to take natural language input — whether spoken or typed — and infer users’ intentions, rather than obliging users to learn specific commands or to manipulate objects on-screen to achieve their goals. AI has been increasingly employed in leading BI tools, in hopes of “democratising” analytics and data science.
Microsoft’s Power BI has included a feature called “Ask a question about your data” for a few years now, but in even recent demos the offering appears more finicky about grammar and spelling than Tableau’s Ask Data. Both are ahead of the likes of Dundas BI, which still uses drag-and-drop to create visualisations.
Tableau’s implementation will allow users to query a database and let the software figure out how database tables need to be joined, which columns should be selected, and what operations must to be performed to obtain the desired answer. It and the other new features will appear in Tableau 2019.1, due for release early next year, and for which the beta version was released this week.
Automation features like these are welcome and necessary, as we are getting more data but the people working with it don’t have more time.
Data scientists spend up to 80 percent of their time on data preparation, and the less time they spend on data preparation, the more they can spend on things that create value.
One way around the time crunch is to hand over workloads to the machines. Another is to make it easier for people that couldn’t previously manipulate the data themselves to do so, the so-called democratisation of data.
The downsides of relying on AI
But there are risks in making data available to more workers. Data is no replacement for domain expertise and context. Before making new automation functions widely available, CIOs should put them through their paces to see whether they’re suitable.
Tools that offer data insights without making clear recommendations may leave users confused about what action to take. “If you don’t give somebody a firm instruction, don’t expect them to get it right every time”.
You can’t just hand over all responsibility to the software, though. Automation is not the same as no supervision. These things still need to be watched.
Ideally, these tools will surface an explanation of what they have done, so as to leave an audit trail.
You also need to figure out whether your data is suitable for the automation tool. Machine learning systems, in particular, need a lot of data to work with. If you are applying machine learning algorithms to data where you have more exceptions than the norm, it’s not going to work.