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3 Stages of Data Science
Businesses everywhere are racing to extract meaningful insight from their data. Many organizations are spinning up data science teams and attacking problems (some more successful than others).
However, one of the challenges is determining the current stage of data science within the organization. Next is determining the desired stage of data science.
Below are 3 stages of a truly mature data science organization.
The beginning stage of data science is dashboards. It is all about answering “How much?” and “What happened?” by looking at reports of historical data. If done well, it might even help an organization answer “Why”. Many organizations will refer to this phase as Business Intelligence.
The dashboard stage can be very expensive for an organization, in terms of people-hours and money. It usually involves investments in:
- Data Warehouse or some other storage environment, for storing the data in a single location for easy reporting
- ETL (Extract Transform Load) Tools for manipulating, combining, and moving data to the data warehouse
- Reporting Tools for displaying the results and allowing users to “explore” the data
Here are some common questions that can be answered via traditional dashboards:
- How many customers live in each region?
- What were the sales on Black Friday?
- How many patients visited the hospital last month?
As you can see, there are large amounts of value that can be gained by this phase alone. It is critical for a business to clearly understand past performance. Unfortunately, this phase is where many businesses stop.
2. Machine Learning
The real “science” of data science does not begin until the second stage which is machine learning. It focuses on estimating quantities that cannot be directly observed. This could be what movies a customer will like, the price of a company’s stock tomorrow, or the causal effect of a particular advertising campaign. Machine Learning uses the data from the first phase and applies statistical or other methods to gain additional insights.
Think of machine learning as answering the following:
- When a customer moves, will he/she spend money at a hardware store?
- When a credit card purchase is made, what is the probability the charge was fraudulent?
- What is the expected lifetime value of a new customer?
- If a hurricane is coming, what will people buy? (pop tarts? it is true).
Notice the connection between an event and some outcome. The value of machine learning comes from estimating the causal outcome of potential events. This phase is filled with terms such as: machine learning, data mining, and statistical modeling. The machine learning stage is all about looking into the future!
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