113th Daily Writing Challenge

By Bernardus Ari Kuncoro

Data Science Use Cases in Legal Department: Legal Document Analysis and Fraud Detection

There are plenty of legal cases which are related to data analytics. Remember the problem of Cambridge Analytica? Indonesia Ecommerce data leak? BPJS data breach? You name it.

For your reference, the Legal Industry has also been started discovering the standards of data analytics. All aspects in legal such as strategy developments, client interaction and project discovery are transformed digitally.

The data points collected in the digital transformation of legal can be analyzed by Data Scientists such as :

  1. Legal Document Analysis. One of the organization like Data Science for Lawyers provides several material to be learnt by lawyers above. One of the cases is Document comparisons, which basically find similarities and difference between legal texts.
  2. Fraud Detection. This use case is usually combine with outside legal department such as accounting and HR. Why? Because the fraud motive usually about money. And the ones who involve are human. However, in the digital era, hackers are pretty
Data Science for Lawyers Learning Materials (Source: Here)

Kalideres, 21 July 2021


112th Daily Writing Challenge

By Bernardus Ari Kuncoro

Accounting Department Use cases

What is accounting department?

Accounting department refers to the division in a firm that looks after the preparation of financial statements, maintenance of general ledger, payment of bills, preparation of customer bills, payroll, and more. In other words, they are responsible for managing the overall economic front of the business. (Source: here)

What is the function of accounting department?

Well, to make sure all the financial sustainability of business organization

What are the data analytics use cases in accounting department?

  1. Financial Outlook Analysis: Data scientists can utilize financial data to spot trends and extrapolate into the future, helping their employers and clients make the best investing decisions.
  2. Financial Statement Analysis: Data scientists who can read financial statement can help investors or venture capitals decide to invest. This is more like the fundamental analysis in stock market business.
  3. Cost and Budget analysis: Some of data scientists might not be familiar with term budget. Consider it is like a plan for food budget to be spent during the week end. You get the benefit in relationship or social needs. Budgeting is a powerful tool that helps the management in performing its functions such as planning, coordinating, and controlling the operations efficiently. While cost, it is actual spending. If cost lesser than budget, then you still have remaining fund or surplus. Conversely, deficit. If you can predict using the behavior of spending factors plus external trending variables, you can help accounting department to warn the C-levels about their financial status.
  4. Asset allocation analysis: Who have asset? Organization or every persons in the world, right? To be exact, the citizen tho resides in the country that have the law of ownership. The proportion of industry asset allocation can be set to reach its financial goal. It is true that asset allocation is fundamental for successful investing. Data scientists together with Accountants help organization choose your investment strategy wisely and build a profitable portfolio.

Kalideres, 20 July 2021


108th Daily Writing Challenge

By Bernardus Ari Kuncoro

In Supply Chain Subdivision

In commerce, a supply chain is a system of organizations, people, activities, information, and resources involved in supplying a product or service to a consumer. (Wikipedia)

To supply a good or service to a consumer, procurement and logistics must be provided. To ease the accounting, most of the time procurement activities are included in Finance, instead.

a. Supply Chain Planning

The goal of planning in supply chain is to make sure that the goods are well delivered in a good condition and timely manner. But due to big data with 4V characteristics, the problems are complex and can be derived into the following challenges that data science can solve.

  • Making the supply chain greener to minimize the environmental impact of global sourcing (e.g., shorter distances or consolidated shipments)
  • Increasing visibility into the supply chain and response time (e.g., through blockchain)
  • Adapting to demographic changes and customer expectations (e.g., free same day deliveries)
  • Allowing manufacturers to decrease their product life-cycle times (e.g., through better market insights and smart sourcing) to react to trends and demand more quickly
  • Increasing the product portfolio to serve not only the mass market but the entire demand curve (e.g., through mass-customization)

(source: Here)

b. Capacity and Inventory Planning

Most of the time, manufacturing company that utilize warehouse, they need to perform capacity and inventory planning. The idea is simple. Making sure that the flow of goods are in control based on demand and production. They will distribute based on the forecasting of goods, hence consumers are happy. They can buy and get the product in desired time.

