Data Preparation

Even the most comprehensive collection of data is useless to you if your users can’t leverage it properly. Data preparation is an umbrella term for all the techniques and processes your data managers undergo to clean up data for easy use.

It facilitates data analysis, removes any errors or flaws in the dataset, and generally makes data processing much less of a headache. Preparing data is also an essential step for machine learning training programs.

What Is Data Preparation For Machine Learning?

The raw data you directly receive from your sources is almost never perfect. It may include redundant, irrelevant, or erroneous entries, and not all of it will be in a consistent format if you have multiple data sources. When you prepare data, you are transforming it into a state that makes the jobs of your machine learning developers easier.

Data preparation might also include generating metadata to put the information into context. The result is that an algorithm can generate predictions more readily using it.

Why Do Businesses Go Through the Trouble of Data Preparation?

Data preparation is usually a time-consuming task, so why do businesses go through the trouble? The procedure is vital to developing impactful ML models for several reasons.

  • It catches errors before processing. Once you combine data from multiple sources together, it loses its context, and ironing out errors becomes more challenging. Data preparation fixes these errors when they’re the most obvious and before they cause issues.
  • It’s part of an overall data management strategy. High-quality data not only prevents later headaches but also improves your data analysis efforts.
  • It improves your business decision-making. Prepared data allows a machine learning algorithm to return more accurate predictions and learn more quickly, ensuring that your company benefits from it.

Data preparation overall improves the return on investment your organization receives from data intelligence initiatives and machine learning development cycles.

A Rundown of Data Preparation Steps and Procedures

Data preparation can be a complicated matter, especially if you have a lot of raw data to go through.

Starting with the Prerequisites

Start by figuring out the goals you want to achieve with your data. What level of accuracy is sufficient to generate a reliable model? What quality metrics do data managers need to look for to keep project timeframes reasonable? Start by planning out your data strategy before you start combining datasets.

Don’t forget to keep track of costs. How much time and money would it take to process all that data and store it for use? If you use any data preparation tools, keep track of licenses and check whether they fit into the company budget.

Anticipate quality issues regarding your data early on. What should a data preparer do in response to erroneous entries? What tools and technologies should they use to make their efforts more comprehensive?

The Data Preparation Workflow

When it’s time to start preparing data, the general workflow follows.

  • Identify. Gather all your data sources and their related repositories.
  • Ingest. Bring all those data sources into one dataset. Keep in mind that formats must be consistent, as data can come from a variety of structured and unstructured sets.
  • Cleanse. The most time-consuming part of data preparation involves removing outliers and extraneous points, filling in missing values, and conforming everything to a consistent format for easy processing.
  • Validate. At the end of cleansing, go through the dataset and ensure errors don’t end up in the training sets for your ML algorithms.
  • Enrich. Add any additional information necessary, such as metadata for providing context into each data point.

Data Preparation Techniques

Data preparation also involves several techniques that make data more digestible for training, validation, and testing purposes.

  • Cleaning, correcting errors before they compromise on model performance and reliability.
  • Transformation, converting data from multiple sources into a consistent format.
  • Feature selection, identifying what parts of the dataset are most relevant for what you want your ML model to achieve. It also requires developers to choose and derive variables from the dataset.

Feature selection also includes dimensionality reduction, the process of converting high-dimension features into lower-dimension ones.

Errors To Look For in Data Preparation

Because cleaning is the most time-consuming part of preparation, make sure your data managers are looking for the right flaws to weed out.

  • Missing data is always a threat since raw datasets are rarely complete. Empty cells and NULL values require resolving.
  • Anomalies and outliers can easily confuse a machine learning algorithm, so visualize the data and take them out of the training set.
  • Feature engineering increases what a model can “learn” from a dataset. Keep model performance up by combining data from multiple relevant sources and enriching it sufficiently.

Data preparation sometimes requires collaboration from subject matter experts. For instance, a model that analyzes company financials needs input from employees in the finance department, who can provide needed context into how the model should use the information.

Current Challenges Facing Data Preparation Efforts

On top of the usual erroneous data points, data preparation often runs into several roadblocks for identifying and addressing quality issues. And deciding how to rework datasets can be just as time-consuming. For instance,

  • Invalid data includes information that’s not necessarily inaccurate but can contain minor errors like typos and incorrect formatting.
  • Inconsistencies arise when data from multiple sources combines together. The type of marketing information you might find in a marketing department may be different from the same set you get from the financial department because of differences in terminology both groups use.
  • Enriching data demands complicated decision-making and analytical skills to ensure you’re boosting the value of the dataset rather than diverting it.
  • Generating contextual information like metadata can be a challenge when data points come from different sources.

Process standardization is another consideration after the project ends. Standardization ensures data preparation can occur again later if you ever need to train another machine learning model.

How Modern Tools Facilitate Data Preparation

The importance of comprehensive, quality data is in such high demand that various business tools have arisen to help with data preparation initiatives. These tools can:

  • Improve scalability of data preparation efforts. For instance, preparing data in the cloud accelerates data gathering and analysis. It also puts all datasets into a single, consistent environment where you don’t have to worry about any set’s underlying infrastructure.
  • Create a faster time-to-value. ML developers who can trust their data can create more impactful models more quickly, resulting in faster benefits for the business.
  • Help teams stay on top of things. Whenever data sources or project requirements change, data managers can stay ahead of the curve and improve their workflows quickly.

Remember that data is at the heart of machine learning development. The most informed algorithms make the best business decisions, so maximize your investment by preparing data for use, both for ML developers and data analysts alike.