Architect 2 Architect

Fellow Bloggers – My role is to create & deliver digital products and solutions that help deliver value to the customer and increase customer loyalty.  As an architect of these solutions I am constantly striving to effectively leverage Big Data, NLP and data science techniques.   However, when it comes to data science I always struggle with the concepts of machine learning (ML) and artificial intelligence (AI) .   In this blog I embark on a quest to find a way to set apart the concepts of ML & AI and to simplify the decision of when to apply which of these two concepts.       In just the past couple of years, ML/ AI have magically penetrated into all aspects of our service industry - from automating a manual process to driving cars to offering self-help assistance to recommending next best offers to automation of complex decision making.  So the question becomes are these algorithms "simulating" the human or just "mimicking" the human.  Do they b

Expanding on the context of Data Aggregation , variously called data refinery, data factory or data lake, I would like to analyze if the concept of Data Aggregation is just a theoretical construct or if there is a practical side to this. My opinion is that Data Aggregation (regardless of how it is referred to) is just a means to an end; an enabler or precursor for  Data Discovery .   This is truly a facility to bring together various types of disconnected sources of data that were previously leveraged in very “targeted” use cases.  The idea being to discover new connections or to explore new usage patterns.   These explorations might belong to the realm of identifying proactive growth opportunities or in the domain of preemptive loss prevention.  Data scientists are able to employ statistical algorithms and predictive modeling techniques to see if new patterns emerge or else to see they are able to ferret out alternate connections.  One also can imagine the use of clustering an

A lot of talk has been heard lately about the concept of data lake.  Variously known as, data refinery, data factory etc.  I find it interesting that we now hear logical architectural terms that speak to the concepts and to the purpose of the big data technologies such as Hadoop / HDFS and Apache distributed database technologies such as HBase / Cassandra .    This may be indicative of a shift.  What I am not sure of is does this mean that there is a level of maturity that has been achieved by this suite of open source technologies? Or could  this point to the fact that these technologies have practical applications that solve enterprise scale problems? Or does it show that enterprises have realized that they are no longer able to just deal with "structured data" and that a vast majority of information lies in the space of "unstructured content" leaving them no choice but to venture into the realm of big data technologies?  Not really sure!   The fact re

Hi Fellow Architects - I was recently reviewing old publications EDW and some of the related concepts such as those of Corporate Information Facotry, Operational BI etc. and came across an old Inmon article from ’98.  I was curious to find out as to whether or not an ODS is relevant in today's landscape where we have EII, EDW Appliances, cloud based Warehousing solutions. Thanks for tuning in!! surekha -

Hi Fellow Architects - Here are some common questions Architects often have to deal with when trying to build/ define a Platform. Of course, I would like to hear from the experienced among you to seed this discussion. What is the purpose of the Platform? Is it for others to build business capabilities or else is this for serving up some business capability that is your competitive advantage. The former may be called Infrastructure as a Service and the later Information as a Service. This informs your decision on how much of the interface to expose. The first, Infrastructure as a Service, requires you tighten the integration and management interface and the integration pathways via strict SLAs and service contracts that drive both the infrastructure usage patterns, infrastructure management and billing. The later, Information as a Service, drives you to define the service contract that is unique to your business needs with very tight service definition and service usage crit

Hi Fellow Architects, Master Data has almost become a boring topic these days but I still find that many orgnaizations have not yet either harnessed the power of their master data or else have not really turned the corner on the "Master Data Project" which is now becoming an unwieldy expensive never ending project.  I was wondering if any of you have had success stories in this space and have been able to come up with some simple best practices on tocpics related to master data such as the following - a) how do you determine what is valuable information to be mastered and what not to? b) how do you determine if the master data continues to be of relavance to the enterprise? c) how the profile of master data has changed in the world of social networking etc.? Your feedback is always welcome!! -- Best Regards. surekha -