Modern data management is changing rapidly, with more and more companies looking to move away from traditional siled systems. Asia-Pacific enterprises are turning to modern enterprise-level data architectures, such as Data Lakes, which provide increased agility and scalability to compete.

However, these new architectures can be complex and difficult to manage without the right tools. This is where the democratization of analytics comes into play; they help organizations unlock the full potential of their digitization efforts.

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

During a virtual roundtable hosted by CDOTrends, Phil Madgwick, Director for ASEAN and Greater China, Alteryxpresented the company’s vision for data modernization in the Asia-Pacific region and how enterprises can overcome the challenges of managing complex data architectures.

Madgwick shared the findings of a study commissioned by IDC on behalf of Alteryx, which found that data analytics is a top priority for many organizations in Asia Pacific. However, the study, which surveyed 500 businesses in 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 is truly mainstreamed into Asia-Pacific businesses.

Measure analytical maturity

According to IDC, analytics experts are 56% more likely to outperform their peers in 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 ofCompetition on Analytics” by Davenport and Harris of the International Institute of Analysis to assess where companies in the Asia-Pacific region stand on analytics maturity. This model includes four key dimensions – strategy, data, people 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 in all four dimensions.

These companies have committed to using data and analytics to drive their business and have put in place the infrastructure, tools and processes to do so. Additionally, 50% or more of their knowledge workers are analytically capable.

In contrast, companies that are “newbies” to analytics have yet to fully embrace data-driven decision-making. These companies tend to rely on traditional methods such as intuition or rules of thumb rather than data and analysis to make decisions.

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

Analytics maturity is a key consideration for companies in the Asia Pacific region, as those further along the maturity curve are more likely to take advantage of the opportunities presented by data analytics. However, one panelist also noted that there can be different levels of analytics maturity within a company, depending on the department or function.

Overcoming Obstacles to Data Democratization

For companies to take full advantage of the democratization of data, they must overcome several obstacles, which can vary from country to country. In Asia-Pacific, 99% of companies are investing heavily in big data and AI, but only 24% consider themselves data-driven.

Common issues include that results are too slow and it takes a long time to see the value of investments in data and analytics. Additionally, data science teams are often absorbed in low-level tasks, such as data preparation and cleaning, leaving them little time to focus on more strategic tasks.

Another challenge is the growing 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 underscores the importance of training analysts to become data scientists, as they are often the ones who can fill the void when data scientists are not available.

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

All of this hinges on companies’ willingness to invest in data analytics and allocate budget in a way that maximizes return on investment. This has been a challenge for many companies. Rather than spreading them across the organization, they often funnel most resources to a few centralized data and analytics teams.

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

For this to happen effectively, Madgwick added, companies need to understand the data analytics journey of analysts. From manipulating data to visualizing data and ultimately gaining meaningful insights and innovations, analysts must go through several stages before they can be considered data savvy.

Cultural push

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

A panelist said their biggest challenge was whether people were ready to accept new technologies, from the CEO down. Another panelist said his company is working to expand its analytics strategy across the business, starting with Excel training to rekindle employees’ interest in data.

However, one panelist raised the issue of silos and why some must exist within an organization for data analytics to work well. 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 democratizing data.

Madgwick agreed and highlighted the crucial role of data governance, which is often overlooked in the debate on data democratization. He said companies would not be able to make informed decisions about what data to make available to which employees without governance.

Additionally, change management strategies are also critical to the success of data democratization. Indeed, the democratization of data often requires a change in corporate culture, which can be difficult. Panelists noted that beyond onboarding teams and analysts, ensuring senior management is on board with the data democratization initiative is critical to its success.

While businesses in the Asia-Pacific region are on their way to becoming analytics-driven organizations, they still have a long way to go before they can reap the full benefits. To overcome obstacles and succeed, companies must develop a data-savvy workforce and create a culture that champions data-driven decision-making.

Learn more about creating the right data culture and lowering expectations at our next virtual roundtable, hosted by CDOTrends and Alteryx on May 25, 2022. To register, please click here.

Image credit: iStockphoto/metamorworks


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