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.

1. Dashboards

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:

1. Data Warehouse or some other storage environment, for storing the data in a single location for easy reporting
2. ETL (Extract Transform Load) Tools for manipulating, combining, and moving data to the data warehouse
3. 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!

3. Actions

Determining the actions to perform, is the third and final phase. It tries to capitalize on the results of machine learning in order to take appropriate actions. The following actions might be suitable for the events identified in the predictive section above.

• When a customer moves, send a “welcome to the neighborhood” packet with coupons to nearby hardware stores.
• Decline the fraudulent charge or deactivate the credit card.
• If the new customer has very high expected lifetime value, provide some special treatment or offers to ensure the customer becomes a customer for life.
• When a hurricane is approaching, place Pop tarts near the front of the store.

As you can see, good machine learning from the second phase can lead to clear actions.

Conclusion

Claiming success in Data Science is all about conquering all three stages. Each stage builds upon the previous stage. If you have put in the effort to complete the first stage, why not continue to the second and third stages?