Clean data is vital for big data business efforts, leading to strong analytics, business intelligence insights and other value-additive information. Over time, data quality can rapidly fall off as different users or teams fill out fields, automated data gathering collects all manner of content, and files are merged or, worse, forgotten. Cleaning it up through data preparation, or data wrangling, is essential to help you make the best quality decisions.
What is data preparation?
Wherever your data comes from, however it’s acquired or created, it’s unlikely to be up to the standard necessary to deliver the best insights for modern big data projects. Data preparation is the traditionally troublesome and unloved task of cleaning that data up by IT, business intelligence or data specialists before it enters data lakes or warehouses.
Fortunately, modern data preparation is largely automated through a standard set of processes, usually data cleansing, data structuring and data transformation to beat it into shape. These tasks ensure that data is presented in a valid format, weeding out low-quality data that will cause errors or duplicate records and compiling data from multiple sources to generate new value.
From customer data that helps generate new product ideas, the overview of millions of health records used in key budgeting decisions to terabytes of smart factory data that can be used to improve processes and production, data preparation is key to any big data decisions.
Why do you need data preparation?
As more businesses move to big data management projects, look to use data for machine learning tasks or share it with partners, they need that data to be accurate and timely. Without data preparation efforts, the results might be unreliable, and it will cost more time and investment to fix any errors that are found.
Even if the business has no immediate plans for its data, cleaning it up through data preparation now will ensure typical business data efforts are easier to scale, help teams collaborate and when those big data plans do launch, you’ll be ahead of the curve for any projects.
3 benefits of data preparation
Since data preparation is roundly hated by IT and data administrators, doing it well and early reduces the friction and aggravation for team members. The longer you leave it, the more time and effort it will take and the more complex the task.
Data preparation also helps reduce data management and analytics costs, which will only ever grow as business data management tasks become more complex and scale up. In addition, adding data preparation to your IT team’s list of skills makes them more valuable and creates transferable skills.
Finally, data preparation helps the data team and business leaders make better decisions, adding a big win for IT’s contributions to generating business value, rather than the typical keeping the lights on tasks.
The biggest data preparation challenges
Siloed and ad hoc data sources are problematic for data preparation tasks as the users and IT need to know when they can pull the data, and what will happen with any newly-added records. Data transformation projects might also overlap with preparation creating further complexities, and new data sources may be created at any time.
Fixing the poor data is the largest challenge for those involved in data preparation. Knowing what to fix and (how to fix it the right way) across missing data and maintaining data quality all require careful consideration.
Data management is a specialist skill and many IT teams may lack the expertise or budget to bring it on board. Innate knowledge and skills can help avoid or quickly solve some of the inevitable issues, so ensuring the right knowledge in place beforehand can be a challenge.
Meeting these challenges creates a more rational and organized business environment, making any future evolution for services, data or applications easier to manage.
5 steps to successful data preparation
Follow these steps or search for further advice to understand your needs and requirements when it comes to cleaning data and doing great things with it.
1. Collect data
Data can come from across the business and a data preparation exercise, pilot or full project can take on some or all of that data. The data can be extracted all at once or added over time to the project, but the business needs to understand how it can add the latest data for analysis.
2. Data discovery
Within each database, there will be a world of quirks and bumps that need to be ironed out before the data is ready for use. These can be identified during the discovery phase and the correct or best version of any changes approved.
3. Data validation
Automated services via a range of third-party or in-built tools will help with the cleaning and validation process. They simplify and automate the cleaning process, speeding up the task for IT.
4. Data transformation
Once the data is clean and suitable for moving to the next step, it can be transformed to an appropriate format. Data can also be enriched by linking it to other sources, adding value and further insights.
5. Data export/application
Finally, you can access the fresh data in your new application and ensure that it meets technical and business requirements. If there are any issues, these can be addressed before end users can access the live information and start doing big data analytics or running machine learning tasks.
If your business has many disparate data sources, there’s bound to be greater knowledge available by linking them together. Consider customer data and your transaction processing systems, or the content of user browsing activity and their feedback or social media posts. Business usage of services and apps compared to browser-based activity are all areas where gaining useful insights can unlock business potential.