Data transformation is an important step in the data science process, but it can be time-consuming and complicated. It involves analyzing large amounts of data, identifying patterns and trends, and applying the insights to your business. At the same time, it directly impacts your ability to understand your customers, improve their experience and make better business decisions.
It’s best to find a solution that’s fast, easy, and affordable to understand and implement data transformation on your own. In this post, we’ll explore an effective alternative to data transformation services with machine learning.
Data transformation is the process of converting data from one format to another. This can be done for a variety of reasons, including:
Data transformation is an important step in the data science process because it can help you to improve the quality of your data, make it compatible with other systems, and make it more valuable to your business.
With that said, "data transformation" is a broad umbrella term that can refer to a number of different activities, including data cleansing, data normalization, data aggregation, data integration, and data conversion.
For instance, consider a BigQuery dataset that contains a table with information on leads for a SaaS firm. Common issues will include email addresses that are no longer valid, missing data, duplicate records with slightly different values, and so on. To prepare this data for analysis, we would need to perform a number of data transformation tasks.
Data transformation doesn't have to be a fully manual process - there are data transformation services (DTS) that can help you get the most out of your data without breaking the bank.
Let's take a look at what data transformation services are, what they can do for you, and how they compare to alternatives.
Microsoft's Data Transformation Services is a tool that can be used to automate the process of extracting, transforming, and loading data to or from a database.
DTS was included with earlier versions of Microsoft SQL Server, and was almost always used with SQL Server databases, although it could be used independently with other databases. The first three versions of SQL Server (SQL Server 7, SQL Server 2000, and SQL Server 2005), for instance, included the Data Transformation Services tool.
The DTS designer allowed for the creation of DTS packages, which could be saved and later executed by the SQL Server Agent or via an OLE DB connection. DTS packages are the fundamental logical unit of work in DTS. A DTS package can be used to extract data from a variety of sources, transform the data in a number of ways, and then load the data into one or more destinations.
DTS packages can be designed to run on demand or they can be scheduled to run at regular intervals. DTS packages can also be executed from within other applications, such as Visual Basic.
DTS is similar to SSIS (SQL Server Integration Services), which is an ETL tool provided by Microsoft to extract data from different sources. SSIS can be used to perform a wide range of data migration tasks. For example, you can use SSIS to migrate data from an Oracle database to a SQL Server database, or from a SQL Server database to a MySQL database.
The main difference between DTS and SSIS is that DTS tools are designed to be used with SQL Server databases, while SSIS can be used with a wide range of data sources.
DTS and SSIS are old-school data transformation tools. If you're looking for a more modern solution, you might want to check out dbt (data build tool). dbt is a command-line tool that enables you to write SQL transformations in a modular way.
dbt is designed to be used with data warehouses, and it supports a wide range of data sources, including Redshift, Snowflake, BigQuery, and Postgres.
Another popular data transformation tool is Apache Airflow. Airflow is an open-source platform that is used to author, schedule, and monitor workflows.
Airflow was originally developed by Airbnb, and it is now used by a wide range of companies, including Twitter, Pinterest, and Slack.
Airflow is written in Python, and it uses a directed acyclic graph (DAG) to represent the workflow.
Talend offers a drag-and-drop interface that makes it easy to build data transformation pipelines. Talend also offers a wide range of connectors that can be used to connect to a variety of data sources.
Matillion is another commercial data transformation tool. Matillion offers a cloud-based data transformation platform that supports a wide range of data sources, including Amazon S3, Redshift, and Snowflake.
Pentaho offers a desktop application that enables you to visually build data transformation pipelines. Pentaho also offers a wide range of connectors that can be used to connect to a variety of data sources.
Python is the go-to language for data science, and pandas is the most popular Python library for data analysis.
Pandas is a powerful data analysis tool that enables you to perform a wide range of data transformation tasks, including cleaning data, filtering data, and aggregating data.
