The story behind Enterprise Explorer

last update 17.06.2020

A short look back im time

Software systems become chaotic and unreliable.

Before introducing Enterprise Explorer lets step back a few decades in time and try to see the whole picture of the evolution of the information age.

In the age of the industry revolution the aim was clear. The Computer would solve problems by helping the humans to deliver the right information fast and instantly.

CASE systems seemed to be the right tools. Computer aided software engineering would secure high quality, defect-free and maintainable software.


Decades later we are facing problems nobody could ever imagine at time time. New obstacles like complexity, inflexibility or the urgent need to deliver insights becomes crucial.
 

Within few years data suddenly became "Big data" challenging IT departments to its limits.

Many legacy systems struggle hard to meet the requirements and additionally get stuck by technology limits. Complexity reaches a level where human perception bumps on its limits. 

Unexpected issues and problems

Todays software systems has grown over years and decades becoming "black boxes" of itself. One of the largest swiss financial institution kept partner data in 36 separate databases, each claiming to be the one-and-only golden source.

Another case was from another client a rather smaller financial Institution. Each night about  54'000 jobs are processed with a goal to deliver financial data at 6 AM latest.
The Job Control System was not capable to determine the critical path or to visualise the dependencies among these jobs efficiently.

SLA violations were daily business. Fixing problems took hours!
 

This picture is the top level view containing 54'000 single jobs. a digital haystack.

Nowadays Enterprises are struggling with some of these issues:

  • unexpected complexity of dependencies

  • uncontrolled heterogenous landscapes and interfaces

  • unreliability of production and processes

  • decreasing efficiency and productivity

  • high cost of implementation

  • general unawareness and high effort for reverse engineering

  • incompetence on predictions and impact analysis 

Most of the issues share a common problem: 

Wrong information or no information!

Age of connections drives data value

Surprisingly the solution is easy.

connected data creates knowledge

to answers questions

So in this age of connections it's the aim to make connection between data from different data silos. Only a subset of the original data is stored in a so called data lake based on graph database technology like Neo4j or TigerGraph.

New database technologies provide solutions to solve todays problems.

  • Flexibility through new way of data modelling

  • Optimised for high connectivity