A Business and Technologies journey

In a previous blog I talked about extended enterprise and how Snowflake can provide the data architecture to support it . Today I will try to illustrate it with a more realistic scenario. The scenario comes from my previous experience in manufacturing but can be adapted to any industry : Several parties need to share their data to get the full picture of a product and then estimate the production costs. This could be part of a Design-to-Cost business process  aiming at optimising product design and fabrication in order to  Use as many as possible off-the-shelf components Reduce raw material usage Reduce the product complexity Mutualise components Reduce waste Eliminate non desired features In order to make it easy to understand I will simplify the data processing part of the scenario and keep the most valuable dimension, the sharing. A composite product Let's imagine we are dealing with a company producing a product and working with several third parties and plants (subsidiari...

Initially I wanted to make a post about agility and two speed IT.  Two speed IT is a quite old concept and even declared deprecated by its creator the BCG. Indeed in term of process most of the biggest organisations have now converged to the Agile methodology for both legacy and all brand new digital services and business processes. It closes the gap between how the projects are managed on legacy systems and new ones. But does agile mean agility ? Not so sure  ... what we see is still limited capacity to innovate in a given constrained period of time. What about the Data hub in this context ? The data hub allows to create an abstraction level on top of the legacy systems in order to keep the pace of the digital transformation. Data Hub allows to ingest the data as-is whatever the sources are (including lecacy systems, ERP, etc.), harmonise them to a unified business model and then create wide range of services to serve the new requirements. All that with maximum agility....

With the new year, it's time for a retrospective of the past year and prepare for 2019. First, 2018 was the year of GDPR (The EU G eneral D ata P rotection R egulation). GDPR had been in the agenda of global companies and consulting firms for at least 2 years for being the most important change in data privacy regulation in 20 years. From the MarkLogic perspective GDPR, is a great opportunity to demonstrate the value of an operation DataHub  to store data and metadata together to support robust data governance required by the regulation.Concrete uses cases deal for example with consent management in customer 360 hub or tackling right to be forgotten by managing data lineage from golden record to source dataset records.  A detailed illustration is described is this blog post :  How a Data Hub can help to find back memory to tackle GDPR right to be forgotten . And a more generic illustration is details here :  Metadata in operationa...

As we are starting a new year, let's have a retrospective of some of the main solutions powered by MarkLogic of my 2017 year. We are going to give an overview of: "Big-ECM" Solution - Insurance Product Testing Datahub - Manufacturing and others... Customer 360 - All industries Semantic integration layer for PLM - Manufacturing virtual MarkLogic World - Insurance Big ECM or how to make the most of documents The challenge Insurers have been dealing for decades with hundred of millions, sometimes billions of documents usually stored in multiples siloed ECM systems due to multiple M&A.  Documents are most of the time considered  only from their attached metadata which are limited to basic client references. But documents received from clients are a key dimension of the customer 360. Combined with other customer knowledge it can produce relavant insights to better serve and retain clients : Semantic analysis : Sentiment analysis Entity extra...