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The Philosophy of Data Analytics at Vatico
Data does not merely describe; it can prescribe. This is why it is important to get data analytics right at Vatico. In today’s blog post, we will examine the following
- What is data analytics?
- Why do we do data analytics?
- The Philosophy of Data Analytics at Vatico
- Processes in Vatico: How do we fulfill this philosophy?
What is data analytics?
Vatico prides itself on data analytics, that is handling data systematically to make impactful, sustainable, and data-driven decisions which serve a particular business need.
Why do we do data analytics?
Data analytics shapes crucial decision-making relating to revenue and cost strategies. It helps Vatico to answer questions such as but not limited to:
- Should we increase the price of SmoothSkin products this month?
- Should we allocate more budget for Lazada advertisements?
- Should we reintroduce free shipping?
The Philosophy of Data Analytics at Vatico
Inspired by Drucker’s Managing for Results: Economic Tasks and Risk-Taking Decisions (1964), Vatico prides itself on being effective, reliable, and rapid.
Referencing the table above, to be effective, all tasks in Vatico must meet an important business need. Next, for data analytics to be reliable, the management must be able to trust your numbers without questions. Last but not least, we want to make insights available to the management as quickly as possible.
Processes in Vatico: How do we fulfill this philosophy?
In order to be effective, rapid, and reliable, Vatico emphasizes heavily on processes. We ensure that we plan and are clear about the objective of the task, so that subsequently we can assess whether we have met the business need. During the implementation stage, we ensure quality of work, while trying to make the insights available as soon as we can. Last but not least, we QC to ensure that the management can trust our numbers.
In data analytics, there might be a tendency to be caught up in the technical aspects of coding. However, it is always important to take a step back and examine whether we are effective, reliable, and rapid on a large scale.
