Data is everywhere. We create approximately 2.6 quintillion bytes of data daily, and that number is only growing with the increasing number of devices. All major industries are incorporating analytics into their infrastructure. From business intelligence to healthcare to finance, data is being collected, cleaned, stored, analyzed, and optimized. As you learn data analytics, you will find that data goes on a journey from being raw to optimized. Mastering the foundations for analysis and knowing the differences between the sophistication levels of data analysis will help you towards making the best business recommendations for optimization.

Sophistication Levels of Data Analysis

Level 1: Exploratory

Question: What can the data tell me?

Using descriptive and statistical methods with programs like R, you will clean, explore and analyze data to answer business questions and derive meaningful insights. Your goal in the exploratory phase is to develop some understanding of your data, that is represented in some form of table or model. In this phase, you should be viewing your data as a whole, and looking for some statistical insight. For example, a retail company may notice a massive influx of traffic coming from Australia. They may want to explore the causes of that traffic and how to take advantage of it.

Level 2: Predictive

Question: What can I forecast based on the data?

When you look at the history of your data, you can often predict what is going to happen in the future. Programs like SQL will allow you to write queries to pull from large databases and then visualize it into programs like Tableau. When your data is pulled into Tableau, you can spot and exploit historical trends to mitigate future risks or capitalize on opportunities. Airline services would use historical data to help correctly plan for flight schedules. They may notice a higher demand for flights to Miami mid-February. Using their historical data, they can prepare to have more planes readily available to meet needs.

Level 3: Prescriptive

Question: Based on the data, what should I do?

This part of the analysis is where decision making happens. Usually done on the managerial level, based on findings done from analysis of the previous two levels, educated guesses can be made to tap into better business practices. For example, a university may use prescriptive analytics to decide where to open a new campus, or how to allocate new funds amongst colleges. Prescriptive analysis essentially uses analytics to make choices that can influence a new outcome.

Level 4: Optimization

Question: How can I combine all of these steps to make informed business decisions?

Optimization is where all the efforts of the previous stages come together. Changes were implemented during the prescriptive phase, and now it is time to look at how the changes performed. Did they match historical data? Can they tweaked and improved even further? Perhaps they made no significant difference. Programs like SQL and Tableau would again help in querying and visualization these questions.

Data will always go on a journey when it comes to analysis. It may even go on several journeys. Finding statistical outcomes out of raw data and using it to make businesses operate better is what makes it so valuable. More and more companies are seeing the potential in data based on where others have been so successful. Companies like Spotify have used data science to help automate their recommended playlists, enhancing user experience. All of their data had to start in the exploratory phase before getting to the optimized music giant they are today.

The Journey as a Whole

These levels of sophistication answer different questions. Knowing what your task is and what tools to use to help complete that task is how your data goes on its analysis journey. Lots of analysis tools can be used to answer all of these questions. Getting a full range of different analysis tools will help set you apart to answer more than one question. The more, the better. Get the skills needed to bring yourself to the next level.

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