The data preparation process is an integral part of using data to generate business intelligence. Good data preparation is vital to getting results, as it helps eliminate errors in analysis, makes the analysis process more efficient, and makes prepared data more accessible to all stakeholders. It’s crucial to understand all the component parts of preparing data and to select the right tools for data preparation to ensure that this crucial step gets accomplished without a hitch.
Why? Today’s businesses depend on data analysis to generate actionable business intelligence. This can then be used for a variety of purposes, including crafting business plans and strategies, improving customer relations, and fine-tuning products and services. While collecting data is a pivotal part of the process, it’s crucial to prepare the data the right way beforehand, so it can be directly fed into data analysis and BI tools to commence the analysis process.
Let us take a deep dive into the data preparation process, look at some of the important variables, and pay attention to some fundamental things to keep in mind.
The Basics of Data Preparation
The process of data preparation entails cleaning, organizing, and transforming raw data before it can be sent for processing and eventual analysis.
Depending on the kind and volume of data you are working with, this can involve combining multiple sets of data into larger, more integrated volumes, making corrections to the data wherever needed, and reformatting the data to make it easier for processing and analysis tools to access it later.
Data preparation can be a long, tedious process. However, it cannot be overlooked, as it has a direct impact on the integrity of the results of data analysis.It is a crucial first step if you want to preserve high standards of data quality , eliminate any kind of data bias, and properly contextualize the data so that it can be turned into valuable business insight.
The process of data preparation, in addition to standardizing the data format, also enriches the source data or removes fringe data elements to enhance data quality. If you want quality results from your analysis and BI tools, any errors, missing values, and other inaccuracies need to be put to bed during the data preparation process.
The final step of data preparation is to store the processed data into the right data repository, which can be a data lake, a data warehouse, or a NoSQL style database.
The preparation work itself is usually tackled by IT teams, data management teams, and BI teams. However, data analysts can also make use of self-service data prep tools to prepare the data by themselves. In such cases, specific data sets can also be curated for self-service BI tools for analysts and other users.
What are the main processes of data preparation?
The data preparation process includes several stages, serving important purposes when it comes to cleaning, preparing, and categorizing the data for future use. While there might be subtle variations in these stages, depending on the use case and the type of data involved, the basic stages are generally the same. Here’s a quick overview.
This is the initial stage of the process where data is gathered and brought to a central location from disparate sources. This can include data lakes and warehouses, operational systems, and other sources of data. This stage is also a great time for data analysts and BI team members to take a first look at the data and to decide whether it is an overall good fit for the application that it is destined for. Great care needs to go into this step as it’s easy to assume that data from a trusted source will always be quality data, but that is not always the case.
Discovery and Profiling
This crucial stage involves taking a detailed and in-depth look at the collected data to develop a clear, concise understanding of its contents. This understanding helps data teams create a concrete plan regarding how exactly to prepare the data for its intended use. At this stage, data teams will also invest time and resources in data profiling – a process that identifies several important data qualities and characteristics that can further help with the preparation process.
During profiling, it is standard practice to look for common attributes, similarities, and patterns so that data can be categorized along these lines. Data profilers will also look for missing data, inconsistencies in the data, and anomalies so that these problems can be resolved before the next stage.
During data cleaning, the errors and inconsistencies identified in the earlier stage are sorted out. Once these are resolved, the data can be used to create data sets that are accurate, reliable, and complete. This involves fixing or altogether removing faulty data, harmonizing data entries that are inconsistent, and filling in any missing values or fields.
Structuring and Data Wrangling
In this stage, the data is structured, organized, and modeled, so that it can be contained into a common, unified format that makes it easy to access and process later on in the data analysis chain. It is at this stage that a process called data wrangling or data munging is carried out. While data cleaning has directly to do with the content of the data, data wrangling is concerned more about the format of the data itself.
Data wrangling involves a series of processes that serve to transform the raw data into formats that are usually used in data analysis. This can involve combining several data sets into one integrated data set. Usually, the process entails enrichment or augmentation of data as well as data validation so that the final results can be published or stored using a universal format that BI or data analysis tools can easily understand and digest.
Validation and publishing are the final steps of the data preparation process. Usually, automated routines are used to validate the data against existing sources or models. These routines measure important metrics like completeness, accuracy, and consistency. This data is then finally published into the favored data repository for further use.
Why is data preparation important?
The importance of data preparation cannot be overstated. While data scientists might often have mixed feelings about spending large amounts of time locating and preparing data, the upside of creating a comprehensive and exhaustive data preparation process can actually save time and effort in the long run.
With dedicated teams managing data preparation or the use of self-service data preparation platforms, the time taken to accomplish data preparation can be significantly cut down without sacrificing data quality and integrity.
What are the benefits of data preparation?
There are several key benefits of comprehensive data preparation, both short and long term:
- Data Quality
Data used for machine learning, BI, predictive analysis, and other kinds of analytical applications needs to be of the highest quality to produce results that are reliable and actionable. Data preparation gives you the best chance of preserving and enhancing data quality to strive towards quality results.
- Data Flexibility
Once the data is prepared, it can be used for multiple applications. This means that you can avoid the repeated effort that goes towards the same data.
- Data Correction
It allows you to identify and correct issues with the data that might not have been apparent otherwise.
- Data Sharing
You can give more users access to better quality data, which usually makes way for more informed and insightful business decisions.
- Data Cost Savings
It provides a cost-effective and highly efficient way to process and prepare the data for analysis.
- Maximum ROI
Most importantly, solid data preparation is a great way to ensure the maximum possible value and ROI from your BI and analytics efforts.
It’s easy to see why and how the data preparation process is crucial for any data analysis workload. The more time and effort you spend in refining, automating, and bolstering data preparation for your data analysis and BI efforts, the better and more actionable results you can get. If your organization relies heavily on the quality and authenticity of BI and data analysis efforts, then effective data preparation is the best way you can keep the quality of your insight high and your data reliable and consistent