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How OCTOPAI Created a Business Glossary in 5 Minutes Flat for Menora Mivtachim

Company Menora Mivtachim
Industry Insurance
Average Monthly Usage 700 Metadata Queries
Needs Expressed - Simplify a complex data landscape - Efficient data warehouse planning - Improve compliance
Benefits Realized - Fast access to relevant reports - Locate metadata quickly and easily - Avoid mistakes in design of new systems

In Just Minutes, OCTOPAI Set Up an Automated Business Glossary for a Top Israeli Insurance Firm

Established in 1935, Menora Mivtachim Insurance Ltd. is an Israeli insurance company that offers general insurance, life and health insurance group packages. Traded on the Tel Aviv Stock Exchange, it is one of the top five largest insurance companies in Israel.

How does a major insurance company gain control over a complex metadata environment?

Meet Shimon, former Head of Business Intelligence (BI) at Menora Mivtachim. His team was in desperate need of a data catalog, among many other things. They had a large enterprise data warehouse, Oracle data platform, DataStage ETL, and Cognos reporting. Plus, they had a self-service application based on Qlik Sense. Thus, Shimon and the team were dealing with a large, complex metadata landscape, and alas, they felt a lack of control over all of their data assets.

And at the time, most of the BI team’s questions relating to data lineage and impact analysis were completely unanswerable. The team wanted to know such things as:

  • The flow of data items in the reporting tools,
  • How data items were populated,
  • Which DataStage job(s) populated those specific objects, and
  • How to find out potential impact on the BI reporting tool and on Qlik Sense if and when they wanted to make a change in a DataStage job. 


What’s more, they wanted to be able to easily search for different objects. So, they searched for a better solution to manage their data.

Finding a smart metadata management solution

They were looking for a solution that would do many, many things – lineage, impact analysis, and a glossary. Oh, and if those demands weren’t enough, they needed to clearly define a data governance program with the support of a metadata automation system. They were looking to move the needle toward achieving compliance. 


“It was like a miracle to be able to have metadata in one place that was created over 5-6 years through the data warehouse, ETL, and database,” said Shimon.


If a new analyst comes to the department, in seconds he can find out from Octopai the definition of columns of data items that comprise a customer profile.


As a business user, one can define what a concept means, and then link this business concept to various physical objects in the database or Cognos or Qliksense. For example, if someone wants to know, what is “premium” ‒ Octopai does a search for this concept and finds relevant reports in Cognos.

Gaining efficiencies in data analysis and insights using Octopai

Octopai’s automated business glossary enables analysts to be more efficient when investigating a new area. They use the automated business glossary to get faster access to relevant reports and gain a fuller grasp on important concepts. The BI team members and developers navigate quickly and are more proficient in system analysis. They are able to make changes on the fly and understand the impact of these changes on the metadata landscape.

Automated data lineage for data governance, say what? Automation for compliance

Regarding regulatory requirements, solvency and accounting reports, oftentimes there is a requirement to show data lineage and indicate a data item’s source. Octopai helped the company to comply with this requirement, too. 


The CDO wanted business users and data analysts to get the right data objects and the right data service. There were so many reports and business objects in the BI environment that it was not easy to find relevant reports or data objects. Octopai provided a simplified way to locate metadata.


As a result, Octopai became a routine part of the company’s policies and procedures. The initiation of any new project in the data warehouse begins by documenting business requirements in the Octopai business glossary. When adding new items in the data warehouse, the BI user starts by filling out a form in Octopai for this new data item and explains the business definition.


“In five minutes of work on Octopai, Menora Mivtachim set up a business glossary to define the meanings of new items to implement in a data warehouse,” said Shimon.


So why did Menora Mivtachim decide on Octopai in the first place? 


  • Simplify a complex landscape –  The company was looking for a tool that would help find metadata fast in a maze of systems. 
  • Efficient data warehouse planning – It makes planning data warehouse design more efficient in terms of creating new reports, new models, or making changes to existing models because the BI team can run better discovery of existing systems. It’s also just more efficient to avoid mistakes in the design of new systems.
  • Improve compliance – From a regulatory perspective, Menora Mivtachim can better explain where the data comes from, which is critical for regulation compliance.


“Now, by using Octopai for automated data discovery, we don’t miss ANY important information about how things are working.”

Shimon Falicovich, Former Head of Business Intelligence @ Menora Mivtachim