Data Consulting and Architecture

WHAT WE CAN DO

Sales Data Analysis

Various techniques are applied to POS data to generate insight. Sales Analysis provide overall sales trends and breakdowns. Cohort Analysis provides insight to repurchase pattern of the customers. Market Basket Analysis shows which products are purchased together and could be useful to creating bundle products.

Customer Data Collection

Transform the process of buyer data collection from paper to digital form with better experience for customers and real-time dashboard data for internal use.

Data Management and Cleansing

Enhance data quality and availability by cleansing data within the current database and ensure clean data collection for incoming data. For example, mobile phone number must be 10 digits starting with 06, 08, or 09.

Real-time Dashboard

Real-time dashboards allow users to access the data and make decision real-time with pre-designed layout as well as additional quick tabulation. This could be done via various tools such as Power BI, Tableau, R Shiny, etc.

WHAT IS DATA ANALYTICS ?

Data Analytics consists of 3 major areas: Data Engineering, Data Sciences, and Data Analysis.

Data Engineering

is the infrastructure of how we collect data and which platform we use to produce results. For example, a company may collect their data via online survey using TypeForm. Then, the data is passed into Amazon Redshift SQL database, then visualized using Tableau.

Data Sciences

deals with how the model are created, especially when doing predictive analysis. Mostly this has to be done with coding with R or Python as popular languages. For example, Netflix used machine learning to create recommendation system.

Data Analysis

is how to interpret the work of data engineering and data sciences into business sense. For example, the UPS case cited earlier is the example of how to data analysis works. Once learned something from data, we implemented the strategy back into business decision.