Why Do You Need Data Preparation and How to Make It Simple

High quality and accurate data is essential to every business. Useful data allows you to make smarter, more informed decisions that make a difference. There's no point in spending time and money analyzing bad or unreliable data. If you're not putting your data through a preparation and transformation process, you're likely compromising your vision.

Handling large amounts of data has always been seen as one of the more complex challenges a business faces. In most cases, you either need specific employee skills or software. But in reality, your data preparation process doesn't need to be complicated. In this article, we'll talk about why data preparation is so critical, the steps involved, and how to make it as simple as possible.

What Is Data Preparation?

Data preparation is the act of aggregating raw data and transforming it into a format that can be easily analyzed. It ensures you're collecting and transforming data into a format that is complete, accurate, and reliable. The raw data can come from multiple sources, and be in any format. The goal is always to produce a clean set of data for accurate reporting.

An example of a data preparation task might be combining statistics and figures from multiple sources to analyze as a whole. Perhaps you want to pull data from your CRM to see sales figures from more than one region, product, and so on. Or, maybe you want to compile employee data from different sources to build a more extensive database of information.

To prepare the data, a data preparation tool can clean and combine the data sources automatically. These types of tools eliminate inconsistencies, aggregate the data into one unified format, flag errors, and much more.

On a small scale, data preparation tools can make dealing with big data less painful. On a broader scale, they are capable of handling and preparing more data than is possible with a manual method.

The Steps Involved in Data Preparation

There are five critical steps in the data preparation process—accessing, discovering, cleaning, transforming, and storing the data. Here's a look at each one.

Accessing the Data

The data preparation process starts by accessing the data you want to use. This can be done in many ways and from several different sources. You can do it using bits of code, through a manual process, or by using an ETL (Extract, Transform, and Load) tool. When sourcing data from multiple places and capturing different types of data, software can make the process much more manageable.

Discovering the Data

The next step is to discover the data, which is when you decide what you ultimately want to do with the information. It's important to understand what you want to know before you start working with sets of raw data.‍

Cleaning the Data

The third step is one of the most crucial and time-consuming steps in the process—cleaning up the data. Data cleansing is the process of arranging all the data sets into one format, and eliminating any data you don't need. Some of the common examples of cleaning data include:

Identifying and standardizing special characters
Identifying inconsistencies in data and making amendments
Flagging sensitive or private data
Completing missing data and values
Identifying and removing bad data

The goal of this step is to take several sources, such as spreadsheets, PDFs, databases, etc., and combine them into one comprehensive, clean set of data. Depending on the system you use, there will be some validation checks during this stage, which enables you to resolve errors and issues before moving forward.

Transforming and Enriching the Data

With a clean set of data to work with, the next stage is to transform and enrich the data, so it's in a format that can be easily analyzed. This is commonly known as the "Data Transformation" stage. It means you're taking one type of data and transforming it into the format you want or need, so it can be easily understood and analyzed.

Storing the Data

Finally, you'll want to store the data somewhere safe and secure. From there, the data can be extracted and analyzed. The most common place to store data is on a third-party application known as a data warehouse.

Why Data Preparation Is Important

Having accurate and easy-to-use data helps businesses make better decisions that accelerate growth and drive revenue. Businesses rely on data in many forms to provide valuable insights into how their business is performing, to make forecasts for the future, report financials to shareholders, and so on. If you're dealing with multiple sources of data (as most companies are), it's necessary to prepare and transform the data before you analyze it.

How You Can Benefit From Data Preparation

Some of the benefits of using data preparation include:

Efficient Data Compilation

Anytime you can implement more efficient processes into your business, you're taking a step in the right direction. If you're doing manual data compilation and preparation, using a data preparation tool will make those processes faster. Time is money, so bringing in data preparation tools to speed up your data projects will have an impact on your bottom line.

Identifying and Fixing Errors

As we mentioned in the data preparation section above, there are typically data validation checks during data preparation. This an essential part of the process to ensure you have accurate data. When you use tools and software, you can easily identify any errors during the data preparation stage. You can set rules or make changes while the data is being cleaned and formatted. In the end, you get verified, accurate, ready-to-use data.

Make Global Changes Easily

One of the main issues when dealing with multiple sources of data is that you can't make global changes across the various data sets. However, you can make global changes in the data preparation process.

Generating Accurate Information

The end goal of data preparation is to have accurate data to analyze. The better the data, the better the decisions a business can make. One of the main reasons to use data preparation software is for the peace of mind in knowing you're eliminating human error as long as you use the software correctly.

How to Simplify Data Preparation

If you've tried to undertake any big data tasks manually, or with software that wasn't able to handle the load, then you know how difficult it can be. However, there are a few ways you can make data preparation easier. The best, most cost-effective, and quickest way is by using tools and software designed to handle data preparation. These are known as ETL (Extract, Transform, Load) tools, and you should consider using them in your business.

What is an ETL Tool?

ETL tools are a type of software designed to aggregate, transform, and load your data automatically. They allow you to pull data from any number of different sources, prepare it in the format you want, and load it into a data warehouse for storage and analysis.

Improvado is an example of an ETL tool, which automatically aggregates your marketing data from sources like Facebook ads, Google Adwords, CRM tools, marketing automation software, and more. In fact, Improvado has over 500 integrations which can sync your data in minutes. You can also create custom dashboards to analyze and visualize the data that is most important to your business.

Conclusion

If you want to use data to make smarter business decisions, using a data preparation tool can help. Every business is dealing with scare resources and limited budget, so eliminating manual processes and investing in a robust software program is a great option. There are multiple data preparation tools on the market, so you can check out our list of the top ETL software tools, or schedule a free demo to see how Improvado can help you make the most of your data.