Majority of leaders in IT, digital transformation and line of business (LOB) recognise the importance of data analytics to stay performant.

However, only one in five organisations in Singapore are assessed to be “analytics experts”, which have a data analytics strategy aligned with enterprise-wide digital transformation.

Those "analytics experts" are more likely to exceed their peers in productivity enhancement (70%), risk control and mitigation (35%), market expansion (28%) and revenue generation (26%).

These are some of the findings from the Toward Analytics Automation in Asia Pacific study that analytics automation company Alteryx commissioned to market research firm International Data Corporation (IDC).

The study also found that top challenges organisations face when it comes to using analytics include “hard-to-use” tools, lack of data literacy, lineage and data integrity.

See also: Consumer banking trends to watch in 2019, according to this software analytics firm

Additionally, analytics and data products are increasingly being consumed at a faster pace, in a larger scale, and with higher complexity. Organisations face an average of:

  • Nine unique data sources that require ingesting, such as files, tables, queues, repositories and third-party data sets, per pipeline. There are 26 new sources or targets requested per month.
  • Four data types that require transformation. These data types include flat files, object data, Relational, NoSQL, Graph, Columnar, Geospatial, IoT, log files and social media assets.
  • Eight unique targets per pipeline to be delivered. These targets are tables, data marts, data warehouses, federated data, datasets and queues. There are 30 new types requested per month.
The study findings suggest a natural progression to becoming an analytics expert, from strategy to data to workforce to process.

Organisations often spearhead their analytics journey with the strategy dimension to onboard and align key stakeholders, followed by the data dimension to establish policies and practices for data integrity. The workforce dimension and process dimension are scrutinised more in later stages of capabilities development, as they seek sustainability in their data-driven business transformations.

There is room for greater analytics maturity in Singapore. While 49% of organisations in Singapore have a well-planned and well-coordinated data and analytics strategy at the enterprise level, the majority lack the necessary workforce (83%) and process capabilities (94%).

“Despite the rapid rate of digital transformation and data generation, many organisations in Singapore are not yet experts in data analytics. They are at the Beginners stage in their workforce and process dimensions, which are critical for empowering employees to do their jobs better, faster and with greater impact,” says Dr. Chris Marshall, associate vice president, APAC, IDC.

Analytics automation can help plug the workforce and capability gaps to enable organisations to advance their data-driven strategies.

In fact, the IT leaders surveyed believe analytics automation introduces flexibility in organisational setup in leveraging data and analytics talent and better management of data integrity and consistency.

Meanwhile, digital transformation leaders agree that analytics automation can democratise skills required for data and analytics roles and bolster agility and scalability in delivering analytics and data products.

As for LoB leaders, they believe that empowerment of knowledge workers and decision-makers to focus on their domain expertise and promoting traceability to gain trust are achievable through analytics automation.

“Data should no longer sit idly in an organisation. With the help of analytics automation, an organisation can leverage its best assets – people, processes and data – to empower their workforce to increase overall organisational performance and efficiency so that decision-making is faster and more reliable,” says Julian Quinn, senior vice president, APJ, Alteryx.

Photo: Unsplash