The pandemic has triggered a shift of business models to become more data-driven and to get their data analytics right. According to Gartner, 90% of corporate strategies will indicate information as a critical enterprise asset and analytics as an essential competency by 2022.
Typically, data analytics is the process of exploring, transforming, and analysing data to identify tends and patterns that reveal meaningful insights and build efficiencies that support decision-making.
Jordan Barker, Director, Sales Engineering at Alteryx, APJ reveals that organisations hope to achieve the following by introducing data and analytics into their business strategy and culture:
- Descriptive analytics – “What happened?”
- E.g. What were our sales for the past week?
- Diagnostic analytics – “Why did this happen?”
- E.g. Why did our sales increase from the previous week?
- Predictive analytics – “What will happen?”
- E.g. Will our store sales remain the same during the holiday season?
- Prescriptive analytics – “What should I do?”
- E.g. Based on our predictions, we recommend shipping more of a certain product to prevent a stockout.
“Both descriptive and diagnostic analytics allow data analysts and leaders to level set. These processes are building blocks that pave the way for more sophisticated insights, that result from predictive and prescriptive analytics,” shared Barker.
“We’ve witnessed a slight difference in objectives between industries. For instance, healthcare organisations are looking to create a unified picture of patient and healthcare operations to improve their business, clinical and patient outcomes, whereas financial services enterprises are yearning to manage compliance, mitigate risk, gain deeper customer insight and improve operational efficiency for higher margins.”
“With that said, a human-centric, modern data analytics strategy in any industry can empower systems and organisations to act based on real-time, automated analytics, ensuring impactful, immediate outcomes to drive industry-changing results.”
Challenges of incorporating data analytics
Despite all the potential benefits of data analytics, there are challenges that would prevent organisations from incorporating it into its operations. One of which that Barker identified is the lack of data literacy as an organisational competence.
“While nations and organisations are looking to upskill their workforce to remain competitive, very few employees in the current workforce use data to drive decisions as they doubt their own data literacy skills or fail to recognise data as an asset,” said Barker.
“Only 25% of employees in Singapore believe they are fully prepared to leverage data effectively, while almost five in 10 employees choose to follow their instincts over data-driven insights when making decisions. As they enter the workforce, a mere one in 10 graduates stated that they are prepared to deal with data at a workplace.”
This demonstrates the importance for organisations to provide the right tools and relevant training to its workforce before being able to realise the potential and benefits that data can offer.
Successful examples of data-driven cultures
To illustrate how some organisations overcame the difficulty of introducing a culture of data-driven decisions, Barker shared three cases:
Deutsche Bank Manila
Deutsche Bank Manila was weighed down by an abundance of manual, non-value-adding processes. The Philippines’ regulatory board, Bangko Sentral ng Pilipinas (BSP), had also enforced stricter measures for the banking and finance industry. This risked Deutsche Bank Manila increasing its operational cost, impacting bottom-line revenue, as well as inciting a loss of control and management.
With the need to drive business agility as an organisation, and to adapt and respond to market changes quickly, Deutsche Bank Manila needed to build a culture to inspire innovative thinking, explore new ideas and adopt new technology.
In an effort to become data-driven, Deutsche Bank Manila launched the Alteryx hackathon in March 2021. With 17 pods, 68 participants, and numerous pain points to address during the hackathon, Deutsche Bank Manila was looking to achieve approximately 566 hours a month of workforce efficiency.
Aside from heightened organisational efficiency, the hackathon helped Deutsche Bank Manila to heighten its workforce collaboration, empowered them to make a difference and reinforced their familiarity with the platform.
Prudential Singapore
To meet customers’ evolving needs and champion for an ideal workplace environment, Prudential Singapore started their purpose-driven cultural and digital transformation journey in 2017.
When Prudential Singapore launched an AI-powered digital health and wellness application to provide customers with round-the-clock access to healthcare services and real-time data in April 2020, it had a legacy work culture that was heavily embedded in spreadsheets. This lack of technical skillsets acted as an organisational barrier from delivering quicker insights-driven outcomes.
