4. The transition to the cloud accelerates
With organisations moving their data to the cloud (as cloud solutions become more secure, reliable and easier to use), analytics' move to the cloud has reached a tipping point. In 2017, the concept of data gravity will take hold as more businesses realise the value of deploying their analytics where their data lives. Cloud data warehouses like Amazon Redshift will continue to pull data, and cloud analytics will become more prevalent as a result. While many organisations will continue to deploy a hybrid architecture of cloud and on-premise solutions, cloud analytics will increasingly represent a faster and more scalable solution.
5. Business analytics gets advanced
Business users have grown more data-savvy. Advanced analytics has grown more approachable. In 2016, a partner at McKinsey & Company commented that "we have to stop thinking of advanced analytics as some form of magic," and that not just data scientists, but business users themselves need to be able to extract value from data to make better business decisions.
In 2017, these two will converge as advanced analytics becomes the standard for the business user. Advanced analytics will no longer be reserved for data scientists and experts. Business users are already leveraging powerful analytics functions like k-means clustering and forecasting. And in 2017, they'll continue to expand their analytics skill set.
6. Data literacy becomes a fundamental skill of the future
In 2016, LinkedIn listed business intelligence as one of the hottest skills to get you hired. Earlier this year, IDC Asia Pacific noted that the lack of big data-related talent would remain as one of the biggest obstacles for many Asia Pacific organisations, while professional services in that area will have a 29% CAGR in the region by 2020.
In 2017, data analytics will become a mandatory core competency for professionals of all types. Much like proficiency in Microsoft Word, Excel, and PowerPoint, basic proficiency in analytics will become a staple in the workplace. To meet this need, we'll see analytics and data programmes permeate higher education. In the workforce, people will leverage intuitive BI platforms, and data will play a role in every major decision.
Top tier educational institutions in the region are doing what they can to keep up. For example, the National University of Singapore (NUS) recently announced a new degree programme in Data Science and Analytics for the new academic year (of 2017), and many other schools have already done or are expected to do the same.
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