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As the world becomes more data-driven, businesses are under pressure to make sense of larger and more complex data sets. Traditional methods of data preparation, such as manual data entry and cleaning, are time-consuming and error-prone.
In fact, studies show that data scientists spend over a fifth of their time on data preparation, which is more than the average time spent on tasks like training and deploying models. For the non-technical business user, data preparation can be even more daunting.
AI self-service data preparation can help business users overcome these challenges by automating tasks that are traditionally done manually. Machine learning powered self-service data preparation can also help users interpret and understand their data more effectively.
In this article, we’ll explore how AI self-service data preparation works, and how you can implement it in your business.
Data science is the field of turning data into valuable insights and predictions. In order to do this, data must first be prepared for analysis. Data preparation includes tasks such as cleaning up messy data, converting it into a usable format, and adding additional features that will help improve predictions.
AI self-service data preparation is a tool that allows anyone with no coding or technical skills to prepare their data for analysis using machine learning techniques like classification and regression analysis.
For example, consider a dataset of customer leads, and you're building a model to score each lead. Some of the values in some columns are likely missing, such as "revenue" or "number of employees." With AI self-service data preparation, you can automatically fill in these missing values using techniques like imputation, which uses known values to infer the missing values.
Instead of simply using the column's median or mean value, imputation can take into account the values of other columns to better predict the missing values. So, for example, if you're imputing the "revenue" column, the model might use the "industry" column and "funding stage" column to better predict the missing values.
Another common data issue is incorrect values, such as typos or data that was entered in the wrong format. For example, a customer's zip code might be entered as "54321" instead of "54321-1234." With AI self-service data preparation, you can automatically detect and correct these values using techniques like fuzzy matching. To give another example, the same location might be in many different formats, such as "LA," "L.A.," "Los Angeles, CA," "Los Angeles," and so on. In this way, just 50 locations, for instance, could be in several hundred different formats.
AI self-service data preparation can automatically standardize these values so that they're in the same format, which is important for downstream analysis. Stringent quality control is essential for data sets used in predictive modeling and machine learning because even a small number of incorrect values can throw off predictions.
Data preparation isn't just about cleaning up these mistakes; it's also about enriching data sets with additional features that will be useful for predictive modeling. For example, you might want to add a column for the average income in the ZIP code where the customer lives. This additional column can help improve predictions because it provides more information about the customer.
To do this manually would require significant effort: You would have to find a data set with income information, match it up with the customer ZIP codes, and then calculate the average income for each ZIP code. With AI self-service data preparation, you can more automatically add these additional features to your data set.
AI self-service data preparation tools typically use a graphical user interface (GUI) that allows users to point and click their way through the data preparation process. This makes it much easier and faster for business users to prepare their data, without having to write any code.
Businesses are under pressure to cut costs, boost the bottom line, and improve operational efficiency. Some firms are turning to layoffs, but this often backfires in a multi-tiered way. For one, it causes morale to plummet, which can lead to even more inefficiencies and a further decline in performance.
Further, it can put valuable knowledge and experience out the door. Moreover, unless the firm heads to bankruptcy, it’s only a matter of time before the company has to rehire and at a higher cost than the original employees.
For businesses to get ahead in today’s challenging macroeconomic environment, they don't need to just do more with less; they need to do things smarter. One way to achieve this is by turning to artificial intelligence for help in areas such as self-service data preparation.
Self-service data preparation is a method of using AI to automatically clean and prepare data for analysis. This is opposed to the traditional approach of manual data preparation, which often takes hours or even days, and can introduce errors.
There are many benefits of using self-service data preparation, including:
If a data team can't gain value from data because it spends so much time on data preparation, it's not doing its job. Data teams should focus on adding value, not just acquiring and maintaining data. This is where self-service data preparation comes in.
It allows businesses to automate the tedious and time-consuming tasks of data preparation, which can free up hours or even days for the team to focus on other tasks, such as analyzing the data.
In addition, self-service data preparation can help businesses reduce the need for manual data preparation, which often takes hours or even days, and can introduce errors. Consider, for instance, the need to impute missing values.
If a team were to build a new model to accurately impute missing values every time they encountered them, it would quickly become a full-time job. But with self-service data preparation, businesses can automatically impute missing values using automated machine learning algorithms.
Inaccurate data isn't just a headache when it comes to building models; it can actually cost businesses money. As an Entrepreneur article points out, "bad data is on average costing businesses 30 percent or more of their revenue", with data from Gartner suggesting that the average financial impact of poor data quality on organizations is $9.7 million to $14.2 million per year.
