The APAC Business Case for Democratizing Analytics Gets Stronger

Modern data management is evolving rapidly, with more businesses looking to move away from traditional, siloed systems. Asia Pacific companies are turning to modern, enterprise-level data architectures such as data lakes, which offer increased agility and scalability to keep up with the competition.

However, these new architectures can be complex and challenging to manage without the right tools. That's where democratizing analytics comes into play; they help organizations unlock the full potential of their digitalization efforts.

Businesses can gain a competitive edge by quickly extracting insights from their data by making analytics more accessible to all users, regardless of their skill level or technical expertise.

In a virtual roundtable discussion organized by CDOTrends, Phil Madgwick, director for ASEAN and Greater China, Alteryx, discussed the company's vision for data modernization in the Asia Pacific and how businesses can overcome the challenges of managing complex data architectures.

Madgwick shared the findings of a commissioned study conducted by IDC on behalf of Alteryx, which found that data analytics is a top priority for many organizations in the Asia Pacific. However, the study, which polled 500 companies across the region, found that only 19% of respondents consider themselves analytics experts. This suggests that there is still a long way to go before data analytics becomes truly embedded in Asia Pacific companies.

Measuring analytics maturity

According to IDC, analytics experts are more likely to exceed their peers by 56% regarding cost reduction, 28% in business model innovation, and 17% in new product development. Companies are therefore under increasing pressure to take data analytics seriously.

IDC and Alteryx adapted a model from “Competing on Analytics” by Davenport and Harris from the International Institute of Analytics to assess where companies in the Asia Pacific stand in analytics maturity. This model comprises four key dimensions – strategy, data, workforce, and process – representing the company's ability to create value from its data.

According to the model, companies that are 'competitors' in analytics have fully embraced data-driven decision-making across all four dimensions.

These companies have committed to using data and analytics to drive their business and have put the necessary infrastructure, tools, and processes in place to make this happen. Furthermore, 50% or more of their knowledge workers are analytically capable.

In contrast, companies that are 'beginners' in analytics have yet to embrace data-driven decision-making fully. These companies tend to rely on traditional methods such as gut feeling or rules of thumb rather than data and analytics to make decisions.

In the region, companies' average score is 2.2, which is at Stage 2 of the model. Stage 2 is where companies implement localized analytics, for example, through the use of reports.

Analytics maturity is an essential consideration for companies in the Asia Pacific, as those that are further along the maturity curve are more likely to capitalize on the opportunities presented by data analytics. However, one panelist also noted that there could be varying levels of analytics maturity within one company, depending on the department or function.

Overcoming barriers to data democratization

For companies to realize the full benefits of data democratization, they need to overcome several barriers, which can vary from country to country. In the Asia Pacific, 99% of companies invest heavily in big data and AI, but only 24% consider themselves data-driven.

Common pain points include that results are too slow and that it takes a long time to see value from data and analytics investments. Furthermore, data science teams are often consumed with low-level tasks, such as data preparation and cleansing, which leaves them little time to focus on more strategic tasks.

Another challenge is the widening gap between people and technology. Data scientists who are comfortable with code and statistics are in high demand, but they are often not available when needed. This highlights the importance of training analysts to be data scientists, as they are often the ones who can fill the gap when data scientists are not available.

The study underscored that it is important not to assume that analysts will be able to use the same tools as data scientists, as they often lack the technical expertise. Instead, businesses should focus on training analysts in the specific skills they need to implement data analytics effectively.

Underpinning all these is the willingness for companies to invest in data analytics and allocate the budget in a way that maximizes the return on investment. This has been a challenge for many companies. Rather than distributing them across the organization, they often channel most of the resources to a few centralized data and analytics teams.

“But if you take that same investment capital, and you spread it in smaller amounts across the majority of your workers and your knowledge workers, that's a quicker return on investment by enabling and empowering your knowledge workers,” Magdwick said.

For this to happen effectively, Madgwick added that companies must understand the data analytics journey of analysts. From data wrangling to data visualization and ultimately deriving meaningful insights and innovations, there are several steps that analysts need to go through before they can be considered data-savvy.

Culture push

Panelists at the roundtable agreed that while comprehensive analytics strategies are in place, their success is often hampered by a lack of data literacy and the right culture.

One panelist said that their biggest challenge is the readiness of people to accept new technology, from the CEO down to the rank and file. Another panelist said that their company has been working to scale its analytics strategy across the enterprise, beginning with Excel training to rekindle the interest of employees in data.

However, one panelist raised the question of silos and why some need to exist within an organization for data analytics to function smoothly. He added that not all data should be democratized, as this could lead to chaos.

Instead, he argued that companies might need to make a business case for data architectures like data lakes, which can help overcome some of the challenges associated with data democratization.

Madgwick agreed and pointed to the crucial role of data governance, which is often overlooked in the data democratization discussion. He said that companies would not be able to make informed decisions about which data should be made available to which employees without governance.

Furthermore, change management strategies are also critical to the success of data democratization. This is because data democratization often requires a change in company culture, which can be challenging. Panelists noted that more than onboarding teams and analysts, ensuring that top management is on board with the data democratization initiative is essential to its success.

While companies in the Asia Pacific are on their way to becoming analytics-driven organizations, they still have some way to go before they can reap the full benefits. To overcome the barriers and achieve success, businesses need to develop a data-literate workforce and create a culture that champions data-driven decision-making.

Image credit: iStockphoto/metamorworks