6 Ways Data Integration Improves Machine Learning

Machine learning powers a wide array of technologies, from medical diagnostics, to Netflix recommendations, to self-driving cars. But for all these critical advancements, machine learning models are still only as reliable as the data they are trained on.

Generating this quality data requires significant time and resources. With machine learning projects, almost 80% of a data scientist’s time is spent on pre-processing data. Only 20% is devoted to developing the model and generating insights.

That’s why data integration platforms are so valuable to machine learning workflows. Data integration platforms make machine learning models more effective and efficient by supplying better, faster, and larger volumes of data, all while enabling data scientists to focus on more important tasks.

Here are six key ways data integration platforms improve machine learning:

1. Streamline Data Ingestion

To feed a machine learning model data, teams must code out a custom data pipeline for each data source. This might be acceptable for very small projects, but models that require multiple data sources can quickly overwhelm limited team resources.

Data integration platforms come with pre-built data pipelines. These pipelines automatically extract source data, and then load the data into an easily accessible data warehouse or data lake. Instead of spending valuable time manually building data ingestions, teams can focus on perfecting the model, and other more essential tasks.

2. Perform Data Cleansing

In order for machine learning models to comprehend data, the data must be converted into the proper format. This is known as “pre-processing.” Manual pre-processing is a tedious and laborious effort. Each dataset demands different cleansing requirements, forcing teams to focus on new code or data processes, instead of mission-critical objectives.

In contrast, data integration platforms automatically cleanse raw data to produce high quality training inputs for ML models. With a dedicated integration platform, teams do not have to expend resources to reshape the quality or the formatting of the data.

3. Boost Data Volume

Data quality and cleanliness are essential for training ML models. But so is data volume. The more data points the model experiences, the more accurately the model is trained. With the computation and storage capabilities of cloud data platforms, teams can expose models to a higher volume of training data than ever before.

ELT-based platforms provide ML models with more training data than their ETL counterparts. ELT platforms can extract data from hundreds of different sources with pre-built data connectors, and then simultaneously load and transform the data directly inside a cloud data warehouse. This depth, speed, and efficiency can greatly expand data throughput for the ML model.

4. Access Previously Unavailable Data

With data integration platforms, teams can broaden the range of data that machine learning models are exposed to. Data integration platforms help teams unlock hard-to-find but useful unstructured “dark data.” By consuming more diverse datasets, machine learning models become faster and more accurate.

5. Automate Entire ML Workflow

With certain data integration platforms, teams can automate an entire machine learning workflow, from data ingestion to model training. Data integration platforms that automate data orchestration, including auto-executing SQL-based queries, can perform not only ingestion but also data cleansing.

These platforms can also execute ML models, using standard SQL, directly inside cloud data warehouses such as Google BigQuery. This improves workflow efficiency and allows teams to manage training, testing, validation, and deployment of an ML model in a single platform.

6. Feed Real-Time Data

Many production ML algorithms require real-time data ingestion and computation, especially prediction systems that companies such as Netflix, Apple, and Amazon use to make user recommendations. Traditional ETL pipelines with scheduled data extraction cannot possibly provide these real-time inputs.

Conversely, data integration solutions feed ML models real-time data by harnessing streaming capabilities and cloud computing nodes proximate to the data source. This enables top companies to deliver curated user experiences and optimize product conversions.

Take Machine Learning to the Next Level with Data Integration Platforms

Data integration platforms can take a team’s machine learning projects to the next level. Every use case is different, but for teams who want to automate ML workflows, and supply more high quality data to ML models, a data integration platform is something to seriously consider.

For more on what teams should look for in a data integration platform, read our new eBook, the Ultimate ELT Buyer’s Guide.