Organizations want to get the most value out of their data, but it takes more and more time for data teams to manage it. The exponential growth of data collection has left many organizations with a massive data governance challenge. Artificial intelligence (AI) and machine learning (ML) can help data teams get on top of their data through the use of augmented data integration.
What is Augmented Data Integration?
Manual data management tasks eat up a lot of your data team's time, and scaling your operations by bringing in more staff can be challenging. Data scientists and specialists are in demand, leading to long and expensive recruiting processes that can end in frustration.
Augmented data integration uses AI and ML technology to improve data integration and data management processes. Gartner found that adding AI into the data management mix can cut down the time spent on manual data management tasks by 45 percent.
These augmented processes can take over the tasks that eat up precious data scientist time and empower the data team with many helpful functions. They can help your organization with data quality, metadata management, and many data governance tasks.
By augmenting your data integration operations, you can better use your organization’s data. While you can build or procure a standalone AI-powered data integration solution, it often makes more sense to use it alongside your existing tools for optimization.
Data Management and Integration Challenges
When considering how to use augmented data integration in your organization, it’s important to identify your most pressing data management challenges. Some of the most common challenges you may encounter include:
- Data volume too large: Data comes from nearly everywhere these days, thanks to the Internet of Things (IoT) and the large number of connected devices available. This data volume can quickly become overwhelming, especially if you don’t have a scaling plan in place.
- Too many data sources: How many applications and databases do you have in your organization? You could be dealing with hundreds or thousands of sources just within your internal systems. Complex infrastructures can lead to many data integration struggles.
- Slow time to insights: How long does it take to process your organization’s data for actionable insights? If you have to wait weeks before reports show up, then you’ll have a hard time reacting to sudden changes in the market.
- Poor-quality data: Another drawback of dealing with a massive amount of data and numerous data sources is that your data quality suffers. You may have old records, errors, and other problems moving into the integrated data stores.
- Variety of data formats: If your data team handles a lot of raw data, then they have to spend a lot of time changing it into the formats needed for their analytics and BI solutions of choice.
- Technical talent tied up: Sometimes your data team simply lacks enough hours in the day to get things done. When they spend so much of their time on manual data integration and management, they can’t get to higher-value tasks.
The Benefits of Augmented Data Integration
Augmented data integration not only automates processes but can also self-configure over time to adjust to changes in your data demands. The system’s recommendations can guide your data team in creating fully optimized data integration workflows, which frees up their time for less tedious tasks.
Your data scientists can go back to problem-solving, rather than repetitive administrative duties that barely relate to their specialty field. They become more engaged, helping your organization drive more value from all of your data.
The AI-powered data integration methods also reduce human error, since there are limits on manual input. The algorithm can run day and night on tasks such as data mapping, which can speed up many other parts of data integration.
Once the AI and ML models learn your data integration use cases, it can help you pinpoint any abnormal issues before they become a problem. You can handle data management concerns proactively.
Implementing Augmented Data Integration
When you’re exploring an augmented data integration solution, keep in mind that this is a tool for your data science team. It augments their abilities, rather than acting as a replacement for them. Your team can scale its operations and get more done without you allocating additional resources.
A good starting point for augmented data integration is your most time-consuming task or one that represents the greatest challenge for your data specialists. Starting small gives you time to understand how AI and ML can improve data integration and the potential use cases for your organization.
Using Integrate.io’s ETL Tool to Support Augmented Data Integration
Many data integration tools can complement your augmented data integration solution. For example, an Extract Transform Load (ETL) tool such as Integrate.io can automate many data pipeline tasks, allowing your AI-powered platform to focus on areas that benefit the most from this type of technology.
Integrate.io’s ETL platform automates extracting and combining data from multiple sources; cleansing data; transforming it into the proper formats; and loading it into databases, data warehouses, and Salesforce. Handling these steps in advance allows the augmented data integration tool to have high-quality datasets ready for further use.
Learn how Integrate.io can help your augmented data integration environment with a 7-day demo of the platform.