Covering Disruptive Technology Powering Business in The Digital Age

by Leslie Ong, Country Manager, Southeast Asia, Tableau Software

The disruptive power of data is now an established concept for business across industries, and Gartner predicts that revenues for big data and analytics in Asia Pacific (APAC) will reach $14.7 billion by the end of 2018, an increase of 14.4% over 2017[1].

As data analytics continues to gain momentum in the region, Tableau has identified nine Business Intelligence (BI) trends to watch out for in 2019.  These trends will be shared over the coming weeks, starting with two significant trends around Artificial Intelligence (AI) – a technology which is becoming hard to ignore, with the potential to disrupt many industries.

 Smart analytics lowers the barriers to entry for analytics adoption

Smart technologies powered by AI are lowering the barriers to analytics adoption by automating stages of the process, making it easier for more people to work with data. An example is natural language processing (NLP), a technology which brings together computer science and linguistics to help computers understand meaning behind human language. BI vendors are starting to offer a natural language interface so users can interact with their data naturally, asking questions as they think of them without deep knowledge of the BI tool.

Within the context of modern BI, natural language is being applied to support the analytical conversation. Analytical conversation is defined as a human having a conversation with the system about their data. The system leverages context within the conversation to understand the user’s intent behind a query and further the dialogue, creating a more natural conversational experience. For example, when a person has a follow-up question of their data, they don’t have to rephrase the question to dig deeper or clarify an ambiguity. The user can simply ask a BI tool to “Find out sales in APAC” and then ask a follow-up question “How about in Singapore?”.

Natural language represents a paradigm shift in how people ask questions of their data. When people can interact with data naturally, it opens up areas of the analytics pipeline that were traditionally reserved for data scientists and advanced analysts. Users are not limited by their analytical skillset—only by their own breadth of questions. It also allows advanced users to answer richer questions in less time and to provide more engaging dashboard capabilities to others. As natural language matures across the BI industry, it will break down barriers to analytics adoption across organisations and further embed data into the core of workplace culture.

The rise of explainable AI

 Organisations are becoming increasingly reliant on machine learning models to power countless business processes, but how can humans be sure these recommendations are trustworthy?

Currently, many of these applications don’t show the algorithms or logic behind decisions and recommendations, so organisations piloting AI programs are rightfully concerned about the technology. The need for transparency has led to the growth of explainable AI – the practice of understanding and presenting transparent views behind a suggestion. Decision-makers expect to be able to ask follow-up questions around why a model says something, how reliable it is, and what it would say if inputs were different—very similar to how a leader would query a human expert when making critical decisions.

Line of business leaders are demanding data science teams to use models that offer documentation or an audit trail around how models are constructed. In efforts to help humans better understand and trust AI, and as more companies embrace the value of AI and machine learning, more needs to be done to justify conclusions in an intelligible and simple fashion

[1] Gartner, Big Data and Business Analytics Revenues in the Asia/Pacific (excluding Japan) Will Reach $14.7 Billion in 2018, Led by Banking and Telecommunication Investments: IDC, 10 Apr 2018

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