Combine manual data review with automation
DATA MANAGER
Enable non-technical data owners to interact with data - in a user-friendly interface - as part of an automated data pipeline.
Save time, eliminate errors and keep your data secure by handling manual review completely within the CloverDX platform.
Automation sometimes needs manual intervention
Even the most automated processes often need some human interaction, such as reviewing changes to a master price list, correcting records that fail automated validation, or approving invoices before they're sent.
This often ends up being done in Excel. But this has several drawbacks:
- It interrupts the automated process, and your data ends up stuck in someone’s inbox, with no visibility over whether it’s being actioned
- It’s not secure – once it leaves the data processing platform it’s no longer under the same access control
- It’s time consuming to work with – Excel isn’t really designed for quickly identifying and fixing problems in large amounts of data
But with CloverDX Data Manager you can build a manual data review process into your automated data workflow – and keep everything under control in the CloverDX platform.
A UI built for data review
Data Manager is designed for making the manual part of a data process as easy, fast and transparent as possible.
- Optimize productivity with a customizable user interface that makes it simple to identify problems and fix them
- Customize to your requirements by defining the lifecycle stages data needs to progress through
- Make data review easier with suggestions, custom error messages, integrated lookups or validations, and more
- Minimize delays with automated processing of records into and out of Data Manager, so they get back into the automated pipeline as soon as they're approved
- Get complete visibility over activity with a progress dashboard
Keep data quality high
Data Manager helps improve data quality throughout the data lifecycle.
- Identify problematic records: Because Data Manager is a part of an automated CloverDX data workflow, powerful data validation can automatically identify records that need human attention
- Faster user review: Contextual information on errors and suggested corrections can be provided to data owners right in the Data Manager interface, for faster resolution of issues
- Fewer errors: Standardized processes and centralized control mean less chance of human error that can damage data quality
Better compliance and control
Sending data as unsecure email attachments can create security and compliance issues.
But with Data Manager, your data stays within the CloverDX platform at all times. And non-technical users can work with it as easily as they would in Excel.
- No data leaves the CloverDX platform, so it all stays under your security and centralized access control
- Because all work on the data happens in the platform, you get a full audit trail of every action
- The same enterprise-level permission management as the rest of the CloverDX platform
Simple to use
Non-technical users can work in a familiar, user-friendly interface, without having to learn a complex technical tool.
Easy workflow management
Users see exactly what's waiting for review, as records get automatically added to Data Manager, avoiding the need to search through emails.
Faster data review
Users can review and amend data faster, with an interface designed for exactly this job, with contextual messages, suggested corrections, and more.
Correcting errors as part of report generation
In a research company, data is being submitted from multiple different sources, in different formats, and needs to be consolidated into a single report. But some inconsistencies in a field need to be manually reviewed and corrected.
The CloverDX process with Data Manager:
- Incoming data is ingested, transformed and consolidated with a CloverDX workflow
- The automated workflow validates the column against a master list
- Any values which don’t match the master list are automatically pulled into a dataset and sent to Data Manager for review
- The product data owner can see – in a simple, user-friendly interface – all the records that need review and correction
- CloverDX validation features can suggest corrections, or the user can edit as they see fit
- Once the data is approved, it’s automatically fed back into the automated pipeline to continue being processed
Integrated lookups for faster validation
A logistics company needs to manually review and correct addresses that fail automated validation, so that packages get delivered properly.
The process previously involved users having to copy and paste addresses to Google maps to validate them - a process that took significant time when dealing with large amounts of data.
With Data Manager, the company can build in a lookup to an external source (Google maps in this case). So users can simply click a button that automatically validates the address data right from the Data Manager interface - saving time and reducing the possibility of errors.
And managers can see their team's progress in one central dashboard, so they know how many records still need to be processed, and who is responsible - removing any potential bottlenecks to a time-sensitive process.