Data Preparation Steps: Ultimate Guide

Data enters your business at unparalleled speeds and volumes. As exciting as that can be, it can also be the cause for overwhelm because it isn’t just ready to be used right away without the proper preparation. In fact, data preparation takes up the majority of time for any machine learning and analysis project. 

But, what if there was a way to make your data preparation steps simple, streamlined, and accurate without having to have second thoughts or time-consuming manual labor? With data preparation techniques and automation, there is. 

Let’s uncover what we are talking about. 

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What is Data Preparation?

Data preparation is the process of transforming raw data into its ready-to-use format for analysis. The key steps involved in data preparation include data collection, data cleansing, and labeling so that the information can be deemed legible by machine learning algorithms. 

Once applied within the algorithm, the data can be turned into visual information for use by any relevant party, be it stakeholders, management teams, and employees alike. 

With data flowing into every organization across different systems and every millisecond, it’s incredibly efficient and pertinent to have data preparation steps for analytics clearly defined and ready to go. 

What are the Benefits of Data Preparation?

Data preparation isn’t just beneficial, it’s necessary if you want to be able to gain value from your raw data. Raw data comes at your organization quickly and often unstructured, rendering it chaotic and potentially incomplete. 

By performing data preparation steps, you can: 

1. Improve Data Quality

Through data preparation, you’re able to fix inconsistencies, remove redundancies, and remedy errors. 

2. Maximize Value

Data preparation makes it possible to combine data from disparate sources, offering a cohesive overview that can reveal important insights about your business and its customers. 

3. Apply Analytics

With data preparation, you can initiate data analysis because the data is made to be formatted and consistent. You can save time and support standardization. 

What are the Challenges of Data Preparation?

Since data enters your systems from different sources, it can be all over the place and inconsistent. The process of having to manually sort through it all, combine data, remove redundancies, and rely on various spreadsheets to do so is time-consuming and error-prone. 

Many businesses will run into challenges when manually dealing with data, including:

1. Inconsistencies

With different systems collecting data, you’ll often run into terminology and identification variances that make it difficult to prepare data for use. 

2. Incompleteness

Insufficient data profiling can lead to data that is missing values. 

3. Lack of Skilled Personnel

To be able to properly enrich data, you need to rely on highly skilled individuals who know what to look for and do. 

4. Invalid Entries

Small typos and spelling errors, or major issues like numerical differences, can all impact analytical accuracy. 

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With finance automation software, you can forego all of these hurdles. Finance automation platforms that feature data preparation techniques remove all the stress and save time by automating data preparation steps. Your team can replace the need for spreadsheet-driven data and prevent having to manually sift, sort, and sync raw data for use.

The software will transform data, initiate analysis, and generate reports, so that your team can focus on high-level and value-add tasks, rather than mundane data cleansing and labeling. Since everything is automated, you also don’t have to risk key person dependencies or bottlenecks. 

What are Data Preparation Steps?

Whether you’re working for or running the finance team of a big, medium, or small business, data preparation steps tend to follow the same tune. 

The workflow runs somewhat like this:

1. Collect Data

The data preparation steps begin with data collection and finding the right data. This can be a very tedious step, especially if you have many data sources to consider, including software, in the cloud, data warehouses, applications, and more. 

Pulling together all this data is laborious, but with automation software, you can simply integrate and connect all your data sources and let the software do the heavy lifting. 

2. Assess and Clean Data

Once you’ve pulled together all the data, it’s time to assess how it looks. For example, do all records follow the same naming conventions and possess the right values? If not, it’s time to fix them. 

3. Validate Data

Teams will often review and validate data to ensure that it is ready for the machine learning algorithm to work with it. Data visualizations are used at this point to discover patterns and spot any anomalies.

4. Enrich Data

Data enrichment is performed to enhance the data by connecting it with relevant information for deeping insights. At this step, data transformation can take place to update formats or values into a defined outcome and/or to make it understood by a wider audience.

5. Store Data

With clean data, the options for its usability are limitless to gain insights. Most companies will choose to store the data in a third-party application, automation software, or some type of business intelligence tool to be able to transform it into insights. 

6. Document Data

It’s recommended to document and catalog data in the same system where users will be accessing it. This way, they can have a reference as to what fields mean and any other valuable tidbits of information that could explain potential skewed data analysis in the future. 

What are Self-Service Data Preparation Tools?

Data scientists spend the majority of their time preparing data rather than performing analysis. With self-service data preparation tools, they can instead spend the bulk of their days conducting analysis that can help teams to make informed business decisions. 

Self-service data preparation tools expedite and execute the data preparation steps, providing:

1. Data Accessibility

The tools prevent the need for various spreadsheets, instead collecting and securing data in cloud applications, warehouses, and data lakes, making them accessible when needed. 

2. Enrichment Functions

The software will cleanse and amplify the data, rather than needing a human to manually do so. 

3. Exporting Capabilities

Data preparations and visualizations are inherently provided through exporting capabilities to people or other applications. 

What is the Future of Data Preparation?

Data preparation isn’t just about procuring analysis. These days, with data preparation and automation software, it’s transforming how organizations function and collaborate across departments. 

Automated data preparation results in greater productivity for all, and enables an environment where IT professionals, finance teams, business users, stakeholders, and leaders can work together in the best interest of the business overall. 

How to Start Data Preparation?

To get started with data preparation, consider these pointers:

1. See the Whole Picture: Data preparation is closely linked to data analysis. In order to make the most out of your analysis, you’ll want to know what you are looking for, and then this informs what kind of data preparation will be necessary.
   

2. Set Goals: Define metrics and goals to assess your organization’s data and its usability. The goals will determine what level of accuracy is expected.
   

3. Prioritize Data Sources: When you’ve conducted step one, you’ll know what type of data you’re looking for to proceed with analysis. This will help to identify and prioritize data sources.
   
   
4. Leverage Automation and Technology: If you’re looking to save time, reduce mistakes, and maximize your team’s efficiency, then using automation tools for your data preparation steps is a no brainer. Along with defining the skill set your team members need to possess, having the right tools by their side will make all the difference for your data outcomes. 

5. Adjust as Needed: Keep an eye on how your data preparation steps are faring. If you see that there are still hiccups along the way, don’t be afraid to try something else. 

What to Look for in a Data Preparation Tool?

Adding a new tool to your business’ toolbelt requires some preparation and research. When picking the best data preparation tool, look out for one that has these features:

• Automated data management 
• Different ways to transform data 
• Top-tier security measures 
• Easy usability 
• Cloud-based for accessibility 
• Ability to integrate with existing tech stack 

With a no-code, finance automation tool, you can not only benefit from automated data preparation, but you can also streamline key finance functions, such as: reconciliation, expense management, reporting, and more. 

What are Best Practices for Data Preparation?

For any organization, these best practices for data preparation can help set you up for success to get the most from your raw data:

• Ensure your entire team understands the goal of data preparation and the analysis that follows. 
• Store the raw data, too. Don’t delete it once it has been transformed because it may be needed again. 
• Document your data preparation and analysis workflows. 
• Continuously monitor data pipelines to make sure there is consistency and accuracy. 

Wrap Up 

Even though there are many data preparation steps involved to turn raw data into usable insights, it doesn’t have to be a time-sucking, soul-crushing, and error-prone exercise. 

With automation software, data preparation is made to be streamlined, seamless, and successful. 

Businesses of all sizes are leveraging automation software to transform how processes flow. The first step is recognizing its power and benefits so that you can get started!