While there are examples of data used to drive positive impact, many companies are not using data to its full potential yet.
“Many firms are sitting on mountains of valuable data that could give them an edge over competitors, however, they are facing a lack of talent to help harness this potential,” according to Leslie Ong, Country Manager, Southeast Asia, Tableau Software, in an interview with Networks Asia. “Hence, companies need to adopt a mindset shift, emphasizing a data-driven approach by leveraging the right tools to upskill workers as the region embraces a digital world.”
In the interview with NWA, Ong also discussed about data is driving the digital economy and what C-levels should pay attention to succeed as digital enterprises.
What are some examples of how data is driving the digital economy?
Asia Pacific is entering a digital future, where businesses of all sizes are expected to adopt more technologies in their everyday work. Of the emerging technologies, data analytics is the most prominent as workplaces of the future are leveraging data for decision-making. In fact, a study by PwC found that 83% of respondents expect data and analytics will become increasingly important in the decision-making process over the next five years.
More companies now realise the value of data and are looking to harness its potential. For instance, GOJEK was able to achieve robust growth by tapping on powerful insights from data. Instead of relying on gut instincts, they turned to data for decision-making. For instance, the company tracks driver location data to monitor and improve the effectiveness of their allocation system. By mapping the geo-locations of bookings, they can identify problem areas with low conversion or allocation rates. This data-driven business strategy has helped GOJEK achieve competitive advantage and improve efficiency.
There is also huge potential for data to be used for social good. For instance, Singapore’s National Volunteers and Philanthropy Centre (NVPC) embraced analytics to make their fund-raising efforts more targeting, which resulted in a 56% increase in donations through its giving.sg website within a year.
While there are examples of data used to drive positive impact, many companies are not using data to its full potential yet. Many firms are sitting on mountains of valuable data that could give them an edge over competitors, however, they are facing a lack of talent to help harness this potential. Hence, companies need to adopt a mindset shift, emphasizing a data-driven approach by leveraging the right tools to upskill workers as the region embraces a digital world.
What should C-levels pay attention to, for futureproofing their organizations to succeed as digital enterprises?
As datasets continue to grow larger and faster than before, the crucial element is for business leaders to now start empowering more of their people to make decisions based on data to create all kinds of benefits. With the pace of change having increased significantly and not looking to ease up, businesses users (who create stronger business impact) need to have data skills as companies look to cut down the time to transform data to insights.
Traditionally, data belonged to the specialists in the IT or business intelligence (BI) departments. However, there is often a significant lag between the questions asked and the answers provided, resulting in insights being outdated by the time they reach the business users. Businesses hence need to move towards a modern, self-service tool. With an easy-to-use analytics platform, workers no longer need to be experts to interrogate data, taking the burden off the IT department. These tools make it easier for companies to put data in the hands of even more people across the business.
For example, Lenovo created flexible dashboards that departments could adapt for quick analyses, leading to a 95% improvement in efficiency across 28 countries.The valuable time saved is then used for delivering insights back into the business, helping to make decisions that ultimately benefited the company.
However, as organisations continue to scale and put data in the hands of more people, they need to ensure people are using data responsibly. To this end, strong governance is essential, which means having policies in place to ensure data is used in the right way and that people are properly trained in how to do so.
What is needed to properly automate data analytics? How ready are businesses here to do that? How is Tableau implementing AI in a way that will make data analytics accessible to all kinds of business in Asia by removing the need for complex data skills? Are there still problems getting the right data to the right people? Why?
More organisations are looking to stay ahead of competition by harnessing the power of data. Gartner research found that APAC CIOs will invest new or additional funding in 2019, with BI and data analytics taking the lead in the top five areas of IT spending.
As data analytics take centre stage in the organisation’s agenda, new technologies are being integrated with modern BI tools to help make analytics more accessible. With developments in Natural Language Processing (NLP) powered by artificial intelligence (AI), Tableau has recently launched Ask Data, which uses NLP to help computers understand the meaning of human language. This introduces an entirely new way to interact with data by allowing people to ask questions in plain language. Now people with no specialist data skills – just curiosity – can find useful insights hidden in data. In addition, there is no need to have a deep understanding of the data structure, and no setup and programming skills are required.
