Dissecting complex data transformations: 3 real-world examples
Over 20 percent of companies draw from 1,000 or more data sources to feed their business intelligence and analytics systems. That’s a lot of data to unpack.
As your data needs evolve, so will the number of sources you handle. Meeting the challenge is difficult, but necessary for extracting powerful analytics.
To stay on top of your data and keep competitive in your market, you’ll need to conduct complex data transformations.
What do we mean by ‘complex’ data transformations?
We often talk about how CloverDX can solve all of your difficult data problems, not just the easy 90 percent. But, what do we mean by that? What makes a data transformation complex?
Well, we define complex data transformations as those that typically involve a combination of numerous technical and process complexities. While each one could seem simple, combining them together is the difficult part.
For your organization, complex data transformations may involve use cases such as migrating difficult data sets from legacy systems over to new cloud environments, or streamlining data ingestion processes across multiple data feeds.
So, let’s take a look at some real-world complex data transformations and how to approach them:
Example 1: Automation migration of 200,000 disparate XML files
Migrating from legacy systems is a top priority for many businesses looking to leverage the power of the cloud. But, it can be a difficult process when you have endless volumes of files and thousands of fields they need to map into.
A software company contacted us with this exact problem. They had:
- 200,000 XML files with constantly changing schemas
- 3000 fields
- 150 database tables to map into
- Only two months until they had to go live
Their original scope was one year. But as the deadline got closer, they realized they couldn’t do it in time and scrambled for last-minute help.
Taking a scalable approach to their data migration, we created a system to automate the migration. It recognized the XML elements and automatically created the mapping documents for them to migrate into. This saved countless hours of developer time as it didn’t require any human input. It also understood changes in the files, and adapted accordingly.
Not only that, but the business could reuse the template and easily reconfigure it when the migration iterated in the future. In the end, they were able to get their migration live within the short timeframe with a framework that could adapt to any future transformations.Case study: Automating a complex data migration
Example 2: Reducing Salesforce data loading time from hours to minutes
Are rapidly increasing data volumes affecting the speed and quality of your service? Slow data loading times can lead to reduced customer satisfaction and complaints.
Ten years ago, a script based approach to data integration might have been a good idea. But, with data volumes increasing exponentially over the last decade, this method will only cause:
- Increased error rates
- Lag times
- High maintenance
- Lower quality of service
A US wealth management firm struggled with similar challenges. Maintaining libraries of legacy scripts just wasn’t practical for them anymore, and the error rates had a drastic impact on their workload. This resulted in them publishing out-of-date data.
To resolve the issue, they needed to reduce the loading time of their Salesforce platform and free up their resources. However, they didn’t just need a trusted ETL and data integration tool to do this. They needed support from an expert team.
Working with CloverDX, they dropped their outdated scripts in favor of a streamlined, automated platform. With expert support behind them, the end result was a significant reduction in data loading times, freed up resources, and the ability to provide more detailed information to their customers.Case study: Reducing Salesforce data loading time
Example 3: Integrating billions of web traffic records to optimize uSell’s marketing
Market insights help businesses by providing them with valuable information about their customers, competitors and industry as a whole. Market data however often comes from a range of disparate sources. To make use of this data, companies need the right techniques to transform it into a workable state for analysis.
One of our clients—a recommerce platform—wanted to leverage the power of their data to optimize their marketing efforts and gain a better understanding of their website visitors. They had a large amount of web traffic data from a number of different sources, but needed a way to integrate it. They wanted a solution to stop them worrying about the complex data itself, so more time could go on extracting value from it.
This would enable them to:
- Visualize their traffic for reporting
- Have better visibility on returning customers
- Better attribute traffic to specific marketing channels
The company used CloverDX to join up their disparate data sources and streamline their ingestion process. This allowed them to transform data into a format suitable for analytics. This process was not only simple, but scalable.Case study: Making complex data suitable for analytics
Simplify complexity with CloverDX
Managing large volumes of data effectively is one of the biggest data challenges facing companies today.
Complexity is inevitable in our data-driven world. But meeting the challenge is key to leveraging data insights and strengthening your business intelligence.
In these three real-world examples, large data volumes and disparate sources were the problem, and tried and tested manual processes weren’t up to the job. To simplify your data challenges, you need a straightforward scalable tool, and an expert support team.
Most tools on the market will solve your easy problems, but CloverDX goes beyond this to tackle your toughest data issues. You can connect to any data sources—whether they’re in cloud, on-premises, or both - and handle any data formats and transformations with customizable components.
Want to see it in action? Book a demo today.