What is Data Wrangling, Why it’s Important and How You Can Speed up the Process
Talk to any data scientist and they will tell agree the first challenge of putting data to work is getting it into a structured format.
This structured format lets you analyze, interpret and make decisions around your data. This process is called data wrangling, sometimes referred to as data munging. Data wrangling is the process of converting and mapping data from one “raw” data form into another format. This is undertaken with the intent of making the data more appropriate and meaningful for a variety of downstream purposes, like exploration analysis and machine learning.
What makes data wrangling so important?
Data wrangling identifies the most valuable information within the data, given the parameters and goals of the individual business. Skipping any of the important data wrangling steps will mean that your data may not be usable for any of your downstream purposes, or your models may be inaccurate. Both of these outcomes are detrimental to the ROI of your data science and machine learning projects.
What is the problem with data wrangling?
Data wrangling is time-consuming and often tedious. Instead of spending time understanding your data, you are spending time pulling it into a usable format. Often, this data preparation step creates bottle-necks in data-driven projects. Moreover, with new incoming data, you have to repeat the same set of wrangling actions again and given data changes, making the actions reproducible and repeatable is often hard! To remain competitive, businesses need to compare and analyze often disparate data sources and build a repeatable wrangling process fast. So the ability to out-wrangle the competition proves a significant competitive edge. This is where we can help with The AI & Analytics Engine’s Smart Data Preparation feature, read on to find out more.
Why is wrangling an important part of machine learning?
Preparing the data so it is in an optimal state for machine learning is a very iterative process, of which data wrangling is a critical part, the flow could look like this:
- An analyst will generally begin with a smaller set of available data that needs to be wrangled, and will aggregate a dataset with a “target variable” — the outcome to be predicted.
- The analyst will typically do the initial data exploration and then build the first model.
- Often the first model will not be good enough, which means additional data sources or transformations may be required to improve the model.
- The analysts will then need to source new data to wrangle with the original dataset and build and evaluate new models again.
Within a single project, there could be plenty of iterations. Often data science projects fail because it takes too long to iterate, so it is critical to adopt a fail-fast method and reduce the iteration time. A critical success factor is the ability of a data science team to accelerate data wrangling steps and integrate them with a machine learning framework. This improves the velocity of results and as such ability for innovation and usability of (timely) insights.
What are the steps in data wrangling?
Once the analyst has determined the information that is most important, as well as the relevant focus areas given the business needs. There are six steps to the wrangling process, keep in mind that these activities do not always proceed directly one following the other. Instead, one step like enriching data often generates more ideas for cleansing it:
- Discovery: What’s in your data? What do you want to get out of it? What might be the best approach for a productive analytic exploration? These are the key questions to ask during the discovery phase and should include fact-checking, understanding where data originated and when it was last updated or verified.
- Structuring: Data is abound in all types shapes and sizes. Often — there will not be any structure to it! This needs to be fixed. Data should be restructured in a manner that best suits the analytical method used. Structuring helps when you understand the outcome of the first step- whatever needs to be done for better analysis.
- Cleansing: Often datasets have outliers, which can skew results. Null values need to be changed and formatting will need to be standardized. Read more about data cleaning here.
- Enriching: There may be undiscovered gold in your data. This could be the relationship between pieces of data, or where the data originated from. Take stock of what is in the data and determine whether you should augment it using additional data to make it better, or whether you can derive any “new” data from the relationships existing in the clean data set you already have on hand.
- Validating: Data must be verified to evaluate any data quality, security and consistency issues and to make sure that any issues are/have been addressed by the applied transformations.
- Exporting: The final step is to prepare the wrangled data for a specific use.
Do you need to be a machine learning engineer or data scientist to wrangle data?
Historically, machine learning projects are owned and deployed by data scientists, with engineers required to build systems that store and process data and integrate models into applications, however modern technologies, like The AI & Analytics Engine are reducing the barrier to entry. These technologies enable business analysts and other non-expert users to wrangle data and even develop and deploy machine learning models. It is important for businesses to consider if waiting for the resources to afford a data scientist, or a team of data scientists will mean lost opportunities to competitors who decided to dive in and begin experimenting with these low code and no code technologies.
There are some important questions to consider when deciding on data wrangling technologies geared toward business users:
- Can the technology integrate data from various data sources?
- Are there visual displays to understand the contents of data and guide the right transformations and is the wrangling process intuitive with limited if any coding required?
- Does the technology allow for reusable data transformation pipelines?
- Can the wrangled data integrate into a machine learning framework, to build the models and iterate fast, in an organized and easy to understand project?
The AI & Analytics Engine, can provides an intuitive graphical user interface for all types of business users and includes automation, and a flexible and transparent project environment to clean and wrangle data, and quickly iterate modelling for optimal results within a single pipeline.
Interestingly, an intuitive and guided data preparation feature integrated into a machine learning pipeline benefits the more seasoned data expert too. In effect supercharging the ability of the expert by reducing manual handling and data prepping time, giving back bandwidth for more in-depth analysis and problem-solving.
By leveraging technology like the AI & Analytics Engine, you don’t have to do all the grunt work, you benefit from sophisticated algorithms with a built-in understanding of down-stream constraints that guide users (expert and business) into good and repeatable wrangling actions — for better results and increased velocity of insights.
Ready to get started prepping your data? That is just one feature in the streamlined ML pipeline. Trial it for free.
Originally published at https://www.pi.exchange.