Bringing the Power of Data to Your Business

  • Sakwannueng Trakoolshoke-satian (Champ), Lead Data Scientist, LSEG (London Stock Exchange Group)
  • Chusana Nuntanart (Dew), Senior Data Scientist, LSEG (London Stock Exchange Group)
  • Manussanun Buranachokphaisan (Kaew), Senior Data Engineer, LINE MAN Wongnai
  1. Understand the business: You need to understand what your business does and what data you need. Then start asking questions and goals on how to use data for your organization. For example, sales and marketing are focused on sales, but customer service is focused on customer satisfaction.
  2. Data Collection: Consider what kind of data you need in order to answer your questions and reach your goals. Collect the needed data and store it securely. If that set of data is insufficient to understand your users, find out what more you can collect.
  3. Data Preparation or Data Cleaning: If your data is in an inaccurate format, clean the data before using. In this process it may take a lot of time.
  4. Data Exploration: Exploring and looking at the data trend. For example, the E-commerce business would look to analyze trends from transactions daily/ weekly/ monthly/ quarterly/ yearly.
  5. Data Analytics: This step is advanced analytics. It is data science. Using statistics, data scientists can find out insights of the data then develop a model for machine learning. Some companies combine steps 4 and 5 together.
  6. Visualization: To present data in a visual form such as dashboard, heatmap, and graph, it is important to communicate through proper size, colour, and form.
  • For data engineer: MySQL and Python
  • For data Scientist: Python (there is a library such as Pandas, NumPy)
  • For business Intelligence: Tableau, Power BI, Excel
  • For anyone in the organization: Basic tools such as Microsoft Access and Microsoft Excel to create database and visualisation
  • Data Quality: Some data has to be cleaned before using
  • Data Literacy: Executives and team members sometimes lack understanding or do not see the importance of data collection and analyzation; therefore data engineer or data scientist must show them how beneficial data can offer and get everyone on the same page




Thailand's first and Southeast Asia's largest startup ecosystem. []

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Data Logging: Sampling versus Profiling


#6 Data Preprocessing with Orange Tool

Understanding the Impact of Covid-19 on NYC’s Subway Ridership

Why Does the House Always Win?

GA COVID-19 Report May 29, 2021

Difference Between Standardization & Normalization

Preprocessing for Time Series Forecasting

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
True Digital Park

True Digital Park

Thailand's first and Southeast Asia's largest startup ecosystem. []

More from Medium

Data things that challenged 2021

Importance of quality data — A Review of “Data Cascades in High-Stakes AI” by Sambasivan et al.

Email — your friendly neighbourhood Analytics reporting platform (Part 2)

Five Things About Big Data that You Didn’t Know