ครอบคลุมเทคโนโลยีที่สร้างความพลิกผัน (Disruptive Technology) และขับเคลื่อนธุรกิจในยุคดิจิทัล

Home > Archives > 2019: The job function of the data worker continues to evolve
image
2019: The job function of the data worker continues to evolve
image
January 3, 2019 data analytics data science

 by Leslie Ong, Southeast Asia Country Manager, Tableau Software

For businesses across the world, the benefits of embracing data analytics are innumerable and tangible. However, as companies look to uncover the potential of data, this has led to a massive skills shortage across APAC, with data scientists in particularly short supply. In the third of our 2019 predictions, we take a closer look at how the skills required to work with data have been evolving.
 Data democracy elevates the data scientist

Businesses are no longer relying on just the data specialists to perform analytics. More departments and roles are expected to work with data; organisations are seeing an overall increase in data literacy and the emergence of citizen data scientists. According to Gartner, a citizen data scientist is “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” This is shifting the definition of data science, and blurring the lines between those with traditional data expertise and business domain knowledge.

Today, data scientists are expected to have advanced statistical and machine learning knowledge, but they are also expected to have a strategic mind for the business, including a deep knowledge of their industry. The outputs of algorithms and models are only effective if they help solve the right problem in the right context. This means working hand in hand with stakeholders to identify and refine the problem statement and hypothesis at the beginning of the process and keeping them involved throughout the workflow.  And at the end of the workflow, it means knowing how to communicate the results to business partners in a way that is relevant and actionable.

Sonic Prabhudesai, Manager of Statistical Analysis at Charles Schwab, says: “Statistical modeling and machine learning are now becoming table stakes in order to become a data scientist. The differentiator is how well those working in the field can communicate their findings in a simple, but actionable way.” Instead of handing over results, data scientists will have a core role in how those results are applied to the business.

With self-service analytics tools, both data scientists and advanced users can explore and get a better understanding of their data. This sparks insights that can direct the rest of the analysis and ultimately, impact the business.

Data storytelling emerges as the new language of corporations

However much we automate, however big our dataset, however clever our calculations, if you cannot communicate findings to others, you can’t make an impact with your analysis. This is the power of data visualisation. Data visualisation is a language and it’s becoming standard for analysts to know how to convey information to decision makers in a way that is actionable and easy to understand. This skill, combined with the ability for analysts to share the steps they took to discover the insights in data, is often defined as “data storytelling.”

Data storytelling is a critical element of the analytics process. And a changing workplace culture, where analytics reigns supreme, is refining the definition of data storytelling. As organisations create cultures of analytics, analysts’ data storytelling methods are more about nurturing a conversation around the data and less about arguing for a singular conclusion. These analytical cultures are also fostering data literacy efforts aimed at teaching people to truly understand the data and to be participants in the analytical conversation—from the moment of discovery to the resulting business decision.

Data storytelling will continue to permeate workplace culture as more organisations create work streams and teams focused on analytical collaboration. This approach is shaping how organisations use data to engage, inform, and test ideas. And as more people understand how to interpret data and explain their analytical process, it amplifies the potential for business impact.

(0)(0)