This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
Read Alteryx customer stories to learn how they transform their organizations into becoming a data-driven business.
Announcing Alteryx + Snowflake | Alteryx and Snowflake make analytics and data science fundamentally easier. With the new integrated starter kit, you can push down data prep transformations and more into Snowflake for faster data quality and analytics output. Learn More
NTUC Income was established in 1970 with the singular purpose of making essential insurance accessible to all Singaporeans. As the only insurance co-operative in Singapore, Income has remained committed to providing meaningful and relevant protection to the community. Today, Income is a leading digital and multi-channel insurers serving over two million people in Singapore who look to us for trusted advice and solutions when making their most important financial decisions. Our wide network of advisers and partners provide life, health and general insurance products and services to serve the protection, savings and investment needs of customers across all segments of society.
We were looking for a modern analytics platform that could solve our data challenges so that we could free ourselves from mundane data preparation tasks and transform our roles to better support Income’s overall digital transformation process in creating better experience for our customers. We were quite intrigued when we heard that Alteryx was featured as one of the leaders of Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms and decided to give it a try. Upon evaluating the platform, we were quickly impressed with its friendly drag-and-drop user interface that provides good auditability of data workflow. Also, we found that Alteryx comes with comprehensive database connectors and wide range of analytics functionalities. We feel empowered that we could now perform modern data manipulation and modelling techniques on large data without requiring any prior knowledge of programming.
Describe the business challenge or problem you needed to solve
The actuarial team at Income deals with data extensively on a daily basis, covering all aspects of data extraction, data preparation, data visualization, to even data modelling, to help us make important decisions such as setting appropriate pricing for our products and ensuring adequate reserves for our insurance portfolio. To help us manage and analyse these large volumes of data, we rely on tools such as Microsoft Excel and Access, as well as some programming languages such as SQL, VBA, or R.
As most data users in any organization would agree, data comes from many different sources and in various sizes and formats. We use multiple tools to converge the data, which often creates many silos of data processes. This will then result in data reconciliation issues in our end analysis and reports. Another problem we face is that some of the data processing tools we use are not effective in handling huge volumes of data and require significant time for our analysts to manually customise the data to serve insights to multiple stakeholders. They are lacking in audit trail and documentation logs, which makes it difficult for a new analyst to trace data errors or make enhancement to the existing data processes.
Describe your working solution
We took a step back and reimagined what would be the ideal data architecture in providing quicker insights to our business stakeholders. We determined that there are three components to this ideal data architecture: 1) a single source of truth from data perspectives, 2) an automated data preparation and reporting workflow, and 3) a user-friendly data visualization and modelling platform to generate insights.
We started adopting Alteryx because it has capabilities to link up all three components of our data architecture. For example, in one single Alteryx workflow, we could extract data from our enterprise data warehouse, perform data transformation steps, and connect the cleaned data to our machine learning platform and visualization tools (such as Tableau). With Alteryx, we could automate our workflows and reuse it to serve various stakeholders with their data questions.
To illustrate further, here are two examples of Alteryx workflows that our users have built - one for studying insurance reserve adequacy, and another for visualizing results from experience study. In terms of auditability, we like that the workflows help us compartmentalize the various preparation steps, so that even new users could understand it in one glance. We have seen great time saving from automating these data workflow, in some cases, cutting a few days of manual work into just one hour automation.
Workflow illustration 1:
Workflow illustration 2:
There were three aspects of transformation that we hoped to address through Alteryx adoption. First, we wanted to build a future-proof data processes that is easy to maintain. To do so, we started mapping out our multiple data processes and various stakeholders to identify common workflows. Then we drew out the data architecture that could centralize the workflows to satisfy their needs. By following through this data architectures, we hope to make our data processes cleaner for the future, instead of the current spider webs that we are facing.
Second, we wanted to ensure our analyst are geared up to use the new tool effectively. We started identifying champions - they are our early adopters who could self-initiate their learning journey. Because Alteryx is a new technology to us, it comes with a very different data framework, so our analyst had to overcome that learning curve quickly. Fortunately, there are many learning resources out there. The Alteryx online community, videos, and tutorials out there are very helpful – and we find that the easiest way to learn is to go through these materials then put them into practice immediately.
Lastly, we wanted to achieve results incrementally by practicing agile methodology. So, we took a step-by-step approach by initiating mini projects, which we called the Happy Flow. Happy Flow for us is an end-to-end mini prototype that our analysts could develop in a short timeframe to test whether our data workflow could fit within the data architecture. Once we came out with a small prototype, we put this framework into other use cases and iterate along the way.
By focusing on these three aspects, we find that we were able to build confidence and skillsets as we improve our workflow along the way.
Describe the benefits you have achieved
Since adopting Alteryx more than one year ago, we have seen promising results in workflow automation. For example, we have been able to transform the data preparation steps of our reserving study, which involves large volume of data, into a 1-click automation that completes in half of the previous turnaround time. Being freed up from the mundane task of data processing, we are now more empowered to perform other tasks such as detailed study of the reserving data to provide new insights for our business stakeholders. We are looking forward to scaling up the adoption to include such as setting up business monitoring dashboards or product pricing templates, and explore other use cases across the company. Through the use of advanced analytics tools like Alteryx, we see the actuarial function playing a bigger role in supporting Income’s overall digital transformation process to create a better experience for our customers.