5 things that will be important in data science in 2018
What’s data science going to look like in 2018? How are job roles in the field going to change? Will AI find new ways to capture the public imagination? Learn more from Packt $5 books - on sale till Jan 16.
What’s data science going to look like in 2018? How are job roles in the field going to change? Will AI find new ways to capture the public imagination? There are certainly plenty of questions worth asking when it comes to data science and machine learning, especially after a year of change and big stories bringing artificial intelligence and deep learning into the public consciousness in a way it never has before.
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Without further ado, here are 5 things that will be essential talking points and issues in data science in 2018. Data scientists, pay attention!
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There has been a lot of debate over the last few years about the ethical implications of many different aspects of data science and artificial intelligence. From discussions around privacy to automation, all the way to the potential personhood of artificial intelligence, there are any issues that have moved beyond the professional and academic domains into wider public discourse.
But that doesn’t mean data professionals shouldn’t take an interest and put forward their own point of view. Far from it; in fact, it’s essential that data scientists, analysts, and engineers all contribute to the ongoing discussions about the field. You are the people developing that field, making decisions every day that will shape the way it is used and viewed by the wider world - so make sure you play your part!
For too long businesses haven’t been getting the most out of their data. And that’s infuriating for data scientists and analysts - it means they’re not being allowed to do their job properly because of mismanagement and a lack of strategic direction. But now is the time for change. It’s time for organizations to start being more intelligent with their data, to ensure there is greater alignment between business goals and needs and the way data is handled, processed, visualized and shared.
3. Data empowerment
Data empowerment sounds big and bold, but it comes down to one thing - better BI tools that more people can use. Data doesn’t, after all, belong in one corner of a room - it belongs everywhere, to all stakeholders. Yes, their needs will be different, but they all need different types of data, processed and analyzed in different ways. Ultimately data empowerment feeds into the previous point about alignment - it helps to ensure everyone is working together towards clearly defined goals, with the same tools and resources at their disposal. Accessibility will be everything in 2018.
Automation means many different things in the data world. And all of them are important. But most interestingly is how data science itself might start to be automated. Thanks to tools like auto-sklearn it’s something we’ve started to see already, and its likely to change the nature of data science roles. As algorithm selection and optimization becomes less intensive and time-consuming, focus will move elsewhere to the next thing that data professionals can add value to.
Okay, so cloud has been part of discussions around software and infrastructure for the good part of a decade. But it’s only very recently that it’s starting to have an impact on the data world. As cloud solutions become more popular, it’s going to open up new options for a diverse range of organizations, providing new ways of storing, capturing and processing data. No doubt we’ll see cloud offerings develop in a way that accommodates the data needs of their customers, but more than that, we’ll also hear more from data scientists and data architects about how cloud is revolutionizing the way they build intelligent systems.