Every year it becomes more apparent that Enterprise Information Systems are shifting from being "Application-centric" to being "Data-centric." The firms that have already made this shift are reducing the complexity and cost of their systems while dropping the cost of integration precipitously.
A data-centric enterprise is one in which most application functionality is implemented on a single, simple, update-able, shared, federate-able data model. "Single" distinguishes this approach from ERP and other monolithic based solutions. "Simple" distinguishes it from Enterprise Data Models. "Update-able" distinguishes it from Data Warehouses and Data Lakes.
While there is huge benefit in adopting this approach, old habits die hard. In particular, the practitioner on this journey will find that there are two major changes from business as usual: modeling and architecting.
Traditional data modeling paradigms do not lead to the single, simple, etc model that is required at the hard of this method. The traditional "application-centric" approach has not provided us with architectures that can take advantage of this approach. This presentation will cover:
- What is "application-centric" and how do so many firms remain stuck at this stage of EIS development
- What are the requirements of the model at the core of a data-centric enterprise
- Why existing web-stack architectures are not sufficient to implement data-centric applications. What needs to be added and how.
We will also cover case studies of several firms who have made this transition and some that are on the journey, to report the kind of business level and strategic benefits they are enjoying. Attendees of this tutorial will:
- Understand what the application-centric approach looks like
- Recognize some of the key forces that keep firms stuck in the application-centric quagmire
- Be able to document the economic advantages of adopting the data-centric approach
- Distinguish the characteristics of a data-centric model from previous attempts at things that sound like this
- Have and understand a schematic of the key ingredients of a data-centric architecture that would support these goals.