Learn the 4 Tiers of Data Science With ‘Tools for Entrepreneurs’

Numbers-And-Finance

In an attempt to understand what Data Science is, and how this can help you as well as your business flourish, we listen today to Thomson Nguyen, co-founder of Framed Data, who teaches us the four pillars constituting the data pyramid.

In this new Tools for Entrepreneurs web class, of which you can find a video at the bottom of this page, organized by General Assembly and Google for Entrepreneurs, we learn what Data Science really is, and how it can be exploited for business purposes. This new web appointment, as mentioned, is presented by Thomson Nguyen, co-founder of Framed Data, who will talk about Data Science, and will take us through the four tiers that help a data driven company build a data pyramid. The four layers of a data pyramid are (from bottom to top):

  • Key performance metrics, also known as KPIs (Key Performance Indicators).
  • Data Warehousing.
  • Analytics and BI (Business Intelligence).
  • Data Science.

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Defining Key Performance Metrics

At the base of the pyramid we have key performance metrics, or KPIs. An organization may use KPIs to measure its generic success, health, or even to evaluate the outcome in specific areas of the business. Examples of KPIs are:

  • Daily or monthly active users if you sell a gaming app for instance.
  • Daily, weekly, monthly transactions or revenue if you run an ecommerce business.

Whatever you do you need to identify legitimate, useful KPI’s that can help you with the analysis of your organization.

More: A Quick Overview on Google Apps for Entrepreneurs

Data Warehousing

If you are able to identify what your KPIs are, then you need to keep them in an analytics database, which was called, in the old days, a data warehouse.

Analytics and Business Intelligence (BI)

Once you’ve done with KPIs and the analytics database, you can then dive into analytics as well as business intelligence. This also is an important step to understand, for knowing what your company is doing at any given time is essential for surviving.

If, as an example, we suppose you sell a smartphone application, during this important Analytics and BI stage, you may be able to:

  • Understand how much revenue you’ve made today as opposed to yesterday, last week, or even last month.
  • Know how many people downloaded your app, and how that changed over time.
  • Measure results of a pay ad campaigns you deployed to increase the number of app downloads, and see if that campaign actually resulted in an increase in activations.

Whatever it is you’d like to gather, you want to make sure you have the right reports, dashboards and tools that allow you to understand the information.

More: Google Supports Data Driven Journalism Course Scheduled for Early 2014

Data Science

How can you now use this bunch of information available to offensively define products? With Analytics and BI you were, in reality, using data to understand the past, but how can you, at this stage, use all that information to actually predict the future? Now that you have all the above up and running, you’re actually able, at last, to get into Data Science.

Data Science is a kind of machine learning; it is, in fact, a combination of the three components we have explored together thus far: you need to have clear KPIs, an analytics database, and some business intelligence in place. Once you get those down the right way, you can then dive into Data Science to predict the future, and also use tools for driving users to take the path you want them to take.


Intro image: Numbers And Finance.jpg. Image credit: Ken Teegardin via Flickr, under Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0) License
YouTube video: Tools for Entrepreneurs: The Data Science Pyramid. Video credit: Google for Entrepreneurs via YouTube
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