You can't throw data at a machine learning algorithm and expect it to work. You need to prepare your data first.
Data preparation, therefore, is an essential step in any data analytics project. It refers to the process of getting your data ready for analysis. This involves removing errors, cleaning up your data, and ultimately transforming it into a suitable format.
It can also involve data augmentation (adding additional data points) and data reduction (removing unnecessary data). In this article, we are going to discuss why data preparation is important and how you can make the process easier by using AI.
Some tools that rely on data, from machine learning algorithms to business intelligence dashboards, can technically function even if the data is not extensively prepared. However, to get the most out of your data, it's important to prepare it properly.
There's a saying: "Garbage in, garbage out." This means that if you put bad data into a tool, you'll get bad results. On the other hand, if you take the time to prepare your data properly, you'll get better results.
Here are some benefits of data preparation.
The point of data analysis is to find trends and insights in your data - particularly those that are otherwise difficult to find. Finding these "cracks in the dam" requires looking at your data in new and different ways.
If your data is inaccurate, then the findings will be similarly inaccurate. This is why it's important to fix errors in your data before you start processing it.
If you have many missing values, for instance, you'll want to either impute them or remove them entirely. Otherwise, your results will be biased. Another common issue is incorrect data types - for example, treating a date field as a text field. This can cause all sorts of problems down the line.
Even if the data types are correct, the values may be in the wrong format. For example, if you're trying to analyze data from different countries, you'll need to make sure that all the currency values are in the same format. Otherwise, you won't be able to compare them properly.
To avoid these issues, it's important to fix errors in your data before you start processing it. This can be a time-consuming task, but it's necessary if you want to produce accurate results.
Often, machine learning models will rely on different data sources. For instance, sales and marketing teams may use both Salesforce and HubSpot data. To train a machine learning model to predict customer churn, you'll need to combine both data sets.
However, before you can combine the data, you'll need to make sure that it's in the same format. Otherwise, the model won't be able to read it properly.
This is why data preparation is so important for machine learning. You need to take the time to format your data properly so that it can be used by the algorithm.
If you plan on using the same data for multiple applications, then preparing it well in advance is important. This is because you won't need to put in the same effort for subsequent analyses.
For instance, if you're using the same data to train different machine learning models, you can save time by preparing the data once and then using it for all your models. This way, you don't have to format and clean the data every time you want to train a new model.
Data preparation ultimately means your results will be more reliable. This is because you've taken the time to remove errors and format the data properly. As a result, you can be more confident in the findings of your data analysis.
Data preparation is an umbrella term that covers the broad range of tasks completed to get data ready for analysis. There's a lifecycle to data preparation that includes data collection, storage, transformation, enrichment, cleansing, feature extraction, and organization.
Let's take a closer look at each of these steps.
The first step in data preparation is data collection. This is where you need to define what data you need and where you're going to get it from.
Data comes in many formats: Structured data (like databases) and unstructured data (like text documents). It can come from internal sources (like CRMs) or external sources (like social media).
In some cases, like accessing internal structured data, it's as easy as clicking "export." However, in other cases, like collecting unstructured data from social media, it can be more difficult. You may need to use web scraping techniques to get the data you need.
When data is less structured, it's generally lower quality. This is because it hasn't been through the same processes that structured data has (like data entry). As a result, it's important to define what data you need, where you're going to get it from, and how you'll confirm its quality.
Once you have your data, you need to store it somewhere. Data storage is all about deciding where to store your data and how to ensure its security.
There are many different storage options available, from traditional relational databases to newer cloud-based storage solutions. The right storage solution for you will depend on your specific needs.
You also need to consider how you'll ensure the security of your data. This is especially important if you're storing sensitive data, like customer information.
For most use cases, cloud-based storage is more convenient, scalable, and cost-effective, as building one's own data infrastructure is very expensive and time-consuming.
After you've collected and stored your data, you need to transform it into a format that can be understood by machine learning algorithms. For instance, a sentiment analysis algorithm doesn't actually analyze a word like "happy." It analyzes the numerical representation of that word.
Data engineering is the process of taking your data and transforming it into a format that can be used by machine learning algorithms. There are many different ways to do this, but some common techniques include:
Data cleansing is all about checking that your data is accurate and free from errors. This is a crucial step in data preparation, as inaccuracies in your data can lead to incorrect results.
There are many different ways to clean data, but some common techniques include:
Once you've transformed and cleansed your data, you need to identify which parts of your data are most important for making predictions. This is known as feature extraction or feature selection.
It's important to consider that correlation does not imply causation. Two variables can be correlated but not actually cause each other to change. Worse yet, keeping in a too-highly correlated feature, such as two slightly different definitions of the same thing, will cause your model to overfit.
For example, you might have a model for predicting whether a lead will convert, and if one of your features is "client_revenue," then the model will be useless, as it'll simply learn that if the revenue is greater than 0, the lead has already converted.
Selecting features that are causally related is critical to the success of most machine learning models. For instance, features that could drive a lead to convert may be how many interactions with your team the lead has had, what content the lead has been interacting with, what their pageviews are, or a whole host of other features.
The most important part of feature selection is understanding your business process, and understanding which features are likely to be drivers of the KPI you're predicting.
After you've transformed, cleansed, and selected your features, you need to store all of your data in a way that makes it easy for a machine learning algorithm to access. This is known as data organization.
There are many different ways to organize data, but some common methods include:
When organizing your data, you need to consider both the structure of your data and how you're going to access it. Big data, for instance, needs to be stored in a way that makes it easy to process in parallel.
Traditionally, data scientists would use tools like SQL and Python in their data preparation process. Now, even business users can use data preparation tools to turn raw data, whether it's a CSV or a data lake, into insights that can help them make better business decisions.
Akkio is a no-code AI tool that offers powerful data prep features. Once you've connected your data, Akkio’s data pipelines will automatically prepare data for analysis to streamline your workflow. This includes tasks like imputation, deduplication, and data validation.
All you need to do is select a column you want to predict, like customer churn, lead conversion, LTV, or even fraud, and Akkio will automatically build a machine learning model to help you get the most accurate results.
In a competitive analysis, Akkio was found to be up to 100 times faster than tools like Google AutoML and Microsoft Azure, while being significantly more cost-effective, and even more accurate on some tests.
Akkio offers data integration with many sources, including Salesforce, HubSpot, Excel, and thousands of others through Zapier. Akkio also integrates with big data tools like Snowflake and BigQuery. Snowflake is a cloud-based data warehouse, where computing and storage are decoupled and scaled independently. Google BigQuery is a serverless, highly scalable, and cost-effective data warehouse that's more commonly used by teams already in the Google ecosystem.
Ultimately, Akkio is a powerful tool that can save you hours of data preparation time. It's also very easy to use, as it has a point-and-click interface that requires no technical expertise.
Data wrangling is essential for building accurate machine learning models for your end users. Self-service data preparation tools allow business analysts, data analysts, or even completely non-technical users to streamline the process of data preprocessing.
Bringing this automation to your data management process means that you don't need to hire data engineers to do it for you. This also allows end users to be more involved in the data preparation steps, rather than leaving it all up to the data science experts.
Akkio's no-code user interface makes it easy to get started with any data initiatives. Try out Akkio today and see how it can help you optimize your data preparation.