If you want to use artificial intelligence (AI) in your business, the first step is ensuring that you have high-quality data. After all, AI is only as good as the data it’s given. And if your data is messy, inaccurate, or incomplete, your AI initiatives are likely to become part of the 87% of AI projects that fail.
Unfortunately, data preparation is often an afterthought, even though it's a prerequisite for success with machine learning. In fact, a recent survey found that data preparation is the most time-consuming part of an AI project, taking up to 22% of the total time.
One reason data preparation is often neglected is that it's seen as a tedious, low-value task. For a sales team that wants to revolutionize their process with AI, from lead scoring to sales automation, the last thing they want to do is spend time on data entry and cleaning.
Yet, without clean data, an AI project is likely to fail. In this article, we'll explore how to prepare your data for success with AI. You'll understand how to set up automatic data cleaning and preparation and how it can help you get better insights through AI.
Data preparation is a critical step in data quality management, as it involves fundamental cleaning steps such as dealing with outliers, data scrubbing, validation, data transformation, and removing duplicate data, as well as irrelevant data.
It is a core part of data science and data wrangling, as data scientists and data analysts must ensure that the raw data they are working with is accurate and ready for visualization and can ultimately improve business decisions.
To help with this process, there are a variety of data cleaning tools, such as Python and Excel, while tools like Tableau are used more for visualization.
Data integration and data management are also important components of data preparation, as they help to ensure that all the different data sources, such as databases and spreadsheets, have accurate and up-to-date data and that the data is in the correct format and syntax.
Data wrangling and dataframe manipulation are also important, as they help to identify inaccuracies, ensure uniform scales for numeric values, and other prevent issues. This can be followed by more advanced concepts such as data segmentation and regression to derive useful metrics and insights.
Investing in data preparation is an investment in the future of your business. The saying that "data is the new oil" is more than just a catchy phrase - it's the truth, but only if you have high-quality data. If your data is messy, it will be difficult to make sense of it and draw accurate conclusions.
There are four main benefits to preparing your data:
Each of these benefits is important in its own way, but when taken together, they paint a picture of why data preparation is essential for any business that wants to make use of data in order to make better decisions.
Let's take a closer look at each benefit.
When data is messy, that means it contains errors or inconsistencies. These can be caused by a number of things, such as human error when data is entered, different standards for how data is recorded, or simply incorrect information.
For instance, a dataset of leads might have revenue values in different formats (e.g. "$120,000" and "120k"). This makes it difficult for a machine learning model to interpret the data correctly. As a result, the model will make inaccurate predictions.
Inconsistent data can also lead to errors in your understanding of the data. For example, if you're trying to understand how many customers you have in each state, but some of the data is in different formats (e.g. "NY" and "New York"), you might count the same customer twice.
Even if the data is in the right format, you might be missing important information. For instance, if you're trying to understand customer churn, you might have a dataset of customer data with a column for "last purchase date." But if that column is missing data for a significant number of customers, your analysis will be incomplete. Old-school methods of imputation, like mean or median imputation, can introduce inaccuracies in your data.
Cleaning and preparing your data helps to reduce errors and inconsistencies, making the data more accurate and reliable.
Data preparation also makes it easier to understand your data. When data is organized and structured in a consistent way, it's easier to develop models of how the data works. This is important for both humans and machines.
If you're trying to develop a machine learning model to predict customer churn, for example, you need to have a good understanding of the data in order to develop the right model. Data preparation can help you to understand the relationships between different variables and how they impact each other.
Data preparation can also improve the accuracy of your predictions. This is because machine learning models are only as good as the data they're trained on. If the data is inaccurate, noisy, or inconsistently formatted, the predictions will be less accurate.
One way to think of it is this: if you're trying to predict the weather, it would be easier to make an accurate prediction if you knew the temperature, humidity, and wind speed. But if you only had the temperature, your prediction would be less accurate. The same is true for machine learning models - the more data you have, and the better organized it is, the more accurate your predictions will be.
Finally, data preparation can save you time and effort in the long run. This is because, once your data is cleaned and structured, it's easier to perform repetitive tasks and analysis. For instance, if you're trying to understand customer churn, you might want to run the same analysis multiple times on different subsets of data.
If your data is already prepared, this task will be much easier and faster. You won't have to waste time cleaning and preparing the data each time you want to run the analysis. In addition, you can be confident that the results of your analysis are accurate since they're based on high-quality data.
Data is critical to business success, but it's not always clean and easy to work with. In order to get the most out of your data, you need to take some time to clean it up and prepare it for analysis. Here are some tips on how to do just that.
The first step in data cleaning is to remove any duplicates or errors. This includes empty cells, repetition, and coding errors. Invalid entries can also be removed at this stage.
Duplicate records can be the result of technical issues, human error, or data entry mistakes. For example, suppose a salesperson merged two customer files together, but some names were in different formats between the two files, such as "John Smith" and "Smith, John". In this case, the data cleansing process would involve identifying and correcting these inconsistencies.
In some cases, you may need to add extra information to your data in order to make it more useful. For example, a dataset of leads might include information on the lead's name, company, phone number, and email address. However, it might be missing other important data points like job title or location.
In this case, you could fill in missing values with estimated values from other entries with the same variable. This would help to ensure that your dataset is as complete and accurate as possible.
Another common issue with data is incorrect formatting. This can include things like typos, incorrect capitalization, and other formatting issues. As with some of the previous issues, this can result in duplicate entries.
Manual data cleaning can be a tedious and time-consuming task. It's much better to use a tool that can automatically clean and prepare your data for you.
When you connect your data to Akkio, the data formats are automatically recognized, and issues like missing data are handled so that models don’t break.
Akkio uses machine learning algorithms and deep learning techniques to analyze large amounts of data quickly and accurately. It also integrates with popular tools like Google Sheets, Hubspot, and Salesforce for easy usage in any setting. Akkio doesn't require any coding knowledge and can be used by non-technical users.
For instance, finance teams can use Akkio to detect fraud, forecast costs, or automate responses to legal requests. Marketing teams can use Akkio to predict customer churn or augment lead scoring. HR teams can use Akkio to identify employee retention risks or recommend training and development programs.
If you're looking for a tool to help you clean your data, we recommend giving Akkio a try. Thanks to its no-code approach and free training time, Akkio is a great option for businesses of all sizes.
Implementing artificial intelligence in your business can help you to get better insights and make better decisions. However, in order for AI to be successful, you need to have high-quality data. Data preparation is the process of getting your data ready for analysis and it's an essential part of any data analysis project.
There are many benefits to preparing your data, including reducing errors and inconsistencies, facilitating understanding, improving predictions, and saving time and effort. The data preparation process usually involves steps like removing duplicates and errors, adding extra information, and correcting formatting issues.
Tools like Akkio can help you quickly and easily gain value from your data. Akkio uses machine learning algorithms and deep learning techniques to analyze large amounts of data quickly and accurately. Try a free trial of Akkio today and see how it can help you to get the most out of your data.