With the workforce spending 80% of its time preparing the data, little time was left to conduct analysis and drive insight-driven outcomes and recommendations. To help empower them to perform citizen-led analytics, Prudential Singapore first introduced Alteryx to a handful of teams to experiment with automating manual data inputs, preparation, reconciliation and visualisation.
Just from this implementation, they saw a 70-80% improvement in processing time – a task that once took a team member more than 150 hours to complete has been now reduced to 30 minutes. This garnered leaderships’ confidence, excitement and encouragement to adopt Alteryx and integrate the APA platform into more teams across the organisation.
Prudential Singapore has since acquired more licenses and is now better positioned to elevate data assets, analytics, daily business processes and people towards achieving business goals and outcomes.
While nations and organisations are looking to upskill their workforce to remain competitive, very few employees in the current workforce use data to drive decisions as they doubt their own data literacy skills or fail to recognise data as an asset.
- Jordan Barker, Director, Sales Engineering, Alteryx, APJ
NTUC Income
Prior to integrating Alteryx’s solution, NTUC Income‘s actuarial team at Income deals with data extensively on a daily basis. This covers all aspects of data extraction, data preparation, data visualisation, to even data modelling – with the aim of helping the organisation make important decisions such as setting appropriate pricing for products and ensuring adequate reserves for insurance portfolios.
However, they faced two looming challenges. Firstly, the difference in data sources, sizes and formats created many silos of data processes which resulted in data reconciliation issues in analysis and reports.
Secondly, the legacy data processing tools were ineffective in handling huge volumes of data and required analysts to spend significant time manually customising the data to serve insights to multiple stakeholders. There was also a lack of audit trail and documentation logs, making it difficult for a new analyst to trace data errors or make enhancements to the existing data processes.
NTUC Income worked with Alteryx to create the ideal data architecture to help them drive quicker and valuable insights. This helped to eliminate mundane data preparation tasks and transform the role of the actuarial team to better support Income’s overall digital transformation efforts and create better experiences for its customers.
Streamlining access to the data
Often, data silos emerge as a result of one or more data systems being incapable of operating or integrating with other systems or subsystems. Barker explained that although the data can be complementary to each other, different teams in the organisation could be operating separately – resulting in the formation of data silos.
“To ensure that data is accessible to the necessary parties, advanced analytics automation platforms act as a bridge between both teams by aggregating data and connecting disparate databases and data sources,” added Barker.
“Apart from simply breaking down data silos, a human-centric, advanced analytics automation platform ensures that data is accessible to necessary parties by automating an organisation’s analytics and data science processes. This leads to intelligent decision making and empowers the workforce to deliver faster, better business outcomes.
Regarding the maintenance of data security, Barker finds that government institutions in the region have only recently implemented data protection regulation and laws.
“In 2019, Singapore mandated organisations to appoint a Data Protection Officer to ensure compliance with data protection regulations,” remarked Barker. “On a regulatory front, Asian enterprises are held accountable when creating and using customer data at the operational level when improving business efficiency and enhancing customer relationships."
Of limited talent and widening skills gap
For smaller companies, cultivating a data-driven culture and business strategy would prove difficult given the limited pool of data science and analytics talents, and widening skills gap in APJ. However, it is not inconceivable.
“While SMEs’ workforces may lack the seemingly specialised skillsets needed to champion for data and analytics organisation-wide, it is not as daunting as it seems,” said Barker.
“Today’s self-serving analytics and data science solutions have proven to simplify and broaden the accessibility of data, analytics and data science to every organisation and employee.
“The unified, human-centered APA platform automates access to data analytics, data science and process automation in an all-in-one solution, so that any and every SME employee of varying degrees of data literacy can leverage their industry specific skills and data. Thus, smaller companies can now converge all of their greatest assets – data, people and processes – to drive insights-driven outcomes.”
