By Geoff Thomas, Qlik Blog
We are now living in a truly hyper-connected environment where vast amounts of data are being transferred, gathered, and consumed daily. There are 3.9 billion internet users globally – a number that is still growing. Just think about the last time you were waiting for a Zoom conference call to start. Perhaps while waiting, you replied a few emails, scrolled through LinkedIn, ordered some groceries, or responded to a couple of WhatsApp messages – in just a few short minutes, you probably generated and consumed large quantities of data.
Similarly, at the workplace, we’re more familiar than ever on the variety of platforms to communicate and collaborate. While these platforms promise to increase efficiency and collaboration, the amount of information and data they make available to employees can be overwhelming.
Augmented Intelligence: Turning data into advantage with human-AI collaboration
Making sense of data and turning it into insights will be crucial in 2020. We are entering a time of uncertain economic growth due to the ongoing trade war, social instability and the COVID-19 outbreak. Even before COVID-19, the International Monetary Fund predicted that even fast-growing regions like Asia will only expand at five percent in 2020, the slowest growth we have seen in a decade. The ability to analyze data and turn it into intelligence will differentiate organizations that succeed in these challenging conditions and those that get left behind.
Thankfully, advancements in technology allow businesses to tackle this issue. Enter Artificial Intelligence (AI). Far from being a science fiction dream, AI is already moving into the forefront of many business strategies. AI adoption has increased by 25 percent year-over-year in the use of business processes, a big increase. In Asia Pacific, the number of companies that have embedded AI into their business has nearly doubled. Many executives have also reported that AI has helped improve revenues in business areas where it is applied, and 44 percent said that the technology has slashed expenditures.
However, only a small share of companies – from a variety of sectors – are attaining disproportionately high business results from AI, suggesting a widening gap between AI power users and organizations that fall behind. Meanwhile, a third of organizations say that they expect increasing AI adoption to negatively impact their workforce in the coming years. As business issues grow more complex, solutions will require the best of both worlds: the intelligence and automation of machines and human interactivity and capabilities.
Hence, I believe that Augmented Intelligence will become the new approach to AI, which will play an increasingly significant role in the future of work.
In Augmented Intelligence, AI is used to enhance human intelligence rather than replace it. Humans will be partnered with AI and advanced analytics, as they will have the ability to spot misleading data points or incorrect conclusions. Even when the data-informed decisions made by machines are correct, we still need knowledgeable people on hand to use their imagination and intuition to automate actions or develop new questions for data to answer. In this way, data analytics will be used to empower people, helping them to become faster and smarter at the tasks they are performing.
Successful human-AI collaboration starts with data literacy
For human-AI collaboration to be successful, we must go back to the basics of ensuring that the existing workforce and future generations are equipped with the right skillsets to read, work with, analyze, and argue with data. However, many teams are still working in silos, and managers may still perceive analytics as being useful only for reporting and data visualization, diminishing the true potential it can have on business decision-making.
Our first-of-its-kind Data Literacy Index uncovered that data literate enterprises are valued up to US$500 million more than their counterparts. There is a clear case for business leaders to defend their market share and commit resources to ensuring that employees are well-equipped to operate in the new data-driven environment. However, many still lack a clear directive and strategy to effectively leverage all that data at their fingertips. Fortunately, there are a handful of approaches organizations can take to start harnessing the potential of the data they are sitting on.
Four ways to drive true data literacy
1. Empower employees with data analytics
Empowering employees with data analytics leads people to take ownership, gets them familiar with the data, and provides them with the right understanding to ask the right questions to be able to extract insights. The benefits of a data-driven workforce are immense. Singapore organizations with employees that know how to use data gain US$3.7 billion in value in terms of productivity. In Australia, the number rises to a staggering US$9.4 billion.
Governments and organizations can start fostering a data literate culture by providing employees with access to relevant data for their specific roles, as well as the tools and encouragement to turn it into insights. Many are beginning to realize that they do not necessarily need professionally-trained data scientists, but people who have exposure to data-informed operations in their working lives. Sixty-three percent of global organizations are actively looking for candidates who can demonstrate their data literacy – with a preference of actual working experience as opposed to just data science training.
Organizations can also choose to embark on a more ambitious route: democratizing data. This means making data accessible, in a governed way, to every employee, regardless of their level in the organization. This will greatly benefit a company’s use of data and, by definition, their approach to newer technologies like AI and machine learning. However, few companies give everyone in their organization access to analytics tools that are appropriate to their level (18 percent) or designed for their job role (18 percent) – more needs to be done here.
2. Train up data detectives
Businesses and employees have typically been focused on reporting historical data instead of uncovering insights which can influence how to operate in the future. Today, that approach is no longer sufficient when it comes to data analytics. There needs to be a shift from the how and what of reporting to the why. Asking questions that dig beyond a KPI or metric will empower and enable businesses to scratch beyond the surface of data. In other words, it is time for teams and departments to become data detectives.
A good example of this is our customer SafeWork NSW. At the start of their analytics journey, SafeWork NSW found that they had over 700 reports, most of which were produced manually, incurring large labor costs and providing limited insights. They were able to reduce this to a suite of nine analytical apps, balancing a scorecard against critical organizational performance areas in less than a year.
On an individual level, people can take simple steps such as asking more questions and interrogating the given facts and information. This means organizations need to encourage and motivate employees to understand the company’s corporate vision and strategy, help them understand the context of the situation that they are trying to analyze and make them feel comfortable with the data that they will be using.
3. Embed curiosity into your culture
The new analytics economy and the world of data promises a lot of answers and insights. In order to utilize this in a timely way, and to be truly be data-informed, we need to ensure our organizations are prepared to ask questions. Hence, it is important to build up curiosity as a value in your business culture.
One simple way is by asking “why.” Doing so will help formulate more questions to uncover truly valuable insights. Akin to peeling back the layers of an onion, this constant quest for data-informed insights will ultimately become a habit that leads to data-informed decisions.
4. Strengthen AI adoption and capabilities
On top of people, organizations and businesses must continue to strengthen the foundation of AI technology. AI should remain at the forefront of their plans to further develop and refine current and future machine learning applications. Some governments around the world have planned national AI strategies to transform their economies and improve lives. Likewise, organizations should also have a systematic long-term strategy to roll out these AI capabilities to further impact their operations and business.
As we look towards AI to harness the true value of data, we must be mindful of the value of human experience and intuition. People still play a key role in providing different perspectives, based on their knowledge of the business and subject matter. Organizations can only find true value if they combine human-centric analysis and exploration with AI and machine learning.
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