Data preparation in most organizations is timeintensive and repetitive, leaving precious little time for analysis. But there’s a way to get to better insights, faster.
What’s the big deal about data preparation?
The big deal is that you can’t succeed without it. And that’s not an overstatement. Data prep might not be glamorous, but it’s the structural foundation of good business analysis. If you don’t clean, validate, and consolidate your raw data the right way, you won’t be able to get meaningful answers.
But in a typical organization, data winds up living in silos, where it can’t fulfill its potential, and in spreadsheets, where it’s manipulated by hand. Silos and manual preparation processes are like a ten-mile obstacle course lying between you and the insights that should be driving your business.
If your organization is struggling with this lag time, you’re in good company, as 69% of businesses say they aren’t yet data-driven — but having other people with you in a sinking boat doesn’t make it more fun to drown. The more data you acquire and the more complex it gets, the more these problems amplify, so you need better solutions. What if you could work with any data format that struck your fancy?
What if you could automate some of these processes and make them fast, transparent, and repeatable?
What is Data Preparation?
Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data.
Data preparation is often a lengthy undertaking for data professionals or business users, but it is essential as a prerequisite to put data in context in order to turn it into insights and eliminate bias resulting from poor data quality.
A successful approach to data prep includes these functions:
- Data exploration
Discover what surprises the dataset holds.
- Data cleansing
Eliminate the dupes, errors, and irrelevancies that muddy the
- Data blending
Join multiple datasets and reveal new truths.
- Data profiling
Spot poor-quality data before it poisons your results.
- ETL (Extract-Transform-Load)
Aggregate data from diverse sources
- Data wrangling
Make data digestible for your analytical models.
Ideally, as you’re moving in and out of these activities, you want to record both your data and your process so that any mistakes you make aren’t permanent, and so that others can repeat your results on their own. Transparency and repeatability are the holy grail of data prep, but you can’t have either in a spreadsheetbased system.