Driving Digital Transformation with Data Science in the Disruptive Era
Dr. Agus Setiawan

PhD Graduate and result-oriented Director with 25 years experience with involvement in all levels of Business Strategy, Sales and Marketing, Managing Project and Product Development. Aside of managing a company, he is also the best corporate trainer and public speaker in seminar and conference.

Driving Digital Transformation with Data Science in the Disruptive Era

In today’s era, all aspects of business are affected by digital transformation, including operations, go-to-market strategy, customer service, marketing, and finance. However, digital transformation involves more than just utilizing new opportunities and accelerating business processes. Additionally, it is about the need to maintain one's position in a business environment that is rapidly changing and outpace digital disruption. We should have a deep understanding of the industry we operate in, how it is changing, and the new innovations we could adopt or build to maintain our competitiveness and expand into new market segments.

To stay competitive, businesses must keep up with the digital revolution, utilizing these smart technologies and incorporating them. Later, the enormous amount of data generated can be processed and turned into strategic information that will help the business. This is where data science plays a critical role in driving digital transformation.

Businesses can adopt the data-driven approach to digital transformation to determine what needs to be transformed and how, as well as to reduce risks and avoid wasting resources. They gather, process, and analyze their business data using data science before turning it into insights that can be put into practice.

Data Science in A Glimpse

Data science is the study of systematic knowledge and pattern extraction from data for research advancement, organizational decision-making, and enabling a data-driven society (Kelleher & Tierney, 2018; Ahalt, 2013; Dhar, 2013). However, this straightforward explanation demonstrates how much science, the scientific method, and business analytics have in common with data science. Data science, which is defined as "the principles and procedures for the systematic pursuit of knowledge involving the recognition and formulation of a problem, the collection of data through observation and experiment, and the formulation and testing of hypotheses," naturally shares much of the definition of science and the scientific method (Merriam-Webster, 2019).

What Data Science Can Do in Driving Digital Transformation in the Disruptive Era

1. Authorizing decision-making via a data-driven approach

Digital transformation is a difficult process. By utilizing data science, you can determine how to transform a business digitally and which areas of the business require transformation. This is what we usually called as adopting “data-driven culture”. When an organization measures its progress using data rather than gut instinct or previous examples, that culture is said to be data-driven. This is commonly referred to as evidence-based decision making in the scientific community. Transparency and accountability are fostered in a data-driven culture where team members make decisions based on hypothesis testing, with the data results ultimately guiding those decisions. Data-driven decision-making enables businesses to grow, respond to consumer trends, and equips them to anticipate and address challenges in a disruptive economy. Thus, you can maintain the transformation quicker as a result.

• Satisfy the needs of project stakeholders.

2. Classifying warnings, opportunities, and scopes via data-insights

With the volume of data increasing, there is a rapid increase in the amount of information and insights that are available, which indirectly creates opportunities and expands one's potential as a business or individual. Data science enables us to describe the business environment in great detail and cope with a lack of data experts. For example, based on customer data, it is likely possible to forecast which customers will make similar purchases in the future and what their next purchase will be. To interpret what customers are likely to purchase in such a process, customer behavior analytics and recommendation systems are used. Data science allows for the prediction of future events, risk protection, real-time customer visibility, decision support, and cost reduction.

3. Adding more values with Machine learning

Machine learning promotes digital transformation more effectively. It helps to break massive data to find trends and exceptions. Artificial intelligence is one effective strategy that uses machine learning algorithms to deliver insights, design timelines models, and foresee potential disruptions. For instance, using additive analytics as a solution, hospital staff can estimate emergency room admissions for patients using a suitable predictive model, improving patient care outcomes and cutting time and costs. This is done by identifying which patients are at high risk for readmission.


Any business that can effectively use its data, especially in the disruptive era we live in today, can benefit from data science. Data science is beneficial to any business in any industry, from statistics and insights across workflows and hiring new candidates to assisting senior staff in making more informed decisions. A true data-driven digital transformation will work to improve data strategy, data management, process, and analytics to enable accurate data insight. Thus, business will be much more ready to compete and maintain their competitive advantage in this disruptive era.

Ahalt S. (2013). Why Data Science?. In: Presented at the National Consortium for Data Science. Chapel Hill.
Dhar V. Data science and prediction. Commun ACM. 2013;56(12):64–73.
Kelleher JD, Tierney B. What is data science?. In: Data Science, MIT Press; 2018. p. 1–38.

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