The Disruptive Analyst

                              Welcome back to the expository series on data assets and data governance! In my previous article, we learned about v alue-added data assets   and how and why you have to manage them strategically.In this episode, I discuss how these assets can be transformed for competitive enterprise capability development. Before delving into capability development from data assets   management, an important distinction claim attention here and needs to be delineated. That is between info quality and data quality , even though the two are used interchangeably , the former has user quality (known as information consumer) as its focus .The first major strategy shift required here is viewing information as a product to be managed as against limiting to data quality in databases and data-warehouses. Two models are considered industry standard for structuring the quality assurance which is the forerunner to information product management.They aid to assess info quality base

Supply chain needs to be even more intelligent and dynamic Let's imagine a new supply chain revolution,which might be fancy termed “phoenix of new supply chain movement” . How would such a supply chain be structured and what could be its salient features. What should be the principal underlying dimensions that the thought leaders and decision-makers need to focus when they engineer the intelligent supply chain architecture. As the anchor point, of my proposal, I would like to point out that there are 4 pillars of the futuristic supply chain model. They should embody the following- 1. Advanced digital models and sustainable ecosystems   The modern digital supply chain, needs to be primarily characterized by demand-driven pipeline, market-intelligent with a lifecycle value approach, and led by predictive analytics based decision frameworks. It has to be agile and flexible to meet challenges of rapidly changing real time environment. The digital artefacts needs to be characterized by

The marriage between AI and supply chain management I n this article I attempt to provide a brief snapshot of  how the AI-powered systems are driving the supply chain management field in 2020. These may include trends that are impactful breakthrough but still maturing as a sustainable solution  as well as those in the post plateau and has matured into growth phase.  The area of dynamic and intelligent approach to supply chain management has drawn immense attention in later half of last decade.The explosive boom in the AI sector has undeniably cast a spell on the way supply chain and logistics industry has been conducting its processes. In the following list, along with the trend, I also mention a selecta of players in the particular technology/trend, and the way they are making an impact.                                                                So here we go! 1.  Process automation    Robotic process automation (RPA) enables automation of manual or human involved business proces

Business processes through their inter-organizational and intra-organizational cross-functional interactions create large-scale information assets. These can be processes that describe workflows, transformations and other maneuvers, and in numerous ways manipulate the data. Each of them leave an imprint and its of vital strategic importance to delineate or govern the trace and access control of those imprints as a cohesive whole in the journey of the asset in its end-to-end lifecycle.So the raw imprint of asset becomes its refined blueprint with added value. In a way, information systems in modern enterprise solutions has to facilitate a data-value view of asset managemen t . In an organizational setting, data governance refers to" the framework and underlying policies of collecting data and governing the ownership and purpose of its usage " .  According to state-of-art research in data management , data-asset value-chain consists of primarily -  Acquisition, curation, explo

There is immense untapped potential in the data universe  There is data flowing relentlessly resulting from the ever expansive information exchange between its creators and consumers .Organizations are more or less baffled by the spooky fluctuations in businesses processes,workflow and enterprise routines that is created by this massive flux of data in the conduct of businesses.Indeed its not a thing of coincidence that the lifecycle of every business processes can be punctuated as a function of data.However, how to qualify, evaluate and improve the multifarious and heterogeneous data for a comprehensive data management and governance strategy is the essential question. Organizations mostly take a very broad strategic approach to data management.To gain access to specific business information as opposed to unrefined bulk data streaming through API interfaces, there needs to be robust data formatting and integrity assessments.A more prudent way is to take a tactical approach to data int