Telco Big Data Golden Harvest on the Digital Fields

Telco Big Data Golden Harvest on the Digital Fields Are you familiar with the term big data? Big data has become an important commodity in the digital era to help business development in various sectors.

With comprehensive big data analysis, companies can determine the right strategy and business decisions.

Well, one of the significant uses of big data is big data from telco companies. This was discussed in the T-Connext Pre-Event Webinar:

Utilizing Telco Data to Connect with your Customers which was held online on Tuesday (20/9). T-Connext is a program to open various opportunities for empowerment and growth between ecosystem actors with digital product innovation and Telkomsel’s investment portfolio.

In the webinar it was revealed that telco big data can be a significant, more comprehensive solution considering the large number of people accessing the internet in Indonesia.

In 2020, 191 million people accessed the internet in Indonesia and it is projected that this number will increase to 250 million in 2025.

Who will benefit from big data solutions in 2023?

Telecom operators are at various stages of maturity with respect to their advancement in big data usage. We can classify them as businesses that:

  1. Have not leveraged big data yet, but have data to process (so-called info-archive companies)
  2. Have started their journey in implementing big data but have yet to apply a full-spectrum solution (so-called info-familiar companies)
  3. Have established a robust big data environment (so-called info-smart companies)

At each level of maturity, telecom service providers can identify issues and pain points regarding big data, and then set up a plan to uncover opportunities and take the most productive steps going forward.

Companies from the first two categories can harness big data’s potential by investing in custom data solutions that will help them with optimizing analytics to increase profitability.

The example of using big data in the telco industry comes from the top, as many telecom companies are already leveraging big data analytics, including AT&T, Swisscom, Telia, T-Mobile and Vodafone.

Accompanying those names are numbers that also express the growing interest in big data from the perspective of telecoms:

  • According to Tractica, companies from the telecom industry are expected to invest $36.7 billion per year in AI-related solutions like software, hardware and services by 2025;
  • According to KBV Research, the global telecom analytics market is expected to grow to $8.7 billion by 2025.

Let’s continue with specifics on how telecom businesses can harness the power of big data.

What are the benefits of using telco big data? Check out the following reviews, come on!

Deep Analysis to Reach Out Customers

General Manager Data Solutions Product Telkomsel Aulia Rahman Amin who attended the webinar said that wide customer coverage to remote areas is one of the advantages of using telco big data.

Thus, big data analysis can be carried out in a more comprehensive manner and provide maximum output. Telkomsel alone currently serves more than 169.7 million customers in Indonesia, leading the market share in the country.

Data collected by telco companies is also more varied, so that users can help formulate business decisions by considering aspects as a whole.

Telco big data can also be used creatively to reach customers registered with the telco company. So, apart from gaining insights from data analysis, business people can also use it as a means of promotion, campaign or survey.

Make More Measured Decisions

The benefits of using telco big data are also being felt by financial service industry players, one of which is financial technology (fintech) companies. In the same webinar,

Chief Data Officer Kredivo Pramananda Budi Setyawan said that the use of telco big data has helped his party make credit risk assessments more accurate by making predictions about the possibility of default from each customer.

Thus, companies are more selective in accepting users. Simply put, data analysis helps fintech companies to get more users, while still minimizing the risk of default.

“If we compare telco data with other external data, it turns out to be far more predictable. So, from telco data, we can have higher accuracy to find out which users can pay and which cannot,” said Pramananda.

Kredivo Chief Data Officer Pramananda Budi Setyawan

Telco big data can also be used to validate certain information that is sometimes inconvenient for customers to fill in directly.

At Kredivo, for example, Pramananda revealed that prospective users often object when they have to fill in the address validation column when registering.

This address validation process tends to be considered complicated to the point of discouraging prospective users from continuing the registration process.

With big data telco solutions, customer convenience is maintained and business needs are also met. There are other important things that must also be noted here!

According to Pramananda, telco big data is difficult to fake. Of course, this is a valuable value that marks the difference between telco big data and other external data.

When is the Right Time to Use Telco Big Data?

The answers to the questions above can be started by conducting an internal assessment. The process includes mapping pain points, so that later it can be seen whether the telco big data is the right solution considering that not all problems can be solved in the same way.

The next step, establish intense communication with telco data provider vendors. This discussion is important, allowing both parties to exchange ideas about what output to achieve, so that later the data presented is as needed.

What you can’t miss is diligently doing backtesting from the results of the data analysis provided. The last thing that must be considered is compiling performance metrics from the use of this data.

What you want to achieve from A to Z must be clear, don’t let it float around, especially if it hasn’t been arranged.

To achieve this, it takes a lot of discussions with internal business teams to prevent miss translation during the big data utilization process.

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