Examining the Importance of Corporate Data through Business

Elice

11/05/2021

It all begins with data! Let’s find out why various companies are making efforts for data-driven strategies and understand the importance of data through various corporate examples.

Apple, Google, and Microsoft’s Unprecedented Announcements on Corporate Data

On April 10th, two long-time rivals in Silicon Valley, Apple and Google, announced their collaboration. Microsoft, which had been one of the most conservative companies regarding copyrights, decided to disclose some of its corporate data. There’s a saying that people do unexpected things when faced with death. So, what happened here?

These unprecedented decisions by the three companies were all prompted by the COVID-19 pandemic. Apple and Google declared joint efforts to collect necessary data for responding to the spread of the virus using devices equipped with Android and iOS. Microsoft, too, decided to disclose the information it had gathered regarding the coronavirus. This appears to be a judgment based on the understanding that with no future for humanity, the future of these companies is also at stake.


글로벌 기업 데이터 발표 사례, 애플 Apple, 구글 Google, 마이크로소프트 Microsoft

The number of COVID-19 cases worldwide is close to 4 million as of May 7, 2020. The darker the color on the map, the more infected the area./Raphaël Dunant, CC BY 4.0


Facebook founder Mark Zuckerberg has said, “There has been a worldwide epidemic of infectious diseases before, but now we have a new superpower,” and mentioned “the ability to collect shared data for the common good.”

Those who live in Korea know that there is a limit to responding to COVID-19 only with data. The medical system is also very important to rein in the epidemic. Korean public health care is highly regarded for its low price and guarantee of medical support for all insured citizens.

In the meantime, it is no coincidence that the IT monsters brought up data rather than approaches like expanding medical personnel or national health insurance that require political compromise and long-term change.


The key to problem-solving lies in accessing data

Despite having a sturdy healthcare system in place, the real dangers of a healthcare system collapsing due to a surge in patients have become strikingly evident through the cases in Italy and the United States. As the Korean government focused on limiting the spread of disease to prevent medical collapse, many resources were devoted to tracking and quarantining personal information.


코로나 사태로 깨달은 데이터 중요성, 데이터 활용 통한 감염병 확산 대응

Korean medical authorities are using mobile phones and credit card usage details of suspected infections to track a person’s movement every 10 minutes while taking necessary quarantine and closure measures./Namoroka.CC BY 4


The American media raised concerns about personal data infringement while looking at Korea’s quarantine policy. However, Korea’s situation must have contributed to the fact that U. S. -born companies like Apple, Google, and Microsoft, have voiced their willingness to respond to the spread of infectious diseases by using anonymized data. During the conflicts, the Silicon Valley giants, who are the most knowledgeable about how data can be utilized as the basis for decision-making in addressing problems, quickly responded.

The decision-making process of organizations with certain goals, including companies and governments, can be summarized into three stages: judgment, decision-making, and action. The outcome of the action becomes the basis for judgment again, forming a feedback loop. Big data being added to this decision-making structure is a recent trend, Zuckerberg said.

In the past, only the government could collect data on a large scale. A country was the only organization with the available manpower, authority, and accessibility required for large-scale data collection. Then, companies began to collect corporate data using digital technology. However, even if a company had accumulated billions of data points, it was futile without the technology to process and manage them effectively. Even today, many companies make decisions based on only a few macro indicators available in the market.


데이터 활용 사례 미국 인구조사, 빅데이터 수집 사례

The US Census, as a prominent example of big data collection, mobilized national administrative resources to survey nationwide population information. The picture shows the landscape of the 1940s census./United States Census Bureau


Then, companies started emerging that collected, organized, and processed data in digital formats right from their startup stages. Technological advancements have enabled the analysis of activity records ranging from hundreds to billions of individuals, predicting their behaviors based on this data. Companies in particular are using this information to forecast sales and develop products. This trend has become prominent, leading to the restructuring of organizational setups for data-centric business activities, coined as “digital transformation.”


Implementation of ERP systems by large corporations that know the importance of data

What is the most fundamental aspect of a company’s own data? There might be various opinions, but the most certain point is its assets. The scope of a company’s activities depends on its capabilities, and these capabilities are determined significantly by the resources it possesses.

As the size of an organization increases, managing enterprise resources becomes more challenging. Resource management ultimately involves understanding what resources are available and deciding how to distribute them. For a conglomerate overseeing multiple subsidiaries, the resource information becomes overwhelming, making it impossible for a single team to handle. Traditionally, the response to this challenge has involved reducing the size of the information to a manageable scale for the responsible organization, defining categories, or lowering the resolution of the information.


무작위 다형 딥러닝 알고리즘 모식도, 기업 데이터 보유 기업들의 전사 ERP 도입

Randomized Deep Learning Algorithm Schema. The characteristic of deep learning is the formation of hidden intermediate layers between the input and output layers through AI learning./Kamy Kowsari, CC BY -SA 4.0


Large conglomerates have naturally adopted digital-based Enterprise Resource Planning (ERP) systems as the capability to process data beyond human comprehension became possible. Samsung established its global ERP in 2010, while Hyundai and Kia recently decided to implement ERP in the cloud, meaning an online database. SK Hynix and Woori Financial Group are also planning to develop systems capable of handling relevant information, and LG CNS has even created an ERP platform utilizing artificial intelligence for internal use.

