Data Preparation: The Heart Of Data And Analytics

Data preparation is the foundation of any big data and analytics project. Data preparation is the process of preparing your raw data for analysis, which involves identifying the structure and format of your data. It involves identifying different types of columns in your dataset, understanding the relationships between these columns, cleaning them up to ensure they are accurate, ensuring that you have all possible relevant data available, and creating new features that might not exist naturally (such as information about countries).

What is data preparation?

Data preparation is the process of preparing data for analysis. It’s the first step in any data science project and it often involves cleaning, transforming and standardizing your raw data so that it can be analyzed with ease.

Data preparation is often thought of as a separate phase from machine learning or analysis, but in reality these three steps are intimately linked together: you can’t build accurate models if your input isn’t clean; similarly, if there’s too much noise in your data then even the most sophisticated algorithms won’t be able to produce useful results (e.g., they might learn that “green” means “red”).

Why is data preparation important?

Data preparation is the first step in the analytics process. It is the heart of data and analytics, as well as its foundation for all other steps. While you may have heard about data cleaning, data wrangling and ETL (extract-transform-load) processes, they’re all part of a larger whole: data preparation.

In fact, if you want to get an idea of how important this step is consider that one study found that 70{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} of companies spend more than half their budget on preparing their data before they even start doing any analysis or modeling with it!

Data Preparation Challenges

Data preparation is a critical component of any big data project. However, it can also be the biggest obstacle to success.

Data preparation challenges often arise from a lack of understanding, skills and tools needed for effective data curation. Data preparation takes time to complete, but it’s important that you get it right before moving on to other steps in your analytics journey.

Example of a data preparation challenge

Data preparation is the heart of data and analytics. It’s where you transform your raw data into something that can be used for analysis.

If you’re not sure what I mean by this, imagine having a box full of rocks that need to be sorted into different categories based on their color and shape. You could use your eyes alone to do this task–allowing you to quickly pick out any red or round rocks–or you could use a machine learning algorithm (e.g., machine vision) trained on thousands or millions of examples from previous runs of this process so it knows how each type looks like in practice, allowing it to pick out more subtle patterns in rock colors than humans would notice on their own (e.g., “this green one has an orange stripe”). This last option would take much longer but give higher accuracy results because we’re using technology instead human-based pattern recognition skills which are limited by our ability at pattern matching!

How to prepare data for analytics?

Data preparation is a vital step in the data analytics process that often gets overlooked. This can lead to poor results and inaccurate insights, which can make it difficult for you to make informed decisions. Fortunately, there are several ways you can prepare your data so it’s ready for analysis:

  • Use a tool like [Tableau Prep]( or [Alteryx]( to cleanse and scrub your data, removing duplicate values and ensuring that all of your columns are properly labeled (for example “Sex” instead of just “M” or “F”). You should also check each column’s type (integer versus string) as well as its range of values–if possible, try not to use any nulls or empty strings in certain fields because these might cause problems later on when trying out different statistical methods on those variables!
  • Adding metadata such as descriptions about what each field represents will help future analysts understand exactly how each piece contributes toward answering their questions.”

Data preparation is essential to the success of any big data project.

Data preparation is the “heart” of data and analytics. It’s a critical part of the big data pipeline, which you can think of as a series of steps that take raw data and transform it into something useful.

Data preparation involves cleaning up your data so that it’s ready for analysis. This might include things like:

  • Filtering out irrelevant records or fields
  • Combining multiple sources into one file (consolidating)
  • Reducing duplicate entries (de-duplicating)
  • Converting from one format to another (e.g., converting numbers into text)


In summary, data preparation is the key to unlocking the value of your analytics. It can be a complex and time-consuming process, but it’s worth it in the end. If you want to improve your business decisions by using data from multiple sources, then make sure that those sources are ready for analytics by following these steps: