Data Mastery: A Guide to Successful Data Management and Analysis

Data Mastery: A Guide to Successful Data Management and Analysis


We are surrounded by data. In fact, the amount of data in the world has been growing at an exponential rate since the mid-1990s. According to IBM’s 2020 Vision Study, 90 percent of all the data in existence today was created in just the past two years

Data Mastery: A Guide to Successful Data Management and Analysis.


Data mastery is a way of thinking that allows you to find meaningful patterns in any dataset by following six steps:

  • Understand the problem.
  • Collect and organize your data.
  • Transform your data into more useful forms, such as a table or graph. * Analyze that transformed data to find interesting relationships between variables, groups of people or things (e.g., cities) and so on. * Make predictions based on these relationships–for example, how much money will customers spend if they buy this product? Or what percentage chance do we have of getting rain on Saturday afternoon?

1. Define the problem

  • Why do you need to define the problem?
  • What is data mastery?
  • What is the difference between data science and data mastery?
  • Why is it important to define the problem before you start?

2. Isolate the data

Once you’ve imported your data into a spreadsheet, it’s time to isolate the data.

Isolating your data means separating the information that you need from all of the other information in your spreadsheet. This process can be difficult because there are so many different types of information in one place and they are often jumbled together. The goal of this step is to make sure that all of the relevant information is in one place before analyzing it further or using it for reporting purposes. There are three main ways to isolate your dataset:

  • Importing only specific columns or rows into new spreadsheets (e.g., importing column A into one spreadsheet while leaving columns B-F untouched)
  • Creating new sheets within an existing workbook and then copying over only certain cells (e.g., creating a new sheet called “Data Set 1” where we copy cells A1-B3 onto)

3. Evaluate the data

Once you’ve collected, organized and cleaned your data, it’s time to evaluate it. This step is crucial because it helps you determine whether the data has any value at all.

Evaluating involves understanding how to use the data in an effective way–and this can be as simple as checking whether there are any missing pieces of information or errors in spelling or formatting (such as an incorrect date). It also involves interpreting what’s there: Are these numbers high enough? Should I be looking for trends? What does this mean for my business?

As part of evaluating your data set, make sure that the information itself checks out by looking for patterns among different variables (e.g., age ranges versus gender) within each category so that nothing seems out-of-place; if some numbers seem abnormally high compared with others in one category, consider why this might be true before moving forward with further analysis!

4. Understand the data

Data understanding is the first step in any data analysis. You need to understand what kind of data you have, what it can tell you and what it can’t.

Data has strengths and weaknesses just like people do, so when we say “understand the data” we mean:

  • Understand its strengths (what does this particular dataset have that makes it useful?)
  • Understand its weaknesses (how accurate or relevant is this information?)
  • What other sources of information are available? How much more could be learned if there were more complete sets?

5. Collect and prepare your data for analysis

Now that we’ve covered the basics of data management, it’s time to get down to the nitty-gritty. In this section, we’ll look at how you can prepare your data for analysis.

Data preparation is crucial to any successful analysis project. It involves cleaning and transforming your raw data so that it’s ready for analysis by machine learning algorithms, which means removing any noise or other anomalies from the dataset, as well as converting them into usable formats (e.g., csv files). This process can be broken down into two steps:

  • Cleaning – Removing unwanted information (such as typos) from records in order to make sure each record contains only valid values; also known as “data scrubbing.”
  • Transforming – Converting various types of variables into more convenient formats before feeding them into an algorithm or modeling tool such as RStudio

6. Analyze and interpret your results

After the data has been collected and analyzed, it’s time to interpret your results. This is where you’ll summarize what you found and make recommendations based on those findings. You may also want to provide a way for others to test your results by sharing the code or making it publicly available (e.g., on GitHub).

It’s important that you don’t just stop there–you should also include an appendix with any assumptions made during analysis, as well as any limitations in scope or scale that might impact how useful this information is going forward.

Data mastery is a way of thinking that allows us to find meaningful patterns in any dataset by following six steps

Data mastery is a way of thinking that allows us to find meaningful patterns in any dataset by following six steps:

  • Define the problem. What do you want to know? Do you want to understand how many people are using your product, or how they’re using it?
  • Collect the data. Where does your company’s data live? How can it be accessed and processed by machine learning algorithms?
  • Organize and cleanse it so that it’s ready for analysis (this part is often done by IT professionals). This step involves making sure all of your data points are complete and consistent (e.g., all emails have valid email addresses), which helps avoid errors later on when analyzing them with machine learning algorithms or other tools–but even if this step isn’t necessary for every project, it’s important not just because errors make results harder to interpret but also because incomplete or inconsistent datasets may not contain enough information about what we want our systems’ outputs (i.e., predictions)


Data mastery is a way of thinking that allows us to find meaningful patterns in any dataset by following six steps.