There is a wide range of data transformation tools available, each with its own strengths and weaknesses. The best data transformation tool for you will depend on your specific needs.
A study by McKinsey found that industry spending on data-related costs is increasing dramatically, with 50% higher spend from 2019 to 2021 compared to the previous two years. The same study, however, found that using standardized, transparent, and auditable data transformation processes can help reduce these costs by as much as 35%.
Businesses that successfully standardize and automate their data transformation processes by 2025 will be able to achieve greater agility, drive faster decision making, and improve business performance.
Instead of data transformation services, businesses should look into using AI as an alternative. The traditional method of data transformation is time-consuming, and often requires coding skills and in-house data scientists. However, with AI, businesses can achieve the same results in a fraction of the time, without any coding skills or in-house data scientists.
Akkio is easy to use, and you can get started in minutes. First, you connect your data source to Akkio. A common option is to connect a CSV, the likes of which you might export from a relational database like MySQL, PostgreSQL, Microsoft SQL Server, or even just a spreadsheet like Excel.
You can also connect more complex data sources, like Google BigQuery, Hubspot, and Salesforce. In any case, Akkio will automatically detect the schema and data types, and conduct basic data quality checks.
Issues like missing data and incorrect data types will be automatically detected and handled in the background. You can also choose to merge multiple data sources together in just a few clicks.
A technique called "fuzzy matching" can be used to automatically merge data that isn't an exact match. This is useful for things like trying to match customer data from different databases that might have slightly different formats.
Akkio is the best alternative to data transformation services, and it's easy to use, fast, and cost-effective. With Akkio, you can quickly and easily prepare your data for analysis, without any coding skills or in-house data scientists.
Data transformation is the prerequisite for training an accurate, robust machine learning model. Virtually any team can use machine learning to optimize their business if they have access to quality data.
For instance, sales teams have several key objectives: increase revenue, reduce customer churn, and increase customer lifetime value (LTV). To achieve these objectives, sales teams need quality data on their customers, including data on customer behavior, preferences, and demographics.
With that data, salespeople can build models to predict which leads are most likely to convert, which customers are at risk of churning, and which customers are most valuable. By using these models, sales teams can focus their efforts on the leads and customers that are most likely to result in increased revenue, market share, and LTV.
Similarly, HR teams have key objectives around reducing employee attrition, hiring the most qualified candidates, and reducing the time to hire. HR teams can use AI to predict which employees are most likely to leave, which candidates are most qualified for a given role, and how long it will take to hire a given candidate.
By using these models, HR teams can focus their efforts on the employees and candidates that are most likely to result in reduced attrition, increased quality of hires, and reduced time to hire.
Even financial teams dealing with highly-complex and volatile data can use machine learning to automate key tasks, such as fraud detection, credit scoring, and risk management. For instance, a team might use a model on BigQuery data to predict loan default rates.
In this case, the team would need to train and deploy a model that takes in a number of features (e.g. loan amount, interest rate, credit score, etc.) and outputs a prediction (e.g. the probability that a loan will default).
Beyond building machine learning models, Akkio lets users effortlessly deploy those models into production with just a few clicks, without any IT or engineering involvement. Through direct integrations with tools like Snowflake, Hubspot, Google Sheets, and Zapier, Akkio makes it easy to keep data in sync and up-to-date, so teams can focus on their core objectives and leave the data management to Akkio.
Data transformation is a critical step in any machine learning project, and Akkio makes it easy.
Akkio is a leading no-code machine learning tool, and it's loved by users for its simplicity and power. With Akkio, you can filter, clean, and prepare your data quickly and easily, without needing to hire an expensive company or use a complicated tool.
As Christina Valente, a Senior Director of Product Operations, writes about Akkio: “With Akkio, we are able to build and deploy AI models in minutes, with no prior machine learning expertise or coding.” So if you're looking for a tool to help you transform your data, Akkio is the perfect choice.
Get started with Akkio today, and see how easy data transformation can be.