Why Data Analytics?

The world’s most valuable resource is no longer oil, but data

The quote from The Economist above could emphasize how important data is in this era. With the technology breakthroughs nowadays – cheaper and better processing power and data storage as well as easier data collection, it is important for any company to utilize the data to make better business decision.

Businesses now can making better decision from utilizing various data sources such as customer data, operation data, financial data, etc. With more data, better decisions could be made resulting in more efficient operation, more informed decision and better strategy.

Better decision backed by data could help the company to increase profit from lower cost and more revenue as well as establish sustainable competitive advantage from better employee engagement and higher customer satisfaction. For example, UPS trucks don’t turn left, the decision made by data resulting in shorter delivery route – lowering operating cost for the company, more revenue from more delivery trips, better employee engagement as it could save time, and higher customer satisfaction as the delivery could be done faster.

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.

Where are we in Data Analytics?

According to Maslow’s Hierarchy of Needs, A person who did not have enough to eat (Physical Need) would not looking for Runway fashion show (Self-Esteem). Similar to person needs, different organizations have different level of needs from Analytics. Implementing a machine learning inside the organization without data properly stored would be giving a beggar one first-class ticket to Maldives.

Our team at Davoy has invented ‘Maslow’s Hierarchy of Analytics’ which identify where the organization is in their analytics implementation and what are the needs they will be required to step-up. It consists of 5 levels per the illustration below.

1) Data-driven Decision

The companies in this stage have data stored but not cleaned or connected. An easy example here is the street food stall in office area knew that they could sell on average 500 Padthai on weekdays and only 50 on weekends, so they decided to close the shop on weekends. Even though the data was not stored electronically, it still could be used to make good business decision.


Next stage of making decision based on data is to make the data stored properly and integrated. For instance, sales data from marketing department could be combined with profit data from finance department easily. The companies in this stage will dispose of silos data and created the integrated data storage.


Time and tide wait for no man. Having only integrated data would not be useful if the industry is very competitive such as online advertisement. After having the data properly integrated, the aspiration is to have as much updated information as possible. This stage also includes for real-time viewing or analysis of data such as dashboards.


Often seen in retail or marketing sector, we do wish to know customers as much as possible and give them the most suitable offer. To be able to do this, we must have individual data of each customers. Even though this might seems easy, the actual implementation is very hard. Try imagine a convenience store which do not have any loyalty program membership, you would not know how often the customer visits or what are the products purchased.


On top of the pyramid, the highest level of data analytics is to use AI (artificial intelligence) or ML (machine learning) to make model adjustment and real-time optimization without human intervention.

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