What you need to know about Data Preparation
Despite how important big data and machine learning have become to business processes, the fact still holds: You only get as good as you give. And it’s only with adequate data preparation that you get the good stuff.
The entire purpose of harnessing data available to companies is to create descriptive models that can transform that data into valuable insights. But a critical part of that machine learning process is the point of gathering, cleaning and transforming the data.
Faulty data begets faulty insights and there’s no remedy for that. In order for data analysts, data scientists and business users to avoid this, the place of data preparation in the analytics process must be given appropriate attention.
But what exactly is data preparation and why is it such an important part of the AI revolution that is turning our world on its head? Here’s what you need to know.
What is data preparation?
Data preparation is exactly what it sounds like. Also called data wrangling, it’s everything that is concerned with the process of getting your data in good shape for analysis. It’s a critical part of the machine learning process.
Data preparation is historically tedious. It’s one part of the job that a majority of data analysts and scientists agree is not so pretty. According to Forbes, up to 76% of data scientists say that data preparation is the worst part of their job.
If data scientists dislike the task of data wrangling so much, then why do they bother to put up with it?
They put up with it because if they don’t, they’ll have wasted all the time it took to create and gather the data only to find highly misleading results with massive holes in them.
If data could be analyzed immediately in exactly the same form that it arrives, there would be no need for data preparation. But that’s usually far from the case. According to Gartner, more than 80% of the data we have in the world today is unstructured. This means that the data is neither neatly tagged, categorized nor sanitized.
There are no “nice” data sets. Most often, they won’t be well-populated with accurate, relevant information in all the correct fields. You would usually have to spend weeks or even months getting the data into a useable format before you can reap useful insights from it.
Effort must be made to ensure that the user is not saddled with data that has several invalid, out of range or missing values that will then go on to produce poor results. Only high quality data can guarantee high quality insights and there’s no replacement for that.
Why is it important?
There’s no escaping the fact that good business decisions are a natural outcome of good data. But there’s a tendency to ignore the grind of data wrangling and focus more on the romance of creating predictive models that will change the world.
That’s why a majority of scientists will rather do without it. However, companies that prioritize good data processing firmly place themselves in the best position to benefit significantly from high quality insights.
Companies benefit the most from high quality, agile data. They need to be properly equipped with the best raw materials for modern business intelligence applications that allow them to gain a competitive advantage in today’s business world.
And clearly, the number of companies that recognize this is increasing steadily. In a study of 695 business intelligence professionals, BARC found that almost 70% of respondents reported that they have adapted intensive data preparation to their process.
The study also found that all respondents that had spent time on their data preparation process reaped significant benefits, over and above their initial expectations.
How does it work?
The specifics of the data preparation process vary across industries and users. Although, the framework is substantially the same. It forms a subset of the wider data mining process. The steps involved generally cover the following:
- Gathering the data: The whole thing starts with finding the right data. This can be obtained from either an existing data catalog or other sources.
- Discovering and assessing the data: The process of discovering the data involves getting to know the data and understanding what must be done to make it useful.
- Cleansing and validating the data: After determining what must be done to make the data useful, the next step is to clean it up. This is easily the hardest and most time consuming part of the process. And it only gets harder the more muddled the data is. Validation will consist of testing the data for errors up to that point, so they can be resolved before executing the next step.
- Transforming data sets into usable formats: Most often, data arrives in a format far from what is needed considering the outcomes that are sought. You may receive the data as video or images when the outcome must be in text. So it is at this point that the data is transformed into the format needed for analysis.
- Enriching the data: This is one point that business users can rely on to improve the quality of insights they will gain from the data. Enriching consists of connecting the data with other related information/sources that will add depth and substance to the data.
- Storing the refined data: After processing, the data is now ready to be stored or used immediately for machine learning.
How RapidMiner improves data preparation
As I’m sure you can imagine from the information outlined above, data preparation is pretty time consuming. Research shows that it takes close to 80% of the overall time spent on the entire machine learning process. Also, considering the data explosion happening around the world and the rising relevance of big data, data preparation can become resource-intensive.
To tackle the difficulties that companies and analysts face, RapidMiner created Turbo Prep to ensure that data preparation isn’t just fast and easy, but also fun and affordable.
RapidMiner Turbo Prep makes it easy to get data ready for predictive modeling. Interactively explore data to evaluate its health, completeness, and quality. Quickly fix common issues like missing values and outliers. Blend multiple datasets together and create new columns using a simple expression editor. When the data is finally ready, create predictive models using RapidMiner Studio and RapidMiner Auto Model or just export it to popular business applications like Excel.