Data analytics: Improving decision making in the digital era

Written by Student Reporter (Carino Javier, Management 2021)

“Data is the new oil.” Data enables us to make effective decisions instead of relying on gut feelings. Muhammad Fajrin Rasyid, the Director of Digital Business of Telkom Indonesia, talked more on the capability of data analytics to improve the decision-making process on Saturday (19/9/2020).

The Director started the webinar by introducing four types of data analytics to help our decision-making process:

  1. Descriptive: What is happening in my business? Descriptive analytics is regarded as informational and hindsight. It only describes the present situation.
  2. Diagnostic: Why is it happening? Diagnostic analytics are heading towards optimization; it offers insight towards the current phenomena we are facing.
  3. Predictive: What is likely to happen? Predictive analytics offers us insight into the future. Historical patterns are often utilized in predictive analytics.
  4. Prescriptive: What do I need to do? The most advanced and complex analytics, prescriptive analytics, does not only predict the future. It also provides recommendations upon what we should do.

Fajrin continued by highlighting the importance of big data in his own company, Telkom Indonesia. “We use big data analytics to build a model to predict our churn rate. So, we can analyze and realize when and why customers are unsubscribing from our services. Then, we can fix or avoid them from unsubscribing, thus increasing our revenues,” said Fajrin.

Data analytics in human resources

Still in the same webinar, the Senior Technical Advisor at BDO, Heru Wiryanto also came as a speaker. Heru’s topic revolved around the usage of data analytics in the context of human resources.

Before going deep into human resources, Heru highlighted the importance of using insights from data accurately. Three steps in data analytics, he emphasized: analyze, align, then act. “The one that I appreciate is actionable insight,” said Heru while mentioning that HR analytics offered an evidence-based approach in a qualitative-based environment. “It means with data analytics, we can create the right recruitment, development, and compensation processes,” Heru added.

Discussed on predictive analytics as the highest goal, Heru showed his slide consisting five steps to start as follow:

  1. Ensuring data accuracy: The fundamental part is to ensure our data accuracy. We have to eliminate discrepancies, invalid, and duplicated data.
  2. Integrating data: Then, we must blend the data into one hub. Organizing and incorporating them to enable us to understand them.
  3.  Telling the story behind the data: We must know the right context to understand and interpret the data to take action effectively.
  4. Gaining competitive advantages: Having data and the capability to interpret its meaning gives us competitive advantages. We can use them to benchmark employees and set realistic goals and targets.
  5. Using predictive analytics: Finally, we can implement predictive analytics after using insights. We started to shift from historical data into predicting patterns in the future.

Heru revealed what it took to have a “superhero” workforce analytics team. Heru said, “To have maximum impact, the team must have good data, be good at storytelling, have business acumen, mastery of visualization, strong psychology skills, and master numbers and statistics.”

Important to be noted, as Heru insisted, people analytics and automatization are not about replacing people. “It is about making their jobs easier and more impactful,” as Heru made it clear.