You need to explore the business events captured in your Oracle EBS, ERP Cloud, or Netsuite data....but answering questions has always been a challenge.

Introduction

These products use normalised data to reduce the data footprint and improve the data quality, usability and concurrency of data input. However this makes it difficult to run reports across everything because it will kill the ERP (Enterprise Resource Planning) system and bring it to its knees, because asking to join data across lots of tables in a single query is a killer.

 

 

The Cycle Of Pain

 

The Enterprise Data Warehouse (EDW) was supposed to help. But you:

 

• Ask the questions you want answered first.

• Create the data model to support those questions.

• Extract, Transform and Load (ETL) the data - often aggregating and summarising data into star or snowflake schemas losing granularity or flexibility.

• Perform any analytics and build your insights on this data.

• You then have questions about the insights. But you can’t answer them since they need data not modelled in the EDW. This is data friction - the IT department has to incorporate the additional requirements and repeat the cycle above – this takes time (weeks and months).

 

So, new insights lead to new questions, and the cycle repeats. This old way is not agile and hampers peoples ability to move.

 

 

Break The Cycle

 

There is a new way which enables a platform to be built from scratch yielding, for example, complete insight into EBS purchasing, inventory, order management, payables, etc., in a matter of days/weeks rather than months/years for an EDW project.

 

The facets of such a modern data platform are:

 

• Load data first – then ask questions.

• Load all the data from everywhere – potentially yielding a 360 degree view of your business across disparate relational systems (e.g. Oracle EBS, ERP Cloud, Netsuite, Salesforce, SAP). This includes data source enhancement and augmentation.

• Take the question to the data – its increasingly impractical and time consuming to move data to another location for each different question or query to be answered. Because the engine in our ‘new way’ is highly join performant (across very high numbers of tables) all data is held in its original source system form and ETL effort is therefore eliminated.

• Reduce data friction – multiple users can make use of the same data for multiple purposes.

• Support Innovation – explore data, iterate analytics projects, and enable advanced functionality including predictive analytics and machine learning.

 

 

 

Find Out More

 

If you have questions, want a free proof-of-concept, or want to find out more about the ‘new way’ please contact us now.