The reason large corporations are adopting comprehensive ERPs lies in the sheer volume of information they deal with. However, the quantity of information is just the tip of the iceberg. The ultimate goal of constructing digital-based ERPs is the efficient management of resources at the data level.

From this perspective, information-based management of corporate resources is not exclusive to large enterprises or organizations of similar scale. Efficient resource management is a concern for all companies, as effective resource utilization guarantees the longevity of a business and revenue improvement.

Digital ERP systems represent just one example of data-driven corporate activities. However, digital transformation encompasses more than just the digitization of ERP systems. The core lies in leveraging data for corporate operations.


Examples of Data Utilization Strategy Development

One of the problems with Massive Open Online Course (MOOC) programs is the low completion rate. On average, only 10% of students successfully finish the course. This issue is also faced by the Korean-style online open courses (K-MOOC) under the Lifelong Learning Promotion Act, which has spent 30 billion won.


기업 데이터 기반 전략 수립의 사례, 무크 MOOC, Massive Open Online Course 기업 데이터 기반 전략 수립의 사례, 무크 MOOC, Massive Open Online Course

K-MOOC provides online public lectures. Improving students’ completion rates is a common challenge faced by online open course services./K-MOOC screen capture


Elice started as a research project focused on the operation and management of the mandatory basic programming course at KAIST. Therefore, the primary emphasis is placed on ensuring that all learners complete the course, and active use of data is made to achieve this goal. For example, the platform utilizes data on learners’ coding activities to predict their achievement. Unlike traditional metrics such as revenue or resources, the focus here is on cognitive changes related to learning and achievement.


학습자들의 활동 데이터를 활용하여 성취도를 예측하는 카이스트 기초 프로그래밍 과목 대시보드

KAIST’s basic programming course online progress. Class assistants actively support the learning of students with low EPS in red circles./Elice screen capture


The predicted achievement score, known as EPS (Elice Performance Score), is provided to individuals involved in operating and managing educational programs, such as teaching assistants, education administrators, and professors. The achievement prediction score is exactly that – a “prediction,” not a final grade.

If a university conducts a self-paced course consisting of 10 modules, the teaching assistant for that course can initiate additional educational support activities for learners who have low EPS scores after completing the first module. Examining the practical exercises or conducting counseling sessions are specific methods employed.


수업 상황에서의 데이터 수집과 데이터 활용

Is there any data to collect from classes? If you can describe the information you want to extract and the context in which it occurs, there are undoubtedly techniques available./Motoko C.K., CC BY-SA 3.0


It is very difficult to determine when and how instructors will intervene in an online environment where instructors and learners are physically disconnected. Nevertheless, in an online learning environment, recent studies on online learning and distance learning have made it clear that it is the timely intervention of learning assistants, including teachers, that has the greatest impact on student participation and achievement levels. The function of EPS is to inform instructors at an appropriate time of who students need this intervention.

The overall predicted achievement of learners will gradually increase if assistants continue their learning support activities by examining the EPS of all students after each unit study. When all 10 units are complete, the shape of the actual achievement graph will look almost the same as the changed EPS graph.


**The First Step for Digital Transformation: Know Your Purpose, and Find the Right Data **

The introduction of chatbots by financial-related companies is a customer management method similar to Elice’s ESP. Chatbots are simply a method of customer counseling using AI. Through various customer response services, including KakaoTalk, answers and guidance are created in advance for what customers often inquire about and provide according to the situation.


챗봇, 기업 상황에 맞는 내부 데이터 활용

Many companies are adopting chatbots, but is the internal data utilization tailored to each company’s situation? What kind of data can be utilized?/pxhere


In-house data that classifies and organizes existing customer inquiries are utilized for the answers and guidance provided. Accessible technologies can vary. However, only executives and employees who work at the company can provide solutions that fit them since each company may have different customer tendencies and activities. After all, whatever the method of digital transformation is, the starting point must be from within, not out.

As such, the use of data is not limited to areas such as explicit resource management and production. Understanding the purpose of an activity and recognizing the existence of necessary data to achieve that purpose or the potential for data collection of certain elements, makes the utilization of the required technology secondary. Big data-related technologies are more than enough. Not knowing what technology to use is closer in meaning to not understanding what data is needed and, furthermore, not knowing how the purpose of the action can be reinterpreted in terms of data.

In the following text, we will explore work areas where data utilization might not have been initially considered, but upon closer inspection, it becomes apparent that data usage is essential. These insights can be helpful in identifying areas where digital transformation is possible in your current work domain.


Read more
👉 The importance of digital transformation as a rediscovery of data utilization


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