Exponential Data Architectures

Over the past two decades there has been a lot of focus on building Enterprise Data Models and Data Warehouses aimed at providing the business intelligence and information required for helping organizations make better data based decisions.  Many IT organizations have invested millions of dollars and countless man hours in their quest to map the information required by the businesses they support.   However, even the most successful of these efforts have at best been met with mixed results.  

With the rapid growth in the volume, variety and velocity of data, today’s Exponential Organizations (ExO) require the ability to quickly sift through this data, derive meaningful insights and enable decisions that help them adapt to rapid and constant changes in the business ecosystem.   Today's ExOs need and Exponential Data Architecture (EDA).

An EDA begins with strong understanding of how a business adds value to its customers, employees and stakeholders and it differs from traditional data architectures in 4 key ways:

1. Traditional IT models focus on applications and the assumption that these applications own the data.  These models create and promote data silos that require additional downstream activities to decouple the data from these applications and transform it into models that can be used for reporting, business intelligence and advanced data analytics.  This approach is too slow, expensive and complex for today's ExOs.  In contrast, an EDA starts with the premise that data is the primary currency of an ExO. In the Exponential IT model, applications access, modify and change this data as it flows through the organization.   This shift enables business agility as a capability by quickly translating data to insights and operational actions.  
2. In order to identify the key data flows in an organization, the Exponential IT model first tries to identify a set of data streams that deliver value to the stakeholders of the organization.   These data streams support a set of core experiences that deliver value to stakeholders in the form of information/products/services.  Each of these data streams are highly optimized value chains that reduce friction and waste in the steps it requires from the time data is procured or produced to the point in time that it is transformed and delivered as value to the customer.  In other words, the Exponential IT model uses  an information-centric approach where applications are instruments to enable seamless and frictionless flow of information and value to customers.   
3. Traditional IT models were built during an era of scarcity where computing power, storage, software and resources were expensive and difficult to obtain.   Today we have an abundance of resources brought about by advances in Cloud Computing, GPUs and crowdsourcing.  The EDA will have to reimagine and rebuild the supply chain of data with the objective of improving the flow of data in these data streams, reduce costs and reduce the latency from the time data is produced to the time that it is consumed in an ExO
4. An EDA is developed in an incremental and iterative manner.   It is prioritized and implemented in increments that address specific aspects of the business that deliver the most value to its customers. The definition and implementation of an EDA should not take months or years, but is done in a matter of weeks and evolved to adapt to the changing needs of the marketplace and customers.

The Enterprise Data Architecture is a critical component of the new Exponential Model for IT.    It helps ExOs deliver simple, secure, seamless, context-aware experiences in an always-on, always-connected world.

Stay tuned for more posts on the Exponential IT model.

Read my previous post on Exponential IT