For data specialists, AI will help automate repetitive and mundane tasks like data preparation, meaning they can spend more time on complex activities like data modelling. The new NLP system understands the user’s intent behind a query, creating a more natural, conversational experience. People can simply type a question (like “What were my sales last month?”) and Tableau will return an interactive visualization. Ask Data’s sophisticated parser automatically cuts through ambiguous language, making it easy for people to ask questions in a natural, colloquial way. This means, if a question could be interpreted multiple ways, Ask Data will combine knowledge about the data source with past user activity and present a number of valid options to choose from, with the ability to refine the results if needed.
Are they looking to AI to solve problems that ML can’t do? Do customers understand the difference between AI and ML and where each fits?
There is some crossover between ML and AI, though they remain predominantly different beasts. ML involves algorithms that are trained on past data to produce future output such as predictions. For example, you can introduce photographs of donuts to a machine so it could determine whether or not a new photograph contains a donut. On the other hand, AI is about mimicking human understanding and behaviour. Adding on to the example shared earlier, AI refers to the machine's successful ability to recognize bagels from donuts.
While it’s AI that is capturing most of the attention, ML has the most practical application within most businesses. ML brings along significant advantages for both developers and the organisations putting it to work. When it comes to ML, work that has already been done in one area can be readily transferred to others. This gives developers a head start and makes the creation of new tools and processes much faster and more cost-effective. ML algorithms are also able to learn automatically. Progress here is particularly evident in the area of image recognition. For example, ML software can be shown large volumes of images of cats and can learn to recognise cats in images that haven’t been previously seen.
Ultimately, priority will remain to connect human intuition and curiosity with the power of the machine by infusing smart, capabilities like NLP that make analytics easier and more accessible to everyone.
How automated can data analytics become? Can we use ML or tools like RPA and RDA instead of AI?
Machine learning can bring automation of advanced statistical analyses and apply models with the highest confidence, allowing less advanced users to take advantage of complex models. This also benefits advanced users as they can use the time to explore and modify calculations, not only addressing trust and transparency in the process, but also testing the different what-if scenarios.
As technology evolves, other trends will continue to change the way people experience data and how companies operate. For example, Robotic Process Automation (RPA) is a software solution that can help automate repetitive human processes running on a computer. Robotic Desktop Automation (RDA), on the other hand, is a form of RPA and this can assist agents to handle routine, mechanical tasks that are part of their everyday work. The digitisation of repetitive processes is essential, helping to create quality and structured data in a timely manner. Poor quality data is often the result of human error. When less time is spent fixing and organizing data, businesses can then devote the time to doing real analysis, generating powerful insights for decisions.
In that regard, BI players will continue to look to integrate new forms of innovation, to help more people without traditional data skills to work with data in new and exciting ways to get new insights.
What are the barriers to widespread adoption of AI? How will spending fit into IT budgets?
Demand for AI is growing at a massive pace. In APAC, spending on cognitive and AI will have a five-year compound annual growth rate of 69.8% to US$5.0 billion in 2021. The banking industry leads this growth, with applications of AI in cases such as fraud analysis and investigation, IT automation, automated customer service agents and program advisors and recommendation systems. Retail and healthcare providers follow closely in terms of AI investments in APAC.
With this greater reliance on AI, there comes human hesitation about the trustworthiness of model-driven recommendations. This has driven the need for transparency and the growth of explainable AI – the practice of understanding and presenting transparent views into machine learning models.
More business leaders are beginning to demand that data science teams use models that are able to offer documentation or an audit trail around how models are constructed. By 2022, IDC anticipates that algorithm opacity, decision bias, malicious use of AI, and data regulations will result in doubling of spending on relevant governance and compliance staff and explainability teams in APEJ3.
What are the skills needed in this new data driven economy? What is lacking from IT staff these days? Do data analysts need IT skills?
Employability in the future will be dependent on candidates having a range of different skills associated with data. In the future digital economy, everyone will work with data in different ways: some will create data while others will only consume it. Hence, the skill requirements will differ from a technical perspective for those creating it versus those only consuming it.
A recent trend that is on the rise is the requirement of soft skills for tech jobs. A LinkedIn report on emerging jobs highlighted how there is an unexpected mix of technical skills and soft skills like management and communication. Similarly, a McKinsey report flagged the importance of soft skills like creativity, critical thinking and decision making in the age of automation and AI. While technical skills like data analytics are rising in importance, soft skills need to go hand in hand as they will help organisations to unlock and communicate the real value of data. As the market values a hybrid set of skillsets, what remains consistent is the need to apply critical thinking to data, asking questions of data and storytelling with it.