On a macro level, this comes out to $3 trillion in losses globally. This is why it's so important for businesses to focus on data quality.
If you've ever manually cleaned data, you know it's not an easy or fun task. It can be time-consuming, and it's often difficult to get the data to a place where it's clean enough for analysis. The tedium of manual data preparation can often lead to mistakes, which can in turn lead to inaccurate results.
Self-service data preparation can help businesses avoid these pitfalls by automating the process of data preparation using machine learning algorithms.
VentureBeat finds that 87% of data science projects never make it into production. The reasons for this are multifold, but a major roadblock is the difficulty of data preparation.
Data preparation itself is a means to an end; the goal is to get the data into a usable format so it can be analyzed. But all too often, data preparation becomes an end in itself, with teams spending weeks or months manually cleaning and preparing data.
The same VentureBeat article explains that "it’s essential that every person on the team is able to collaborate with everyone else," but data preparation is often an individual activity. This can lead to a bottleneck in the data pipeline, with one team member holding up progress for the entire team.
Self-service data preparation can help businesses avoid these pitfalls by automating the process of data preparation. This way, businesses can focus on what matters: ultimately gaining actionable insights from their data.
A machine learning workflow has many steps, and each step depends on the output of the previous step. From data acquisition to data preparation to model training to model deployment, there are a lot of moving parts.
If one team is responsible for acquiring data, another for preparing it, and yet another for training models, it can be difficult to keep track of who has the most up-to-date version of the data. This often leads to delays as teams wait for others to finish their part of the process.
With self-service data preparation, businesses can automate the process of data preparation and avoid these delays. This way, businesses can ensure that everyone has access to the most up-to-date information as quickly as possible.
As businesses have realized the power of data-driven decision-making, the demand for data preparation tools has exploded.
There are many different tools available for self-service data preparation, each with their own unique approach and set of features. Some tools use automation and machine learning while others use code - but regardless of which tool you choose, they all essentially do the same thing: they take unstructured or semi-structured raw data from various sources and clean it up so it can be used for analysis.
Microsoft Power Query, for instance, is a popular way for Excel experts to prepare data. It includes a visual interface that allows users to see the transformations being applied to their data as they are happening. Other options include Trifacta Wrangler, which also has a visual interface, and Alteryx Designer.
For businesses seeking to use AI, the best solution includes data preparation as part of the workflow - rather than a standalone process.
Akkio is a no-code AI tool offering self-service data prep for users of all technical levels, from business users to business analysts to full-fledged data engineers.
Akkio's data preparation software can connect to many data sources, including big data lakes and data warehouses, tools like Salesforce, and volumes of data from any format.
The software's built-in no-code ETL functionality and data transformation capabilities make it easy to clean, prepare, and join data from multiple sources for analysis.
Data preparation is crucial for business intelligence and data discovery, as well as data governance and data integration. Akkio's visual dashboards make it easy to transform data and build models. Once a model is built, it's easy to deploy it in real time using our integrations or our API.
Akkio is designed to enable AI as a self-service feature. This means that anyone can use it to create predictions and optimize their KPIs across departments.
For instance, sales and marketing teams can use Akkio to create predictions based on business data like past purchases and transactions. This allows them to more accurately forecast future sales, score leads, create targeted marketing campaigns, and so on.
Similarly, HR teams can use Akkio to predict employee churn, and take preventive measures to keep their best talent. Moreover, financial teams can use Akkio to detect fraud, score creditworthiness, optimize pricing, and more.
Compared to tools like SQL or Tableau, you don’t need any advanced data skills to get started with Akkio. In short, Akkio’s self-service data preparation is a game-changer for businesses of all sizes. It’s easy to use, requires no coding skills, and can be used to improve a wide range of business applications.
As businesses become increasingly data-driven, the need for effective data preparation tools is more important than ever. Self-service data preparation can help businesses automate the process of data preparation, saving time and improving accuracy.
Akkio is a tool that allows anyone with no coding skills or technical knowledge whatsoever to prepare their datasets for machine learning in minutes. Akkio’s self-service data preparation is a game-changer for businesses of all sizes. It’s easy to use, requires no coding skills, and can be used to improve a wide range of business applications.
If you’re looking for a way to streamline your data preparation process, Akkio is the perfect solution. Try it today!