The challenges of capacity and inventory planning are optimizing the interval of 80 – 90% capacity.

Interested in digging up this case? Find the sample MIT thesis of Raytheon’s Circuit Card Assembly (CCA) factory here.

To be continued

Kalideres, 16 July 2021


107th Daily Writing Challenges

By: Bernardus Ari Kuncoro

In production subdivision, there are following data science use cases.

a. Demand Forecasting

In a way to support company’s vision and mission, production team have to make sure how many adequate results of the products. This case is a bit complicated, because not only historical data will be significant, but also external data including the buying power, season, and trend of the market. For data professionals, you need to be aware of those data sources, even ‘unknown data’.

The Demand Forecast vs Actual Sales (Photo was taken from here)

b. Strategy and Operation Planning

To become an impactful and efficient company, strategy and planning in operation are fundamental. Every step of production must meet the criteria of the optimization. In a service company such as banking, for example. There is a specific need on each branch to prepare enough cash money.

This use case is real in one of the biggest bank in Indonesia (Bank Rakyat Indonesia) whose branches located in all of the Sub regency in Indonesia Archipelago. They perform machine learning modeling by using the historical data on each branches and unit. The cash money prediction result will be reference for them to stock the cash money in the office.

Cash optimisation for BRI Branch Offices and Business Units

c. Operational KPIs Dashboard

Each divisions has goals to reach the company’s mission. Usually they use Key Performance Indicator to track the routine tasks. The dashboard will show how many products have been produced, challenges and problem lists, and so on.

d. Product Allocation

Mapping the number of produced items based on the condition in the market is very crucial. Imagine the Unilever produce the bar soaps. Usually they will use historical data, either it is based on seasonality or trend.

e. Predictive Maintenance of Manufacture Machines

Imagine you have the machines from a toaster that you use for breakfast to a computer that you utilize for working. Realize or not, if they fail, what will be the impact? We might not be productive, right? You will not have a toast bread ready. Hungry. Cannot work. Therefore, the quality of a machine is not only based on how useful and efficient, but also how reliable. To be reliable, maintain the machines is obligatory. I recommend you to read this article to jump start the business problem and solution options.


106th Daily Writing Challenge

By: Bernardus Ari Kuncoro

2. Use Cases in Operation

Operation division’s goal is to make sure all the products are ready. The results should met the quality and quantity expectation of the market.

This division is where inputs, or factors of production, is converted to outputs, which are goods and services.

Operations is the heart of a business providing goods and services in a quantity and of a quality that meets the needs of the customers. Operations control the supply chain, including procurement and logistics.

Use Cases in Operation

To be continued

Kalideres, 13 July 2021


104th Daily Writing Challenge

By: Bernardus Ari Kuncoro

It is essential for data professionals know and be familiar with the bank of business problems. The more and various problems they face, the more they become experienced. In this article, I would like to frame the overwhelm thinking about business for data professionals.


  1. Introduction
  2. Use cases in Operation
  3. Use Cases in Finance
  4. Use Cases in Marketing
  5. Use Cases in R&D
  6. Conclusion

1. Introduction

Three common divisions in profit-oriented organization that include Operation, Finance, and Marketing

Most of the time, we found that profit-oriented organizations have at least three division. The one that is responsible for production that includes supply chain, and IT Ops. We frequently call it as Operation. The Finance team that includes Human Resources, accounting, and procurement. And also marketing, the division which is responsible for sales, marketing, and customer service. Last but not least there are some fully funded organizations who have budget for innovation, thus they add more division like Research and Development (R&D).

Additional R&D division that might be more relevant to organization that prioritize the innovation. Note *HR can sometimes standalone or be under operation.

2. Use Cases in Operation

To be continued.

Kalideres, 13 July 2021