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How Mimun Yashir Used OCTOPAI to Increase its BI Team’s Productivity by 85%

Company Mimun Yashir
Industry Finance
Average Monthly Usage 60 Metadata Queries
Needs Expressed - Impact Analysis for Business Changes - Finding and Fixing Reporting Errors
Benefits Realized - Huge time and money savings - No manual searching

Meet David, Business Intelligence Manager

David Bachinsky is the Business Intelligence (BI) Manager at Mimun Yashir (in English, known as Direct Financing), a leading consumer credit company in Israel. Mimun Yashir offers the public non-bank credit solutions for financing a vehicle, tuition, vacation, renovation, or any other purpose – under affordable conditions and full transparency.

For David, using Octopai was about leveraging automated metadata management to help deal with inaccuracies in reports, especially when there could be unanticipated future changes to fields from other departments. They had major issues with reporting inaccuracies once they compared data across departments, and this was causing all sorts of confusion and mess across the organization. 

Octopai wasn’t around when Israel implemented a change in zip codes (adding two additional digits), but David talks about this as the main example of why they purchased Octopai. The amount of manual work that they had to do to implement the change – searching and searching to find every place where the zip code field was located, as well as working to understand the impact analysis of making the necessary change – was enough to drive the BI team over the edge. With Octopai, David knew that a change as big as this would have been simple (and quick) to implement.


The Business Intelligence Challenge

You know the story. The business team finds an error on a report and goes straight to the BI team so they can figure out what went wrong. The reporting team doesn’t know where the error and data inconsistencies originated. They ask the ETL team to help reverse engineer (manually, mind you) the reports to try to figure out the source of the error, what manipulations happened along the way from the source of the information to the reports. If they actually managed to find the source and correct the error, it’s party time. But all of this work wasted days of multiple employees’ valuable that could and should be focused elsewhere.

As a consumer credit company, any incorrect business decisions made due to errors in reporting data are extremely costly. The amount of time spent checking and correcting errors manually was bogging the BI team down, and their time was spent focusing on this manual work instead of on the work they were actually hired to do. David understood that their reporting errors could be eliminated with automation. With automation comes reliable data, and reliable data equals better business decisions.

With Octopai, data discovery and data lineage are automated, so understanding what manipulations happened along the way between the ETL system all the way to reporting can be visualized in seconds. Finding the source of an error takes minutes as opposed to days. And more importantly, it takes one person to do all of this instead of the whole team.

The Secret to Saving the Time and Work of Two Full-Time Employees

Mimun Yashir purchased Octopai primarily for its automated data discovery and data lineage capabilities – mainly in order to conduct impact analysis. Its six-person team uses Octopai on average 60 times each month, the equivalent of the work of two additional full-time employees. Now, well into its second year using Octopai, Mimun Yashir claims an 85% increase in overall efficiency of its BI team.

Here’s what Mimum Yashir says about Octopai:

“We use Octopai for so many things, every single day. It saves days of work, saves us from doing all the dirty work, frees up our time to deal with development and not searching all day for everything.”

These days, you can find the BI team at Mimun Yashir focusing on the work they were actually hired to do – developing new projects, new scripts, new reports, etc. and meeting SLA deadlines with ease. They’ve been able to enlarge their capacity and know that the data that they’re using in everything they do is, most importantly, accurate.

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