Data Preparation has been gaining momentum as a relevant topic. Data scientists are moving away from focusing exclusively on data processing models. These experts, together with Machine Learning specialists, are now pointing to the importance of the Data Preparation Process.
The rise of complex Artificial Intelligence business intelligence tools that require vast amounts of data is changing data management best practices. Focusing on data quality is becoming more important than the actual data analysis done by a model because this can help prevent the garbage in, garbage out problem. Companies need to understand this if they want to make the most out of their data sets.
In this post, we discuss the importance of the data preparation process, the steps involved, and some simple ideas that can help you improve data quality to make the most out of your data sets.
What Is the Data Preparation Process?
If there is one thing you should remember from this article, it is this: each Machine Learning project is unique because of its data sets. Because data can vary considerably from one project to the next, it is important to make sure that the right data prep steps are taken.
The Data Preparation Process involves the different steps that need to be taken in order to provide Machine Learning models with the right input. Data needs to undergo different steps so that it can be properly used. A common mistake is to think that raw data can be directly processed without first undergoing the data preparation process.
Data scientists always have to perform different data prep stages so that the resulting data is aligned with the problem that must be solved. Failing to do so may result in a powerful model that uses poor-quality data.
Garbage In, Garbage Out
When the different steps to guarantee a minimum level of data quality are not considered, data processing models may fail to achieve the intended goal. To perform a proper data analysis, there should be a minimum level of data quality being collected, and most importantly, the data should be processed and prepared to solve a specific problem.
Most of the time, different data sets are used. Data integration requires data scientists to merge different data sets into a single one. Any data integration exercise should allow developers to build a model that actually solves the intended problem.
No matter how good or powerful a model is, if the data used is not integrated and does not meet certain criteria, the result will be of poor quality. This is often referred to as the garbage in, garbage out problem. If garbage is inserted into a model, garbage will come out.
Understanding Raw Data
In order to guarantee the quality of any data-intensive model, it is important that data scientists understand the different data preparation steps. The right data prep has the power to provide models with the right data sets.
However, before undergoing any data prep step, companies are faced with pure and raw data. Raw data is data that has not been processed for a specific task.
Even two projects that try to solve the same problem may require different actions on their raw data depending on their data sources. This can be explained because when two companies are trying to implement the same solution to a problem, it is necessary to address the requirements of the specific data sets being used.
Processing raw data properly usually requires understanding the data sources. Your team should ask questions like:
- Where did the data come from?
- How reliable is the data?
- What does the data look like?
- What needs to be done to the data to solve the problem?
If your team is able to answer these questions, you will be one step closer to solving the problem.
Self-Service Data Preparation
Self-service data preparation is a solution to the problem of raw data that is gaining popularity. By using specialized tools, users can directly perform data management tasks, allowing them to process the data needed to achieve specific goals.
There are different self-service data preparation tools available. Choosing the right self-service data preparation tools is a critical success factor for your data management endeavors. In order to choose the right ones, it is best to contact an experienced ML service provider.
In most cases, however, you will most likely have to do some advanced data management and integration.
The Data Preparation Steps
Although each Machine Learning project and its data requirements are unique, there are a series of data preparation steps that are common to all data integration processes.
The key steps of any data preparation process are:
- Understand the problem
- Data prep
- Evaluate models
- Finalize model
Understand the Problem
Defining and understanding the problem is the first step before diving into the requirements of your ML model and its data. Clearly define what you are trying to solve before asking data-related questions.
Data Prep
This is where the data is actually prepared in order to solve the problem. Depending on the problem you have defined, you will need to perform certain tasks using specialized data preparation tools.
Evaluate Models
Once you have prepared your data, you will have to test different ML models to see how they perform solving the problem. This requires you to establish success criteria that will help you choose the best model.
Finalize Model
This last step involves summarizing the insights obtained from evaluating the different models as well as choosing the best one.
The Importance of the Right Data Sets
Having the right data is key to the success of your Machine Learning and HiTech projects. So is having the right data preparation tools to integrate your different data sets.
In order to get things right, the best thing you can do is find an experienced software developer who can understand your needs and the problem you are trying to solve. By doing so, you will be able to undergo a thorough data preparation process that will help you achieve your desired goal.
