“Early masters of technology accumulate and wield disproportionate power. Hence, major technological breakthroughs always cause revolutionary economic shifts that disrupt society and politics, in turn altering the global balance of power. We find ourselves in a new digital Gilded Age,” said Singapore’s Foreign Affairs Minister Vivian Balakrishnan at a recent Institute of Policy Studies conference, to which he adds that Singapore has emerged a winner.

Far from being complacent, Finance Minister Heng Swee Keat revealed that this year’s national budget will double-down on Singapore’s transformation, spur innovation for its industries and create good jobs for workers.  Here are the six trends to watch for, as the importance heightens for Singapore’s workforce to quickly acquire capabilities in data skills to spur greater strides in organizational transformation.

Trend one: Greater demand for data ethics

The Monetary Authority of Singapore (MAS) recently released a set of principles to promote fairness, ethics, accountability and transparency (FEAT) in the use of artificial intelligence (AI) and data analytics in finance. The principles are aimed at strengthening internal governance around data management and use. With data regulations like GDPR and consumers’ increased consciousness about sharing personal data, leaders are assessing the future of ethical data practices within their organizations. Having organizational conversations around data ethics and privacy in the context of daily business practices is critical, which will surface through the introduction of codes of ethics and changes in business processes.

Trend two: Natural language processing makes data analytics easier for more workers

Smart technologies powered by AI are lowering the barriers to adoption of analytics by automating stages of the process, making it easier for more people to work with data.

One such technology is natural language processing (NLP), which helps computers understand human language. It is set to make a significant impact, and by 2024 the NLP market is expected to be worth US$845.29 million in the Asia Pacific (APAC) alone. To enable even more people to see and understand data with NLP, for example, Tableau Software has infused NLP in a feature called Ask Data in an all-new 2019.1 product release. This will enable users to simply type a question like “What were my APAC sales last month?” and then ask supplementary questions like “What about in Singapore?”

With new-found capabilities that will enable people to interact conversationally with data, even non-IT trained workers will be able to ask deeper questions, master data literacy and help transform departments and workplaces into data-driven, self-service and smarter operations.

The promise of AI suggests machines will enhance human understanding by automating decision-making. However, with greater reliance on AI and machine learning comes human hesitation about the trustworthiness of model-driven recommendations. This is driving a need for transparency and the growth of explainable AI – the practice of understanding and presenting transparent views into machine learning models. Business leaders are beginning to demand that data science teams use models that are easier to explain and offer documentation or an audit trail around how models are constructed.

Trend three: Data culture embedded in the enterprise

Providing access to analytics is not the same as adoption, so leaders should measure how people are using business intelligence (BI) platforms to make an impact on their enterprises. One way companies are increasing engagement is through internal user communities. For example, at JPMorgan Chase, the center of excellence team helped onboard thousands of analysts to grow the user-base on their BI platform. These users then become experts who help socialize best practices and align others around data definitions. The outcome will be increased impact and return on investment from your BI solution, a more efficient workforce, and a more competitive organization.

To help these workers work with their data how and where they need it, capabilities such as mobile analytics, embedded analytics, dashboard extensions, and application program interfaces (APIs) are being embraced by more enterprises. Embedded analytics puts data and insights where people are already working so they do not need to navigate to another application or shared server, while dashboard extensions bring access to other systems right into the dashboard. Mobile analytics puts data directly into the hands of people in the field. These advancements are empowering new audiences with on-demand data in context.

Trend four: Data storytelling a key aspect of 21st-century literacy

LinkedIn’s recent report lists data scientist as one of the top five emerging jobs across several markets in the Asia Pacific. This is a role that has been evolving as modern BI puts data in the hands of more workers, and today’s data scientists are expected to have advanced technical capabilities along with deep industry and business knowledge. Instead of handing over their results, data scientists now participate in how results are applied to the business.

LinkedIn’s report also noted that the top emerging roles were all tech jobs which require soft skills like design and communication, as well as technical skills. This is especially true in data, because if you can’t communicate data findings, you can’t make an impact with your analysis. This is the power of data visualization. It is now a critical skill for analysts to be able to convey the steps in their analysis that led to insights in an actionable, easy-to-understand way, also defined as “data storytelling.” As companies create a culture of analytics, the definition of data storytelling is changing.

Instead of presenting a singular conclusion, today’s data storytelling methods emphasize nurturing a conversation.

This asks the communicator and the audience to come to a shared conclusion, inviting a diversity of perspectives before making a business decision and amplifying the potential for business impact.

Trend five: Cloud accelerates shift from traditional to modern analytics

Cloud computing in the Asia Pacific is expected to grow at a rate of 28.4% of the compound annual growth rate between 2016 and 2022.

Many companies are now moving data to the cloud because of the added flexibility and scalability at a lower total cost of ownership and easier integration of different data types. With this shift, there is a natural movement of services and applications like analytics moving to the cloud, and it will cause today’s business leaders to assess whether their chosen BI platform will support this, also initiating the move from traditional to modern analytics.

Modern analytics will usher greater diversity and complexity of data from more sources. A culture of analytics will also encourage more of the workforce to harness data to drive decisions, making the management and governance of data more critical than ever. Companies are turning to data curation to bridge the gap between data and its real-world applications. Ultimately, governed data curation will provide a stronger foundation for the entire analytical pipeline, helping users to move beyond asking questions of their data to asking questions of their business.

Trend six: Data-readiness amplifies social good

Finally, 2019 will see the “data for good” movement take flight, as non-governmental organizations and nonprofits realize the benefits of using data in driving their social initiatives. This is having a real impact on organizations and the people they help. For instance, the National Volunteer & Philanthropy Center was able to increase online donations by 56% in just a year by incorporating visual analytics in their donor engagement efforts.

With the cost-efficiency and flexibility of cloud computing, NGOs and nonprofits can now afford to develop sophisticated data environments without massive on-premise investments. This has also resulted in the creation of data commonwealths – platforms for sharing and collaborating across organizations to achieve a goal.

This “data for good” movement reflects the altruistic potential of sharing data to solve our most difficult global problems.

In closing, while Singapore’s investments in advanced infrastructure such as 5G networks will bolster its status as a data hub – through lighting-speed highways for data flows across undersea cables across the globe and via orbiting satellites – what is equally important is the country’s ability to tap into this tsunami of data to fuel innovation with frontier technologies such as pattern recognition, machine learning and automation for the benefit of its enterprises, government agencies, and workforce.

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