Perspective 6: Data Analytic – Understanding Data Types (Part 3)

Up to now in this perspective we have talked about how to Identify Data, and various techniques for analyzing data. If you are using the data to help drive a business case for a project that will render huge transformational change for the organization, you want to make sure the data aligns to that objective. In this post, as will conclude our conversation about data I would like to focus on how to leverage the different types of data analytics to analyze data.

Data Analytics

First, what is data analytics?

Data analytics is a method, or science, to analyze raw data. You can leverage data analytics to help with forecasting, continuous improvement, competitive advantage and/or price optimization, as well as adhere to fraud protection and regulatory compliance. There are also many tools you can leverage to perform data analytics, such as SAS, Tableau, SQL, Python, and more.

Let us talk briefly about the various types of data and then discuss a few examples of each.

Types of Data Analysis

• Descriptive (What) – analysis that seeks to explain what happened with variables.
• Diagnostic (Why & How) – analysis that seeks to explain “why” and “how” between a particular data set.
• Predictive (Predicts) – analysis that seeks to predict the future and what actions to take based on how variables are likely to behave.
• Prescriptive (Improve) – determines which action to take to improve a situation or solve a problem.

Examples of Data Types:

• Descriptive

  What happened with exercise equipment sales in the month of June?

  • Explanation: In this example, you are describing the data in which you want to analyze. This could be a result of reporting that shows a unusual decline in sales from previous months and there is a desire to understand why. This could also be a result of year over year trending where sales decline in June and you want to understand why.
  • Focus: You are looking at the data to describe what the data is telling you.

• Diagnostic

  Why did exercise equipment sales increase in some retail stores, but not in others in the month of June?

  • Explanation: In this example, you are trying to diagnose a specific situation, which for this example is, why sales increased in some areas and not in others.
  • Focus: You are looking at the data to determine the root cause.

• Predictive

  Can we leverage the same type of strategy across all stores that increased sales for those stores that are underperforming?

  • Explanation: In this scenario you are trying to predict what options/recommendations/solutions you could implement to get the desired result.
  • Focus: You are looking at the data to make predictions on what you can do
• Prescriptive – We are finding that the same sales strategy doesn’t work across all of our markets, what other options can we try to drive sales growth in those areas that are not seeing growth.
  • Explanation: In this scenario you are using the data to determine what options are available to get the desired result you desire.
  • Focus: You are prescribing a solution to the problem leveraging data.

Understanding data can:

• Bring clarity.
• Identify patterns and trends.
• Build stronger solutions to enhance or totally transform organizations.
• Can easily tell you what is going well and what may not be going as well.
• Gives your knowledge on questions you can ask.

Though data may be complex it is extremely powerful. Do not run away from data. Gradually approach it or jump in but take opportunities to understand the data which your organization works with.

Soon, I will be sharing my last Videocast of the year with a special guest who will discuss data in more detail. So, be sure to follow my YouTube channel and be on the lookout for that.

Until next time, signing off,
Paula Bell, The BA Martial Artist ????