The Need for Data Literacy

Disruptive Mode Published in Sunstar on September 2, 2021

The power of big data, data science and data analytics propel businesses and governments around the world today to develop clear and effective solutions and strategies to meet targets, address customer or citizen’s needs, and generate more significant results. The challenge is how to find experts well-versed in data science and to influence decision-makers and leaders to value data.

A one-shot approach would be to think that data science is a singular field instead of a multi-faceted discipline that does not belong specifically to one academic course but is a combination of skills, mindset and experience ingrained in a person or an organization such that it sees the value of collecting, storing, analyzing, processing, visualizing, and sharing data and transforming data in becoming actionable insights.

Where do we seek support on this matter since most of our leaders today especially in the public sector do not see the value of data. In the Philippines, data is simply news, either good or bad. It doesn’t normally draw strategists to plan about future steps or create more data to identify pain points and solutions.

The level of data literacy of most of our leaders is very poor, such that it is also hard to expect a citizenry that is empowered by data. This country needs to create a new culture and a new generation of Filipinos who will value data whether historical, informative, predictive, or prescriptive as important tools to development.

In his book, Data analytics, John Harper says analytics has been defined as the process of understanding the data by creating certain meaningful patterns. Analytics is a vital aspect in various types of businesses for understanding not just the performance of the business but also for quantifying such performance for better knowledge of trends. The most important aspect of analytics would be the visualization of data.

On one hand, the main idea of data science is to work on data-driven decision making. Data-driven decision making is the discipline of creating decisions that have the backing of analyzed data that has been collected from some relevant sources. Without this kind of data, it is easy to base your decisions on experience, intuition, or on what others tell you are the right decisions. However, all of these can be wrong. To come up with smart decisions, data-driven decision making may incorporate experience, intuition and vast knowledge.

The main aspect of data science is to discover findings from data. It involves unearthing hidden insight that can allow companies to make smart business decisions.

Data science is a multidisciplinary job, and there are three main competencies required. Mathematics, technology and business acumen.

Math skills are at the very core of determining meaning from data is the ability to be able to see such data in a quantitative way. Data contains patterns, textures, correlations, and dimensions that are expressed numerically.

Harper states ingenuity and creativity as requirement for using learned technical skills to build models and then find the correct and clever solution to a problem. The ability to leverage technology to acquire vast data sets and to work with algorithms is also important. With tools like SQL, SAS and R, a data scientist is able to piece together data and information that are not structured and bring out the insights that would otherwise remain hidden or unused.

A data scientist needs business acumen or subject matter expertise. A data scientist is a strategy consultant before anything else. Data scientists are valuable resources in companies because they are in the position to be able to add significant value to the business. However, this means that they have to know how to approach a business problem and how to dissect it, and this is just as important as knowing how to approach an algorithmic problem. Ultimately, value doesn’t come from a number; it comes from the strategic thinking that is based on that number.

One suggested way to develop data science skills is to mainstream data literacy in the educational system across various disciplines. Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. Data literacy can protect us from scheming politicians who have mostly no compunction about twisting facts and figures just to get elected. So data science is a timely topic.

Categories Opinion

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