Data Girls, how to build analytics foundation

This guest post is written by Polly Wong from Facebook APAC HQ in Singapore where she works as a Marketing Science Analytics Manager and who offered to look back and  share important elements of her data analytics journey

The Data Girls

The Data Girls workshop attracted many data lovers to attend for better analysis and visualization skills. It reminded me of how I progressed through my career from an engineering graduate to a regional analytics manager at Facebook Singapore in the past decade. Nowadays I do marketing analytics, which is a multidisciplinary expertise from computer science, information technology, business, statistics and mathematics.


In 2003, I started as an engineering intern and soon realized that my interest was not in engineering calculation. My passion with marketing analytics began with an assignment to clean up the data from engineering product event with MS Excel and to provide suggestions on how to improve event registration. Although I could not remember the details that had led to the success of the next event, this exercise had convinced myself that even a small change on campaign or data strategy could lead to incremental revenue. I have then worked in both in-house and agency roles with many large organizations in B2C and B2B markets to develop deep and actionable customer insights to transform their campaign performance. The key of making a meaningful change is based on insights from data.


Build a strong foundation and evolve along the roadmap.


You could use different approaches to analyze data for valuable insights. The evolution starts from foundation work such as data organizing to predictive analytics or machine learning. The following diagram shows a typical roadmap of analytics work. The more data sophistication, the more impact you could expect it to bring to the business.


Analytics foundation refers to the data management work, such as consolidating multiple data sources, removing inconsistent or invalid data points, and preparing regular reports. With the advancement of technology and tools, analytics has to become a diagnostic tool to understand, for example, why sales dropped in the top 10 customers. Analytics reporting facilitates insight communication to the business audience. The last stage, prediction, is the game changer. Predictive tools like modelling allow us to project what will happen in the future, which can be very powerful in driving business results.


Communication skill remains the key


In addition to mastering the use of analytics tools, communication skills are the key to success. In business environments, the most valuable analyst is usually the one with strong technical skills who can build a predictive or machine learning model in a couple of weeks, as well as being able to communicate the outcomes to a non-technical audience. I personally transformed myself from a data geek to a good communicator by attending public speaking training for over three years. This transformation takes times! But it is very rewarding to be able to explain the business implications of your analysis and have a deep conversation with clients who are very curious on the analytical approach.


Women in Analytics


Last but not least, female data analysts are usually the minority at the workplace. According to a past population study, only 26% of women make up the “computer and mathematical occupations” category.# I used to struggle to present in front of a large group of men and get support from peers. I now strongly believe that being a woman with analytics career has a strong edge and the hard work pays off. If you are interested in meeting more women in analytics, you are welcome to join our Data Girl community. It is a group to bring this community closer together in Singapore and Asia Pacific.









Polly Wong

Analytics Manager @ Facebook APAC

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